Recently, I needed to debug a problem that only occurred in RHCOS images that are running in secure execution mode on an IBM Z system. Since I don’t have a OCP installation at hand, I wanted to run such an image directly with QEMU or libvirt. This sounded easy at a first glance, since there are qcow2 images available for RHCOS, but in the end, it was quite tricky to get this working, so I’d like to summarize the steps here, maybe it’s helpful for somebody else, too. Since the “secex” images are encrypted, you cannot play the usual tricks with e.g. guestfs here, you have to go through the ignition process of the image first. Well, maybe there is already the right documentation for this available somewhere and I missed it, but most other documents mainly talk about x86 or normal (unencrypted) images (like the one for Fedora CoreOS on libvirt ), so I think it will be helpful to have this summary here anyway.

Preparation

First, make sure that you have the right tools installed for this task:

sudo dnf install butane wget mkpasswd openssh virt-install qemu-img

Since we are interested in the secure execution image, we have to download the image with “secex” in the name, together with the right GPG key that is required for encrypting the config file later, for example:

wget https://mirror.openshift.com/pub/openshift-v4/s390x/dependencies/rhcos/4.16/4.16.3/rhcos-qemu-secex.s390x.qcow2.gz
wget https://mirror.openshift.com/pub/openshift-v4/s390x/dependencies/rhcos/4.16/4.16.3/rhcos-ignition-secex-key.gpg.pub

Finally, uncompress the image. And since we want to avoid modifying the original image, let’s also create an overlay qcow2 image for it:

gzip -d rhcos-qemu-secex.s390x.qcow2.gz
qemu-img create -f qcow2 -b rhcos-qemu-secex.s390x.qcow2 -F qcow2 rhcos.qcow2

Creation of the configuration file

For being able to log in your guest via ssh later, you need an ssh key, so let’s create one and add it to your local ssh-agent:

ssh-keygen -f rhcos-key
ssh-add rhcos-key

If you also want to log in on the console via password, create a password hash with the mkpasswd tool, too.

Now create a butane configuration file and save it as “config.bu”:

variant: fcos
version: 1.4.0
passwd:
  users:
    - name: core
      ssh_authorized_keys:
        - INSERT_THE_CONTENTS_OF_YOUR_rhcos-key.pub_FILE_HERE
      password_hash: INSERT_THE_HASH_FROM_mkpasswd_HERE
      groups:
        - wheel
storage:
  files:
    - path: /etc/se-hostkeys/ibm-z-hostkey-1
      overwrite: true
      contents:
        local: HKD.crt
systemd:
  units:
    - name: [email protected]
      mask: false

Make sure to replace the “INSERT_…” markers in the file with the contents of your rhcos-key.pub and the hash from mkpasswd, and also make sure to have the host key document (required for encrypting the guest with genprotimg) available as HKD.crt in the current directory.

Next, the butane config file needs to be converted into an ignition file, which then needs to be encrypted with the GPG key of the RHCOS image:

butane -d . config.bu > config.ign
gpg --recipient-file rhcos-ignition-secex-key.gpg.pub --yes \
    --output config.crypted --armor --encrypt config.ign

Ignition of the guest image

The encrypted config file can now be used to start the ignition of the guest. On s390x, the config file is not presented via the “fw_cfg” mechanism to the guest (like it is done on x86), but with a drive that has a special serial number. Thus QEMU should be started like this:

/usr/libexec/qemu-kvm -d guest_errors -accel kvm -m 4G -smp 4 -nographic \
  -object s390-pv-guest,id=pv0 -machine confidential-guest-support=pv0 \
  -drive if=none,id=dr1,file=rhcos.qcow2,auto-read-only=off,cache=unsafe \
  -device virtio-blk,drive=dr1 -netdev user,id=n1,hostfwd=tcp::2222-:22 \
  -device virtio-net-ccw,netdev=n1 \
  -drive if=none,id=drv_cfg,format=raw,file=config.crypted,readonly=on \
  -device virtio-blk,serial=ignition_crypted,iommu_platform=on,drive=drv_cfg

This should start the ignition process during the first boot of the guest. During future boots of the guest, you don’t have to specify the drive with the “config.crypted” file anymore. Once the ignition is done, you can log in to the guest either on the console with the password that you created with mkpasswd, or via ssh:

ssh -p 2222 core@localhost

Now you should be able to use the image. But keep in mind that this is an rpm-ostree based image, so for installing additional packages, you have to use rpm-ostree install instead of dnf install here. And the kernel can be replaced like this, for example:

sudo rpm-ostree override replace \
 kernel-5.14.0-...s390x.rpm \
 kernel-core-5.14.0-...s390x.rpm \
 kernel-modules-5.14.0-...s390x.rpm \
 kernel-modules-core-5.14.0-...s390x.rpm \
 kernel-modules-extra-5.14.0-...s390x.rpm

That’s it! Now you can enjoy your configured secure-execution RHCOS image!

Special thanks to Nikita D. for helping me understanding the ignition process of the secure execution images.

My KVM Forum 2024 talk "IOThread Virtqueue Mapping: Improving virtio-blk SMP scalability in QEMU" is now available on YouTube. The slides are also available here.

IOThread Virtqueue Mapping is a new QEMU feature for configuring multiple IOThreads that will handle a virtio-blk device's virtqueues. This means QEMU can take advantage of the host's Linux multi-queue block layer and assign CPUs to I/O processing. Giving additional resources to virtio-blk emulation allows QEMU to achieve higher IOPS and saturate fast local NVMe drives. This is especially important for applications that submit I/O on many vCPUs simultaneously - a workload that QEMU had trouble keeping up with in the past.

You can read more about IOThread Virtqueue Mapping in this Red Hat blog post.

We’d like to announce the availability of the QEMU 9.1.0 release. This release contains 2800+ commits from 263 authors.

You can grab the tarball from our download page. The full list of changes are available in the changelog.

Highlights include:

• migration: compression offload support via Intel In-Memory Analytics Accelerator (IAA) or User Space Accelerator Development Kit (UADK), along with enhanced support for postcopy failure recovery
• virtio: support for VIRTIO_F_NOTIFICATION_DATA, allowing guest drivers to provide additional data as part of sending device notifications for performance/debug purposes
• guest-agent: support for guest-network-get-route command on linux, guest-ssh-* commands on Windows, and enhanced CLI support for configuring allowed/blocked commands
• block: security fixes for QEMU NBD server and NBD TLS encryption
• ARM: emulation support for FEAT_NMI, FEAT_CSV2_3, FEAT_ETS2, FEAT_Spec_FPACC, FEAT_WFxT, FEAT_Debugv8p8 architecture features
• ARM: nested/two-stage page table support for emulated SMMUv3
• ARM: xilinx_zynq board support for cache controller and multiple CPUs, and B-L475E-IOT01A board support for a DM163 display
• LoongArch: support for directly booting an ELF kernel and for running up to 256 vCPUs via extioi virt extension
• LoongArch: enhanced debug/GDB support
• RISC-V: support for version 1.13 of privileged architecture specification
• RISC-V: support for Zve32x, Zve64x, Zimop, Zcmop, Zama16b, Zabha, Zawrs, and Smcntrpmf extensions
• RISC-V: enhanced debug/GDB support and general fixes
• SPARC: emulation support for FMAF, IMA, VIS3, and VIS4 architecture features
• x86: KVM support for running AMD SEV-SNP guests
• x86: CPU emulation support for Icelake-Server-v7, SapphireRapids-v3, and SierraForest
• and lots more…

Thank you to everybody who contributed to this release, whether that was by writing code, reporting bugs, improving documentation, testing, or providing the project with CI resources. We couldn’t do these without you!

Ever struggled to create configuration files for starting Linux on IBM Z and LinuxONE installations? Fear no more, we got you covered now: A new assistant available online will help you create parameter files!
Writing parameter files can be a challenge, with bugs triggering cycles with lengthy turnaround times. Our new installation assistant generates installer parameter files by walking you through a step-by-step process, where you answer simple questions to generate a parameter file. Comes with contextual help in every stage, so you can follow along what is happening!
Currently supports OSA and PCI networking devices, IPv4/v6, and VLAN installations.

Currently supports RHEL 9 and SLES 15 SP5 or later.
Access the assistant at https://ibm.github.io/liz/

Network boot kickoff.

Step number one for the firmware on any system is sending out a DHCP request, asking the DHCP server for an IP address, the boot server (called "next server" in dhcp terms) and the bootfile.

On success the firmware will contact the boot server, fetch the bootfile and hand over control to the bootfile. Traditional method to serve the bootfile is using tftp (trivial file transfer protocol). Modern systems support http too. I have an article on setting up the dhcp server for virtual machines you might want check out.

What the bootfile is expected to be depends on the system being booted. There are embedded systems -- for example IP phones -- which load the complete system software that way.

When booting UEFI systems the bootfile typically is an EFI binary. That is not the only option though, more on that below.

UEFI network boot with a boot loader.

The traditional way to netboot linux on UEFI systems is using a bootloader. The bootfile location handed out by the DHCP server points to the bootloader and is the first file loaded over the network. Typical choices for the bootloader are grub.efi, snponly.efi (from ipxe project) or syslinux.efi.

Next step is the bootloader fetching the config file. That works the same way the bootloader itself was loaded, using the EFI network driver provided by either the platform firmware (typically the case for onboard NICs) or via PCI option rom (plug-in NICs). The bootloader does not need its own network drivers.

The loaded config file controls how the boot will continue. This can be very simple, three lines asking the bootloader to fetch kernel + initrd from a fixed location, then start the kernel with some command line. This can also be very complex, creating an interactive menu system where the user has dozens of options to choose from (see for example netboot.xyz).

Now the user can -- in case the config file defines menus -- pick what he wants boot.

Final step is the bootloader fetching the kernel and initrd (again using the EFI network driver) and starting the kernel. Voila.

Boot loaders and secure boot.

When using secure boot there is one more intermediate step needed: The first binary needs to be be shim.efi, which in turn will download the actual bootloader. Most distros ship only grub.efi with a secure boot signature, which limits the boot loader choice to that.

Also all components (shim + grub + kernel) must come from the same distribution. shim.efi has the distro secure boot signing certificate embedded, so Fedora shim will only boot grub + kernel with a secure boot signature from Fedora.

Netbooting machines without EFI network driver.

You probably do not have to worry about this. Shipping systems with EFI network driver and UEFI network boot support is standard feature today, snponly.efi should be used for these systems.

When using older hardware network boot support might be missing though. Should that be the case the ipxe project can help because it also features a large collection of firmware network drivers. It ships an all-in-one EFI binary named ipxe.efi which includes the the bootloader and scripting features (which are in snponly.efi too) and additionally all the ipxe hardware drivers.

That way ipxe.efi can boot from the network even if the firmware does not provide a driver. In that case ipxe.efi itself must be loaded from local storage though. You can download the efi binary and ready-to-use ISO/USB images from boot.ipxe.org.

UEFI network boot with an UKI.

A UKI (unified kernel image) is an EFI binary bundle. It contains a linux kernel, an initrd, the command line and a few more optional components not covered here in sections of the EFI binary. Also the systemd efi stub, which handles booting the bundled linux kernel with the bundled initrd.

One advantage is that the secure boot signature of an UKI image will cover all components and not only the kernel itself, which is a big step forward for linux boot security.

Another advantage is that a UKI is self-contained. It does not need a bootloader which knows how to boot linux kernels and handle initrd loading. It is simply an EFI binary which you can start any way you want, for example from the EFI shell prompt.

The later makes UKIs interesting for network booting, because they can be used as bootfile too. The DHCP server hands out the UKI location, the UEFI firmware fetches the UKI and starts it. Done.

Combining the bootloader and UKI approaches is possible too. UEFI bootloaders can load not only linux kernels. EFI binaries (including UKIs) can be loaded too, in case of grub.efi with the chainloader command. So if you want interactive menus to choose an UKI to boot you can do that.

UEFI network boot with an ISO image.

Modern UEFI implementations can netboot ISO images too. Unfortunately there are a few restrictions though:

1. It is a relatively new feature. It exists for a few years already in edk2, but with the glacial speeds firmware feature updates are delivered (if at all) this means there is hardware in the wild which does not support this.
2. It is only supported for HTTP boot. Which makes sense given that ISO images can be bulky and the http protocol typically is much faster than the tftp protocol used by PXE boot. Nevertheless you might need additional setup steps because of this.

When the UEFI firmware gets an ISO image as bootfile from the DHCP server it will load the image into a ramdisk, register the ramdisk as block device and try to boot from it.

From that point on booting works the same way booting from a local cdrom device works. The firmware will look for the boot loader on the ramdisk and load it. The bootloader will find the other components needed on the ramdisk, i.e. kernel and initrd in case of linux. All without any network access.

The UEFI firmware will also create ACPI tables for a pseudo nvdimm device. That way the booted linux kernel will find the ramdisk too. You can use the standard Fedora / CentOS / RHEL netinst ISO image, linux will find the images/install.img on the ramdisk and boot up all the way to anaconda. With enough RAM you can even load the DVD with all packages, then do the complete system install from ramdisk.

The big advantage of this approach is that the netboot workflow becomes very simliar to other installation workflows. It's not the odd kid on the block any more where loading kernel and initrd works in a completely different way. The very same ISO image can be:

1. Burned to a physical cdrom and used to boot a physical machine.
2. In many cases the ISO images are hypbrid, so they can be flashed to a USB stick too for booting a physical machine.
3. The ISO image can be attached as virtual device to a virtual machine.
4. On server grade managed hardware the ISO image can be attached as virtual media using redfish and the BMC.
5. And finally: The ISO image can be loaded into a ramdisk via UEFI http boot.

Bonus: secure boot support suddenly isn't a headace any more.

The kernel command line.

There is one problem with the fancy new world though. We have lots of places in the linux world which depend on the linux kernel command line for system configuration. For example anaconda expects getting the URL of the install repository and the kickstart file that way.

When using a boot loader that is simple. The kernel command line simply goes into the boot loader config file.

With ISO images it is more complicated, changing the grub config file on a ISO image is a cumbersome process. Also ISO images are not exactly small, so install images with customized grub.cfg need quite some storage space.

UKIs can pass through command line arguments to the linux kernel, but that is only allowed in case secure boot is disabled. When using UKIs with secure boot the best option is to use the UKIs built and signed on distro build infrastructure. Which implies using the kernel command line for customization is not going to work with secure boot enabled.

So, all of the above (and UKIs in general) will work better if we can replace the kernel command line as universal configuration vehicle with something else. Which most likely will not be a single thing but a number of different approaches depending on the use case. Some steps into that direction did happen already. Systemd can autodetect partitions (so booting from disk without root=... on the kernel command line works). And systemd credentials can be used to configure some aspects of a linux system. There is still a loooong way to go though.

A new video illustrating the steps to perform on a KVM host and in a virtual server configuration to make AP queues of cryptographic adapters available to a KVM guest can be found here.

QEMU 9.0 and libvirt 10.0 introduced an important improvement for disk (virtio-blk) scalability. Until now, a virtio-blk device could use one iothread in the host (or the QEMU main thread).
For a while it was already possible to specify multiple virtio queues for a single virtio-block device, and the Linux guest driver was able to exploit thse concurrently:

<driver name='qemu' queues='3'>

All queues have been handled by one iothread. This introduced a limit for the maximum amount of IO requests per seconds per disk no matter how many queues have been defined.

Now it is possible to assign a queue to a given host iothread allowing for higher throughput per device:

<driver name='qemu' queues='3'>
  <iothreads>
    <iothread id='2'>
      <queue id='1'/>
    </iothread>
    <iothread id='3'>
      <queue id='0'/>
      <queue id='2'/>
    </iothread>
  </iothreads>
</driver>

Example with 3 queues to illustrate the possibility to have 2 queues on one iothread and one queue on another. In real life 2 or 4 queues make more sense.

Initial tests showed improved performance and reduced CPU cycles when going from 1 to 2 queues. More performance analysis needs to happen but this looks like a very promising improvement and going from 1 to 2 is almost a no-brainer. Adding more queues continues to improve the performance, but also increases the overall CPU consumption so this needs additional considerations.

Sharing iothreads across multiple disks continues to be possible. 

This feature is also being backported into some distributions like RHEL 9.4 or will be available via regular QEMU/libvirt upgrades.

Canonical released a new version of their Ubuntu server offering Ubuntu Server 24.04!

 Highlights include

• HSM support for Secure Execution
• Further Crypto enhancements and extensions

See the announcement on the mailing list here, and the blog entry at Canonical with all Z-specific highlights here.

The KVM Forum 2024 conference will take place in Brno, Czech Republic on September 22-23, 2024. KVM Forum brings together the Linux virtualization community, especially around the KVM stack, including QEMU and other virtual machine monitors.

The Call for Presentations is open until June 8, 2024. You are invited to submit presentation proposals via the KVM Forum CfP page. All presentation slots will be 25 minutes + 5 minutes for questions.

Suggested topics include:

• Scalability and Optimization
• Hardening and security
• Confidential computing
• Testing
• KVM and the Linux Kernel
  • New Features and Architecture Ports
  • Device Passthrough: VFIO, mdev, vDPA
  • Network Virtualization
  • Virtio and vhost
• Virtual Machine Monitors and Management
  • VMM Implementation: APIs, Live Migration, Performance Tuning, etc.
  • Multi-process VMMs: vhost-user, vfio-user, QEMU Storage Daemon, SPDK
  • QEMU without KVM: Hypervisor.framework, Windows Hypervisor Platform, etc.
  • Managing KVM: Libvirt, KubeVirt, Kata Containers
• Emulation
  • New Devices, Boards and Architectures
  • CPU Emulation and Binary Translation

IBM Secure Execution for Linux -- the Linux Kernel Virtual Machine (KVM) based Confidential Computing technology for IBM LinuxONE and Linux on IBM Z -- now allows Secure Execution guests leverage secure passthrough access to up to 12 Crypto Express 8S adapter domains in accelerator or EP11 co-processor mode.

Customers who require the highest level of protection (FIPS 140-2 level 4 certified) for their cryptographic keys and thus for their sensitive data can now have their workloads deployed as Secure Execution KVM guests with access to Hardware Security Modules (HSMs) if the provider uses IBM z16 or LinuxONE 4 servers with Crypto Express 8S adapters. This combination provides business value for solutions around key and certificate management, multi-party computation and digital assets. But more use cases arise as confidential computing becomes more common and the need to leverage such highly certified HSM to protect AI models or provide data sovereignty across organizational and infrastructure boundaries grows.

To exploit this new function, IBM z16 or LinuxONE 4 severs with firmware bundles S30 and S31b are needed. To use a Crypto Express 8S adapter in EP11 mode the minimal EP11 firmware version loaded must be version 5.8.30.

IBM is working with Linux distribution partners to include the required Linux support for this function for both the KVM Hypervisor and the Secure Execution guests in future distribution releases. Linux support for this function is already available today with Ubuntu 24.04 (Noble Numbat).

This new capability showcases IBM’s commitment and previously stated direction to foster the use of confidential computing and expand the security value proposition of existing security and crypto solutions as the business needs of our customers and technical possibilities evolve.

For detailed information on how to use Crypto Express support see the Introducing IBM Secure Execution for Linux publication.

• Reinhard Bündgen – [email protected]
• Nicolas Mäding – [email protected]

We’d like to announce the availability of the QEMU 9.0.0 release. This release contains 2700+ commits from 220 authors.

You can grab the tarball from our download page. The full list of changes are available in the changelog.

Highlights include:

• block: virtio-blk now supports multiqueue where different queues of a single disk can be processed by different I/O threads
• gdbstub: various improvements such as catching syscalls in user-mode, support for fork-follow modes, and support for siginfo:read
• memory: preallocation of memory backends can now be handled concurrently using multiple threads in some cases
• migration: support for “mapped-ram” capability allowing for more efficient VM snapshots, improved support for zero-page detection, and checkpoint-restart support for VFIO
• ARM: architectural feature support for ECV (Enhanced Counter Virtualization), NV (Nested Virtualization), and NV2 (Enhanced Nested Virtualization)
• ARM: board support for B-L475E-IOT01A IoT node, mp3-an536 (MPS3 dev board + AN536 firmware), and raspi4b (Raspberry Pi 4 Model B)
• ARM: additional IO/disk/USB/SPI/ethernet controller and timer support for Freescale i.MX6, Allwinner R40, Banana Pi, npcm7xxx, and virt boards
• HPPA: numerous bug fixes and SeaBIOS-hppa firmware updated to version 16
• LoongArch: KVM acceleration support, including LSX/LASX vector extensions
• RISC-V: ISA/extension support for Zacas, amocas, RVA22 profiles, Zaamo, Zalrsc, Ztso, and more
• RISC-V: SMBIOS support for RISC-V virt machine, ACPI support for SRAT, SLIT, AIA, PLIC and updated RHCT table support, and numerous fixes
• s390x: Emulation support for CVDG, CVB, CVBY and CVBG instructions, and fixes for LAE (Load Address Extended) emulation
• and lots more…

Thank you to everybody who contributed to this release, whether that was by writing code, reporting bugs, improving documentation, testing, or providing the project with CI resources. We couldn’t do these without you!

During my work on SBSA Reference Platform I have spent lot of time in firmware’s code. Which mostly meant Tianocore EDK2 as Trusted Firmware is quite small.

Writing all those ACPI tables by hand takes time. So I checked ConfigurationManager component which can do it for me.

Introduction

In 2018 Sami Mujawar from Arm contributed Dynamic Tables Framework to Tianocore EDK2 project. The goal was to have code which generates all ACPI tables from all those data structs describing hardware which EDK2 already has.

In 2023 I was writing code for IORT and GTDT tables to generate them from C. And started wondering about use of ConfigurationManager.

Mailed edk2-devel ML for pointers, documentation, hints. Got nothing in return, idea went to the shelf.

SBSA-Ref and multiple PCI Express buses

Last week I got SBSA-Ref system booting in NUMA configuration with three separate PCI Express buses. And started working on getting EDK2 firmware to recognize them as such.

Took me a day and pci command listed cards properly:

Shell> pci
   Seg  Bus  Dev  Func
   ---  ---  ---  ----
    00   00   00    00 ==> Bridge Device - Host/PCI bridge
             Vendor 1B36 Device 0008 Prog Interface 0
    00   00   01    00 ==> Network Controller - Ethernet controller
             Vendor 8086 Device 10D3 Prog Interface 0
    00   00   02    00 ==> Bridge Device - PCI/PCI bridge
             Vendor 1B36 Device 000C Prog Interface 0
    00   00   03    00 ==> Bridge Device - Host/PCI bridge
             Vendor 1B36 Device 000B Prog Interface 0
    00   00   04    00 ==> Bridge Device - Host/PCI bridge
             Vendor 1B36 Device 000B Prog Interface 0
    00   01   00    00 ==> Mass Storage Controller - Non-volatile memory subsystem
             Vendor 1B36 Device 0010 Prog Interface 2
    00   40   00    00 ==> Bridge Device - PCI/PCI bridge
             Vendor 1B36 Device 000C Prog Interface 0
    00   40   01    00 ==> Bridge Device - PCI/PCI bridge
             Vendor 1B36 Device 000C Prog Interface 0
    00   41   00    00 ==> Base System Peripherals - SD Host controller
             Vendor 1B36 Device 0007 Prog Interface 1
    00   42   00    00 ==> Display Controller - Other display controller
             Vendor 1234 Device 1111 Prog Interface 0
    00   80   00    00 ==> Bridge Device - PCI/PCI bridge
             Vendor 1B36 Device 000E Prog Interface 0
    00   80   01    00 ==> Bridge Device - PCI/PCI bridge
             Vendor 1B36 Device 000C Prog Interface 0
    00   81   09    00 ==> Multimedia Device - Audio device
             Vendor 1274 Device 5000 Prog Interface 0
    00   81   10    00 ==> Network Controller - Ethernet controller
             Vendor 8086 Device 100E Prog Interface 0
    00   82   00    00 ==> Mass Storage Controller - Serial ATA controller
             Vendor 8086 Device 2922 Prog Interface 1

Three buses are: 0x00, 0x40 and 0x80. But then I had to tell Operating System about those. Which meant playing with ACPI tables code in C.

So idea came “what about trying ConfigurationManager?”.

Another try

Mailed edk2-devel ML again for pointers, documentation hints. And then looked at code written for N1SDP and started playing with ConfigurationManager…

ConfigurationManager.c has EDKII_PLATFORM_REPOSITORY_INFO struct with hundreds of lines of data (as another structs). From listing which ACPI tables I want to have (FADT, GTDT, APIC, SPCR, DBG2, IORT, MCFG, SRAT, DSDT, PPTT etc.) to listing all hardware details like GIC, PCIe, Timers, CPU and Memory information.

Then code for querying this struct. I thought that CM/DT (ConfigurationManager/DynamicTables) framework will have those already in EDK2 code but no — each platform has own set of functions. Another hundreds of lines to maintain.

Took some time to get it built, then started filling proper data and compared with ACPI tables I had previously. There were differences to sort out. But digging more and more into code I saw that I go deeper and deeper into rabbit hole…

Dynamic systems do not fit CM?

For platforms with dynamic hardware configuration (like SBSA-Ref) I needed to write code which would populate that struct with data on runtime. Check amount of cpu cores and write cpu information (with topology, cache etc), create all GIC structures and mappings. Then same for PCIe buses. Etc. Etc. etc…

STATIC
EFI_STATUS
EFIAPI
InitializePlatformRepository (
  IN  EDKII_PLATFORM_REPOSITORY_INFO  * CONST PlatRepoInfo
  )
{
  GicInfo                 GicInfo;
  CM_ARM_GIC_REDIST_INFO *GicRedistInfo;
  CM_ARM_GIC_ITS_INFO    *GicItsInfo;
  CM_ARM_SMMUV3_NODE     *SmmuV3Info;

  GetGicDetails(&GicInfo);
  PlatRepoInfo->GicDInfo.PhysicalBaseAddress = GicInfo.DistributorBase;

  GicRedistInfo = &PlatRepoInfo->GicRedistInfo[0];

  GicRedistInfo->DiscoveryRangeBaseAddress = GicInfo.RedistributorsBase;

  GicItsInfo = &PlatRepoInfo->GicItsInfo[0];
  GicItsInfo->PhysicalBaseAddress = GicInfo.ItsBase;

  SmmuV3Info = &PlatRepoInfo->SmmuV3Info[0];
  SmmuV3Info->BaseAddress = PcdGet64 (PcdSmmuBase);

  return EFI_SUCCESS;
}

Which in my case can mean even more code written to populate CM struct of structs than it would take to generate ACPI tables by hand.

Summary

ConfigurationManager and DynamicTables frameworks look tempting. There may be systems where it can be used with success. I know that I do not want to touch it again. All those structs of structs may look good for someone familiar with LISP or JSON but not for me.

During last weeks we worked on getting rid of DeviceTree from EDK2 on SBSA Reference Platform. And finally we managed!

All code is merged into upstream EDK2 repository.

What?

Someone may wonder where DeviceTree was in SBSA Reference Platform. Wasn’t it UEFI and ACPI platform?

Yes, from Operating System point of view it is UEFI and ACPI. But if you look deeper you will see DeviceTree hidden inside our chain of software components:

/dts-v1/;

/ {
        machine-version-minor = <0x03>;
        machine-version-major = <0x00>;
        #size-cells = <0x02>;
        #address-cells = <0x02>;
        compatible = "linux,sbsa-ref";

        chosen {
        };

        memory@10000000000 {
                reg = <0x100 0x00 0x00 0x40000000>;
                device_type = "memory";
        };

        intc {
                reg = <0x00 0x40060000 0x00 0x10000
                       0x00 0x40080000 0x00 0x4000000>;

                its {
                        reg = <0x00 0x44081000 0x00 0x20000>;
                };
        };

        cpus {
                #size-cells = <0x00>;
                #address-cells = <0x02>;

                cpu@0 {
                        reg = <0x00 0x00>;
                };

                cpu@1 {
                        reg = <0x00 0x01>;
                };

                cpu@2 {
                        reg = <0x00 0x02>;
                };

                cpu@3 {
                        reg = <0x00 0x03>;
                };
        };
};

It is very minimal one, providing us with only those information we need. It does not even pass any compliance checks. For example for Linux, GIC node (/intc/ one) should have gazillion of fields, but we only need addresses.

Trusted Firmware reads it, parses and provides information from it via Secure Monitor Calls (SMC) to upper firmware level (EDK2 in our case). DeviceTree is provided too but we do not read it any more.

Why?

Our goal is to treat software components a bit different then people may expect. QEMU is “virtual hardware” layer, TF-A provides interface to “embedded controller” (EC) layer and EDK2 is firmware layer on top.

On physical hardware firmware assumes some parts and asks EC for the rest of system information. QEMU does not give us that, while giving us a way to alter system configuration more than it would be possible on most of hardware platforms using a bunch of cli arguments.

EDK2 asks for CPU, GIC and Memory. When there is no info about processors or memory, it informs the user and shutdowns the system (such situation does not have a chance of happening but it works as an example).

Bonus stuff: NUMA

Bonus part of this work was adding firmware support for NUMA configuration. When QEMU is run with NUMA arguments then operating system gets whole memory and proper configuration information.

QEMU arguments used:

-smp 4,sockets=4,maxcpus=4
-m 4G,slots=2,maxmem=5G
-object memory-backend-ram,size=1G,id=m0
-object memory-backend-ram,size=3G,id=m1
-numa node,nodeid=0,cpus=0-1,memdev=m0
-numa node,nodeid=1,cpus=2,memdev=m1
-numa node,nodeid=2,cpus=3

How Operating System sees NUMA information:

root@sbsa-ref:~# numactl --hardware
available: 3 nodes (0-2)
node 0 cpus: 0 1
node 0 size: 975 MB
node 0 free: 840 MB
node 1 cpus: 2
node 1 size: 2950 MB
node 1 free: 2909 MB
node 2 cpus: 3
node 2 size: 0 MB
node 2 free: 0 MB
node distances:
node   0   1   2
  0:  10  20  20
  1:  20  10  20
  2:  20  20  10

What next?

There is CPU topology information in review queue. All those sockets, clusters, cores and threads. QEMU will pass it in DeviceTree, TF-A will give it via SMC and then EDK2 will put it in one of ACPI tables (PPTT == Processor Properties Topology Table).

If someone decide to write own firmware for SBSA Reference Platform (like port of U-Boot) then both DeviceTree and set of SMC calls will wait for them, ready to be used to gather hardware information.

Programming languages currently offer few defences against supply chain attacks where a malicious third-party library compromises a program. As I write this, the open source community is trying to figure out the details of the xz-utils backdoor, but there is a long history of supply chain attacks. High profile incidents have made plain the danger of shipping software built from large numbers dependencies, many of them unaudited and under little scrutiny for malicious code. In this post I will share ideas on future supply chain safe programming languages.

Supply Chain Safe Programming Languages?

I'm using the term Supply Chain Safe Programming Languages for languages that defend against supply chain attacks and allow library dependencies to be introduced with strong guarantees about what the dependencies can and cannot do. This type of programming language is not yet widely available as of March 2024, to the best of my knowledge.

Supply chain safety is often associated with software packaging and distribution techniques for verifying that software was built from known good inputs. Although adding supply chain safety tools on top of existing programming languages is a pragmatic solution, I think future progress requires addressing supply chain safety directly in the programming language.

Why today's languages are not supply chain safe

Many existing languages have a module system that gives the programmer control over the visibility of variables and functions. By hiding variable and functions from other modules, one might hope to achieve isolation so that a component like a decompression library could not read a sensitive variable from the program. Unfortunately this level of isolation between components is not really available in popular programming languages today even in languages with public/private visibility features. Visibility is more of a software engineering tool for keeping programs decoupled than an isolation mechanism that actually protects components of a program from each other. There are many ways to bypass visibility.

The fundamental problem is that existing programming languages do not even acknowledge that programs often consist of untrusted components. Compilers and interpreters currently treat the entire input source code as having more or less the same level of trust. Here are some of the ways in which today's programming languages fall short:

• Unsafe programming languages like C, C++, and even Rust allow the programmer to bypass the type system to do pretty much anything.
• Dynamic languages like Python and JavaScript have introspection and monkey patching abilities that allow the programmer to hook other parts of the program and escape attempts at isolation.
• Build systems and metaprogramming facilities like macros allow untrusted components to generate code that executes in the context of another component.
• Standard libraries provide access to spawning new programs, remapping virtual memory, loading shared libraries written in unsafe languages, hijacking function calls through the linker, raw access to the program's memory space with /proc/self/mem, and so on. All of these can bypass language-level mechanisms for isolating components in a program.

Whatever your current language, it's unlikely that the language itself allows you to isolate components of a program. The best approach we have today for run-time isolation is through sandboxing. Examples of sandboxing approaches include seccomp(2), v8 Isolates for JavaScript, invoking untrusted code in a WebAssembly runtime, or the descendents of chroot(2).

Sandboxes are not supported directly by the programming language and have a number of drawbacks and limitations. Integrating sandboxing into programs is tedious so they are primarily used in the most critical attack surfaces like web browsers or hypervisors. There is usually a performance overhead associated with interacting with the sandbox because data needs to be marshalled or copied. Sandboxing is an opt-in mechanism that doesn't raise the bar of software in general. I believe that supply chain safe programming languages could offer similar isolation but as the default for most software.

What a Supply Chain Safe Programming Language looks like

The goal of a supply chain safe programming language is to isolate components of a program by default. Rather than leaving supply chain safety outside the scope of the language, the language should allow components to be integrated with strong guarantees about what effects they can have on each other. There may be practical reasons to offer an escape hatch to unsafe behavior, but the default needs to be safe.

At what level of granularity should isolation operate? I think modules are too coarse grained because they are often collections of functions that perform very different types of computation requiring different levels of access to resources. The level of granularity should at least go down to the function level within a component, although even achieving module-level granularity would be a major improvement over today's standards.

An example is that a hash table lookup function should be unable to connect to the internet. That way the function can be used without fear of it becoming a liability if it contains bugs or its source code is manipulated by an attacker.

A well-known problem in programming language security is that the majority of languages expose ambient capabilities to all components in a program. Ambient capabilities provide access to resources that are not explicitly passed in to the component. Think of a file descriptor in a POSIX process that is available to any function in the program, including a string compare function that has no business manipulating file descriptors.

Capability-based security approaches are a solution to the ambient capabilities problem in languages today. Although mainstream programming languages do not offer capabilities as part of the language, there have been special-purpose and research languages that demonstrated that this approach works. In a type safe programming language with capability-based security it becomes possible to give components access to only those resources that they require. Usually type safety is the mechanism that prevents capabilities from being created out of thin air, although other approaches may be possible for dynamic languages. The type system will not allow a component to create itself a new capability that the component does not already possess.

Capability-based security addresses safety at runtime, but it does not address safety at compile time. If we want to compose programs from untrusted components then it is not possible to rely on today's build scripts, code generators, or macro systems. The problem is that they can be abused by a component to execute code in the context of another component.

Compile-time supply chain safety means isolating components so their code stays within their component. For example, a "leftpad" macro that pads a string literal with leading spaces would be unsafe if it can generate code that is compiled as part of the main program using the macro. Similarly, a build script for the leftpad module must not be able to affect or escape the build environment.

Macros, build scripts, code generators, and so on are powerful tools that programmers find valuable. The challenge for supply chain safe programming languages is to harness that power so that it remains convenient to use without endangering safety. One example solution is running build scripts in an isolated environment that cannot affect other components in the program. This way a component can take advantage of custom build-time behavior without endangering the program. However, it is unclear to me how far inter-component facilities like macros can be made safe, if at all.

Conclusion

I don't have the answers or even a prototype, but I think supply chain safe programming languages are an inevitability. Modern programs are composed of many third-party components yet we do not have effective techniques for confining components. Languages treat the entire program as trusted rather than as separate untrusted components that must be isolated.

Hopefully we will begin to see new mainstream programming languages emerge that are supply chain safe, not just memory safe!

Recently people asked me how to run SBSA Reference Platform for their own testing and development. Which shows that I should write some documentation.

But first let me blog about it…

Requirements

To run SBSA Reference Platform emulation you need:

• QEMU (8.2+ recommended)
• EDK2 firmware files

That’s all. Sure, some hardware resources would be handy but everyone has some kind of computer available, right?

QEMU

Nothing special is required as long as you have qemu-system-aarch64 binary available.

EDK2

We provide EDK2 binaries on CodeLinaro server. Go to “latest/edk2” directory, fetch both “SBSA_FLASH*” files, unpack them and you are ready to go. You may compare checksums (before unpacking) with values present in “latest/README.txt” file.

Those binaries are built from release versions of Trusted Firmware (TF-A) and Tianocore EDK2 plus latest “edk2-platforms” code (as this repo is not using tags).

Building EDK2

If you decide to build EDK2 on your own then we provide TF-A binaries in “edk2-non-osi” repository. I update those when it is needed.

Instructions to build EDK2 are provided in Qemu/SbsaQemu directory of “edk2-platforms” repository.

Running SBSA Reference Platform emulation

Note that this machine is fully emulated. Even on AArch64 systems where virtualization is available.

Let go through example QEMU command line:

qemu-system-aarch64
-machine sbsa-ref
-drive file=firmware/SBSA_FLASH0.fd,format=raw,if=pflash
-drive file=firmware/SBSA_FLASH1.fd,format=raw,if=pflash
-serial stdio
-device usb-kbd
-device usb-tablet
-cdrom disks/alpine-standard-3.19.1-aarch64.iso

At first we select “sbsa-ref” machine (defaults to four Neoverse-N1 cpu cores and 1GB of ram). Then we point to firmware files (order of them is important).

Serial console is useful for diagnostic output, just remember to not press Ctrl-C there unless you want to take whole emulation down.

USB devices are to have working keyboard and pointing device. USB tablet is more useful that USB mouse (-device usb-mouse adds it). If you want to run *BSD operating systems then I recommend to add USB mouse.

And the last entry adds Alpine 3.19.1 ISO image.

System boots to text console on graphical output. For some reason boot console is on serial port.

Adding hard disk

If you want to add hard disk then adding “-hdb disk.img” is enough (“hdb” as cdrom took 1st slot on the AHCI controller).

Handy thing is “virtual FAT drive” which allows to create guest’s drive from directory on the host:

-drive if=ide,file=fat:ro:DIRECTORY_ON_HOST,format=raw

This is useful for running EFI binaries as this drive is visible in UEFI environment. It is not present when operating system is booted.

Adding NVME drive

NVME is composed from two things:

• PCIe device
• storage drive

So let add it in a nice way, using PCIe root-port:

-device pcie-root-port,id=root_port_for_nvme1,chassis=2,slot=0
  -device nvme,serial=deadbeef,bus=root_port_for_nvme1,drive=nvme
     -drive file=disks/nvme.img,format=raw,id=nvme,if=none

Using NUMA configuration

QEMU can emulate Non-Uniform Memory Access (NUMA) setup. This usually means multisocket systems with memory available per cpu socket.

Example config:

-smp 4,sockets=4,maxcpus=4
-m 4G,slots=2,maxmem=5G
-object memory-backend-ram,size=1G,id=m0
-object memory-backend-ram,size=3G,id=m1
-numa node,nodeid=0,cpus=0-1,memdev=m0
-numa node,nodeid=1,cpus=2,memdev=m1
-numa node,nodeid=2,cpus=3

This adds four cpu sockets and 4GB of memory. First node has 2 cpu cores and 1GB ram, second node has 1 cpu and 3GB of ram and last node has 1 cpu without local memory.

Note that support for such setup in work in progress now (March 2024). We merged required code into TF-A and have set of patches for EDK2 in review. Without them you will see resources only from the first NUMA node.

Complex PCI Express setup

Our platform has GIC ITS support so we can try some complex PCI Express structures.

This example uses PCIe switch to add more PCIe slots and then (to complicate things) puts PCIe-to-PCI bridge into one of them to make use of old Intel e1000 network card:

-device pcie-root-port,id=root_port_for_switch1,chassis=0,slot=0
  -device x3130-upstream,id=up_port1,bus=root_port_for_switch1
    -device xio3130-downstream,id=down_port1,bus=up_port1,chassis=1,slot=0
      -device ac97,bus=down_port1
    -device xio3130-downstream,id=down_port2,bus=up_port1,chassis=1,slot=1
   -device pcie-pci-bridge,id=pci1,bus=down_port2
     -device e1000,bus=pci1,addr=2

Some helper scripts

During last year I wrote some helper scripts for working with SBSA Reference Platform testing. They are stored in sbsa-ref-status repository on GitHub.

May lack up-to-date documentation but can show my way of using the platform.

Summary

SBSA Reference Platform can be used for testing several things. From operating systems to (S)BSA compliance of the platform. Or to check how some things are emulated in QEMU. Or playing with PCIe setups (NUMA systems can have separate PCI Express buses but we do not handle it yet in firmware).

Have fun!

The certificates of the host key signing keys that are needed to verify host key documents will expire on April 24, 2024 for IBM z15 and LinuxONE III and on March 29, 2024 for IBM z16 and LinuxONE 4. Due to a requirement from the Certificate Authority (DigiCert), the renewed certificates are equipped with a new Locality value (“Armonk” instead of “Poughkeepsie”). These renewed certificates cause the current versions of the genprotimg, pvattest, and pvsecret tools to fail the verification of host key documents.

The IBM Z team is preparing updates of the genprotimg, pvattest, and pvsecret tools to accept the new certificates and is working with Linux distribution partners to release the updated tools.

To build new Secure Execution images, attestation requests, or add-secret requests before the updated tools are available in Linux distributions, follow these steps:

Step 1:

Obtain the host key document, the host key signing key certificate, the intermediate certificate from the Certificate Authority, and the list of revoked host keys (CRL):

• For IBM z15 and LinuxONE III, see https://www.ibm.com/support/resourcelink/api/content/public/secure-execution-gen1.html

• For IBM z16 and LinuxONE 4, see https://www.ibm.com/support/resourcelink/api/content/public/secure-execution-gen2.html
  
  

Step 2: 

Step 3:

Verify each host key document using the check_hostkeydoc script. For example, issue

# ./check_hostkeydoc HKD1234.crt ibm-z-host-key-signing.crt \ -c DigiCertCA.crt -r ibm-z-host-key.crl

This example verifies the host key document HKD1234.crt using the host key signing key certificate ibm-z-host-key-signing.crt, and the intermediate certificate of the Certificate Authority DigiCertCA.crt,as well as the list of revoked host keys ibm-z-host-key.crl.

After the host key documents are verified using the check_hostkeydoc script, you can safely call genprotimg, pvattest, or pvsecret with the –-no-verify option.

For a description about how to manually verify host key documents, see https://www.ibm.com/docs/en/linux-on-z?topic=execution-verify-host-key-document.

KubeVirt makes it possible to run virtual machines on Kubernetes alongside container workloads. Virtual machines are configured using VirtualMachineInstance YAML. But under the hood of KubeVirt lies the same libvirt tooling that is commonly used to run KVM virtual machines on Linux. Accessing libvirt can be convenient for development and troubleshooting.

Note that bypassing KubeVirt must be done carefully. Doing this in production may interfere with running VMs. If a feature is missing from KubeVirt, then please request it.

The following diagram shows how the user's VirtualMachineInstance is turned into a libvirt domain:

Accessing virsh

Libvirt's virsh command-line tool is available inside the virt-launcher Pod that runs a virtual machine. First determine vm1's virt-launcher Pod name by filtering on its label (thanks to Alice Frosi for this trick!):

$ kubectl get pod -l vm.kubevirt.io/name=vm1
NAME                      READY   STATUS    RESTARTS   AGE
virt-launcher-vm1-5gxvg   2/2     Running   0          8m13s

Find the name of the libvirt domain (this is guessable but it doesn't hurt to check):

$ kubectl exec virt-launcher-vm1-5gxvg -- virsh list
 Id   Name          State
-----------------------------
 1    default_vm1   running

Arbitrary virsh commands can be invoked. Here is an example of dumping the libvirt domain XML:

$ kubectl exec virt-launcher-vm1-5gxvg -- virsh dumpxml default_vm1
<domain type='kvm' id='1'>
  <name>default_vm1</name>
...

Viewing libvirt logs and full the QEMU command-line

The libvirt logs are captured by Kubernetes so you can view them with kubectl log <virt-launcher-pod-name>. If you don't know the virt-launcher pod name, check with kubectl get pod and look for your virtual machine's name.

The full QEMU command-line is part of the libvirt logs, but unescaping the JSON string is inconvenient. Here is another way to get the full QEMU command-line:

$ kubectl exec <virt-launcher-pod-name> -- ps aux | grep qemu

Customizing KubeVirt's libvirt domain XML

KubeVirt has a feature for customizing libvirt domain XML called hook sidecars. After the libvirt XML is generated, it is sent to a user-defined container that processes the XML and returns it back. The libvirt domain is defined using this processed XML. To learn more about how it works, check out the documentation.

Hook sidecars are available when the Sidecar feature gate is enabled in the kubevirt/kubevirt custom resource. Normally only the cluster administrator can modify the kubevirt CR, so be sure to check when trying this feature:

$ kubectl auth can-i update  kubevirt/kubevirt -n kubevirt
yes

Although you can provide a complete container image for the hook sidecar, there is a shortcut if you just want to run a script. A generic hook sidecar image is available that launches a script which can be provided as a ConfigMap. Here is example YAML including a ConfigMap that I've used to test the libvirt IOThread Virtqueue Mapping feature:

---
apiVersion: kubevirt.io/v1
kind: KubeVirt
metadata:
  name: kubevirt
  namespace: kubevirt
spec:
  configuration:
    developerConfiguration: 
      featureGates:
        - Sidecar
---
apiVersion: cdi.kubevirt.io/v1beta1
kind: DataVolume
metadata:
  name: "fedora"
spec:
  storage:
    accessModes:
        - ReadWriteOnce
    resources:
      requests:
        storage: 5Gi
  source:
    http:
      url: "https://download.fedoraproject.org/pub/fedora/linux/releases/38/Cloud/x86_64/images/Fedora-Cloud-Base-38-1.6.x86_64.raw.xz"
---
apiVersion: v1
kind: ConfigMap
metadata:
  name: sidecar-script
data:
  my_script.sh: |
    #!/usr/bin/env python3
    import xml.etree.ElementTree as ET
    import os.path
    import sys
    
    NUM_IOTHREADS = 4
    VOLUME_NAME = 'data' # VirtualMachine volume name
    
    def main(xml):
        domain = ET.fromstring(xml)
    
        domain.find('iothreads').text = str(NUM_IOTHREADS)
    
        disk = domain.find(f"./devices/disk/alias[@name='ua-{VOLUME_NAME}']..")
        driver = disk.find('driver')
        del driver.attrib['iothread']
        iothreads = ET.SubElement(driver, 'iothreads')
        for i in range(NUM_IOTHREADS):
            iothread = ET.SubElement(iothreads, 'iothread')
            iothread.set('id', str(i + 1))
    
        ET.dump(domain)
    
    if __name__ == "__main__":
        # Workaround for https://github.com/kubevirt/kubevirt/issues/11276
        if os.path.exists('/tmp/ran-once'):
            main(sys.argv[4])
        else:
            open('/tmp/ran-once', 'wb')
            print(sys.argv[4])
---
apiVersion: kubevirt.io/v1
kind: VirtualMachineInstance
metadata:
  creationTimestamp: 2018-07-04T15:03:08Z
  generation: 1
  labels:
    kubevirt.io/os: linux
  name: vm1
  annotations:
    hooks.kubevirt.io/hookSidecars: '[{"args": ["--version", "v1alpha3"],
      "image": "kubevirt/sidecar-shim:20240108_99b6c4bdb",
      "configMap": {"name": "sidecar-script",
                    "key": "my_script.sh",
                    "hookPath": "/usr/bin/onDefineDomain"}}]'
spec:
  domain:
    ioThreadsPolicy: auto
    cpu:
      cores: 8
    devices:
      blockMultiQueue: true
      disks:
      - disk:
          bus: virtio
        name: disk0
      - disk:
          bus: virtio
        name: data
    machine:
      type: q35
    resources:
      requests:
        memory: 1024M
  volumes:
  - name: disk0
    persistentVolumeClaim:
      claimName: fedora
  - name: data
    emptyDisk:
      capacity: 8Gi

If you need to go down one level further and customize the QEMU command-line, see my post on passing QEMU command-line options in libvirt domain XML.

More KubeVirt debugging tricks

The official KubeVirt documentation has a Virtualization Debugging section with more tricks for customizing libvirt logging, launching QEMU with strace or gdb, etc. Thanks to Alice Frosi for sharing the link!

Conclusion

It is possible to get libvirt access in KubeVirt for development and testing. This can make troubleshooting easier and it gives you the full range of libvirt domain XML if you want to experiment with features that are not yet exposed by KubeVirt.

A vDPA device is a type of device that follows the virtio specification for its datapath but has a vendor-specific control path.

vDPA devices can be both physically located on the hardware or emulated by software.

A small vDPA parent driver in the host kernel is required only for the control path. The main advantage is the unified software stack for all vDPA devices:

• vhost interface (vhost-vdpa) for userspace or guest virtio driver, like a VM running in QEMU
• virtio interface (virtio-vdpa) for bare-metal or containerized applications running in the host
• management interface (vdpa netlink) for instantiating devices and configuring virtio parameters

Useful Resources

Many blog posts and talks have been published in recent years that can help you better understand vDPA and the use cases. On vdpa-dev.gitlab.io we collected some of them; I suggest you at least explore the following:

• Introduction to vDPA kernel framework
• Introducing VDUSE: a software-defined datapath for virtio

Block devices

Most of the work in vDPA has been driven by network devices, but in recent years, we have also developed support for block devices.

The main use case is definitely leveraging the hardware to directly emulate the virtio-blk device and support different network backends such as Ceph RBD or iSCSI. This is the goal of some SmartNICs or DPUs, which are able to emulate virtio-net devices of course, but also virtio-blk for network storage.

The abstraction provided by vDPA also makes software accelerators possible, similar to existing vhost or vhost-user devices. We discussed about that at KVM Forum 2021.

We talked about the fast path and the slow path in that talk. When QEMU needs to handle requests, like supporting live migration or executing I/O throttling, it uses the slow path. During the slow path, the device exposed to the guest is emulated in QEMU. QEMU intercepts the requests and forwards them to the vDPA device by taking advantage of the driver implemented in libblkio. On the other hand, when QEMU doesn’t need to intervene, the fast path comes into play. In this case, the vDPA device can be directly exposed to the guest, bypassing QEMU’s emulation.

libblkio exposes common API for accessing block devices in userspace. It supports several drivers. We will focus more on virtio-blk-vhost-vdpa driver, which is used by virtio-blk-vhost-vdpa block device in QEMU. It only supports slow path for now, but in the future it should be able to switch to fast path automatically. Since QEMU 7.2, it supports libblkio drivers, so you can use the following options to attach a vDPA block device to a VM:

   -blockdev node-name=drive_src1,driver=virtio-blk-vhost-vdpa,path=/dev/vhost-vdpa-0,cache.direct=on \
   -device virtio-blk-pci,id=src1,bootindex=2,drive=drive_src1 \

Anyway, to fully leverage the performance of a vDPA hardware device, we can always use the generic vhost-vdpa-device-pci device offered by QEMU that supports any vDPA device and exposes it directly to the guest. Of course, QEMU is not able to intercept requests in this scenario and therefore some features offered by its block layer (e.g. live migration, disk format, etc.) are not supported. Since QEMU 8.0, you can use the following options to attach a generic vDPA device to a VM:

    -device vhost-vdpa-device-pci,vhostdev=/dev/vhost-vdpa-0

At KVM Forum 2022, Alberto Faria and Stefan Hajnoczi introduced libblkio, while Kevin Wolf and I discussed its usage in the QEMU Storage Deamon (QSD).

Software devices

One of the significant benefits of vDPA is its strong abstraction, enabling the implementation of virtio devices in both hardware and software—whether in the kernel or user space. This unification under a single framework, where devices appear identical for QEMU facilitates the seamless integration of hardware and software components.

Kernel devices

Regarding in-kernel devices, starting from Linux v5.13, there exists a simple simulator designed for development and debugging purposes. It is available through the vdpa-sim-blk kernel module, which emulates a 128 MB ramdisk. As highlighted in the presentation at KVM Forum 2021, a future device in the kernel (similar to the repeatedly proposed but never merged vhost-blk) could potentially offer excellent performance. Such a device could be used as an alternative when hardware is unavailable, for instance, facilitating live migration in any system, regardless of whether the destination system features a SmartNIC/DPU or not.

User space devices

Instead, regarding user space, we can use VDUSE. QSD supports it and thus allows us to export any disk image supported by QEMU, such as a vDPA device in this way:

qemu-storage-daemon \
    --blockdev file,filename=/path/to/disk.qcow2,node-name=file \
    --blockdev qcow2,file=file,node-name=qcow2 \
    --export type=vduse-blk,id=vduse0,name=vduse0,node-name=qcow2,writable=on

Containers, VMs, or bare-metal

As mentioned in the introduction, vDPA supports different buses such as vhost-vdpa and virtio-vdpa. This flexibility enables the utilization of vDPA devices with virtual machines or user space drivers (e.g., libblkio) through the vhost-vdpa bus. Additionally, it allows interaction with applications running directly on the host or within containers via the virtio-vdpa bus.

The vdpa tool in iproute2 facilitates the management of vdpa devices through netlink, enabling the allocation and deallocation of these devices.

Starting with Linux 5.17, vDPA drivers support driver_ovveride. This enhancement allows dynamic reconfiguration during runtime, permitting the migration of a device from one bus to another in this way:

# load vdpa buses
$ modprobe -a virtio-vdpa vhost-vdpa
# load vdpa-blk in-kernel simulator
$ modprobe vdpa-sim-blk

# instantiate a new vdpasim_blk device called `vdpa0`
$ vdpa dev add mgmtdev vdpasim_blk name vdpa0

# `vdpa0` is attached to the first vDPA bus driver loaded
$ driverctl -b vdpa list-devices
vdpa0 virtio_vdpa

# change the `vdpa0` bus to `vhost-vdpa`
$ driverctl -b vdpa set-override vdpa0 vhost_vdpa

# `vdpa0` is now attached to the `vhost-vdpa` bus
$ driverctl -b vdpa list-devices
vdpa0 vhost_vdpa [*]

# Note: driverctl(8) integrates with udev so the binding is preserved.

Examples

Below are several examples on how to use VDUSE and the QEMU Storage Daemon with VMs (QEMU) or Containers (podman). These steps are easily adaptable to any hardware that supports virtio-blk devices via vDPA.

qcow2 image available for host applications and containers

# load vdpa buses
$ modprobe -a virtio-vdpa vhost-vdpa

# create an empty qcow2 image
$ qemu-img create -f qcow2 test.qcow2 10G

# load vduse kernel module
$ modprobe vduse

# launch QSD exposing the `test.qcow2` image as `vduse0` vDPA device
$ qemu-storage-daemon --blockdev file,filename=test.qcow2,node-name=file \
  --blockdev qcow2,file=file,node-name=qcow2 \
  --export vduse-blk,id=vduse0,name=vduse0,num-queues=1,node-name=qcow2,writable=on &

# instantiate the `vduse0` device (same name used in QSD)
$ vdpa dev add name vduse0 mgmtdev vduse

# be sure to attach it to the `virtio-vdpa` device to use with host applications
$ driverctl -b vdpa set-override vduse0 virtio_vdpa

# device exposed as a virtio device, but attached to the host kernel
$ lsblk -pv
NAME     TYPE TRAN   SIZE RQ-SIZE  MQ
/dev/vda disk virtio  10G     256   1

# start a container with `/dev/vda` attached
podman run -it --rm --device /dev/vda --group-add keep-groups fedora:39 bash

Launch a VM using a vDPA device

# download Fedora cloud image (or use any other bootable image you want)
$ wget https://download.fedoraproject.org/pub/fedora/linux/releases/39/Cloud/x86_64/images/Fedora-Cloud-Base-39-1.5.x86_64.qcow2

# launch QSD exposing the VM image as `vduse1` vDPA device
$ qemu-storage-daemon \
  --blockdev file,filename=Fedora-Cloud-Base-39-1.5.x86_64.qcow2,node-name=file \
  --blockdev qcow2,file=file,node-name=qcow2 \
  --export vduse-blk,id=vduse1,name=vduse1,num-queues=1,node-name=qcow2,writable=on &

# instantiate the `vduse1` device (same name used in QSD)
$ vdpa dev add name vduse1 mgmtdev vduse

# initially it's attached to the host (`/dev/vdb`), because `virtio-vdpa`
# is the first kernel module we loaded
$ lsblk -pv
NAME     TYPE TRAN   SIZE RQ-SIZE  MQ
/dev/vda disk virtio  10G     256   1
/dev/vdb disk virtio   5G     256   1
$ lsblk /dev/vdb
NAME   MAJ:MIN RM  SIZE RO TYPE MOUNTPOINTS
vdb    251:16   0    5G  0 disk 
├─vdb1 251:17   0    1M  0 part 
├─vdb2 251:18   0 1000M  0 part 
├─vdb3 251:19   0  100M  0 part 
├─vdb4 251:20   0    4M  0 part 
└─vdb5 251:21   0  3.9G  0 part 

# and it is identified as `virtio1` in the host
$ ls /sys/bus/vdpa/devices/vduse1/
driver  driver_override  power  subsystem  uevent  virtio1

# attach it to the `vhost-vdpa` device to use the device with VMs
$ driverctl -b vdpa set-override vduse1 vhost_vdpa

# `/dev/vdb` is not available anymore
$ lsblk -pv
NAME     TYPE TRAN   SIZE RQ-SIZE  MQ
/dev/vda disk virtio  10G     256   1

# the device is identified as `vhost-vdpa-1` in the host
$ ls /sys/bus/vdpa/devices/vduse1/
driver  driver_override  power  subsystem  uevent  vhost-vdpa-1
$ ls -l /dev/vhost-vdpa-1
crw-------. 1 root root 511, 0 Feb 12 17:58 /dev/vhost-vdpa-1

# launch QEMU using `/dev/vhost-vdpa-1` device with the
# `virtio-blk-vhost-vdpa` libblkio driver
$ qemu-system-x86_64 -m 512M -smp 2 -M q35,accel=kvm,memory-backend=mem \
  -object memory-backend-memfd,share=on,id=mem,size="512M" \
  -blockdev node-name=drive0,driver=virtio-blk-vhost-vdpa,path=/dev/vhost-vdpa-1,cache.direct=on \
  -device virtio-blk-pci,drive=drive0

# `virtio-blk-vhost-vdpa` blockdev can be used with any QEMU block layer
# features (e.g live migration, I/O throttling).
# In this example we are using I/O throttling:
$ qemu-system-x86_64 -m 512M -smp 2 -M q35,accel=kvm,memory-backend=mem \
  -object memory-backend-memfd,share=on,id=mem,size="512M" \
  -blockdev node-name=drive0,driver=virtio-blk-vhost-vdpa,path=/dev/vhost-vdpa-1,cache.direct=on \
  -blockdev node-name=throttle0,driver=throttle,file=drive0,throttle-group=limits0 \
  -object throttle-group,id=limits0,x-iops-total=2000 \
  -device virtio-blk-pci,drive=throttle0

# Alternatively, we can use the generic `vhost-vdpa-device-pci` to take
# advantage of all the performance, but without having any QEMU block layer
# features available
$ qemu-system-x86_64 -m 512M -smp 2 -M q35,accel=kvm,memory-backend=mem \
  -object memory-backend-memfd,share=on,id=mem,size="512M" \
  -device vhost-vdpa-device-pci,vhostdev=/dev/vhost-vdpa-0

File systems and relational databases are like cousins. They share more than is apparent at first glance.

It's not immediately obvious that relational databases and file systems rely upon the same underlying concept. That underlying concept is the key-value store and this article explores how both file systems and databases can be implemented on top of key-value stores.

The key-value store interface

Key-value stores provide an ordered map data structure. A map is a data structure that supports storing and retrieving from a collection of pairs. It's called a map because it is like a mathematical relation from a given key to an associated value. These are the key-value pairs that a key-value store holds. Finally, ordered means that the collection can be traversed in sorted key order. Not all key-value store implementations support ordered traversal, but both file systems and databases need this property as we shall see.

Here is a key-value store with an integer key and a string value:

Notice that the keys can be enumerated in sorted order: 2 → 14 → 17.

A key-value store provides the following interface for storing and retrieving values by a given key:

• put(Key, Value) - an insert/update operation that stores a value for a given key
• get(Key) -> Value - a lookup operation that retrieves the most recently stored value for a given key
• first() -> Key, last() -> Key, next(Key) -> Key, prev(Key) -> Key - a cursor API that enumerates keys in sorted order

You've probably seen this sort of API if you have explored libraries like LevelDB, RocksDB, LMDB, BoltDB, etc or used NoSQL key-value stores. File systems and databases usually implement their own customized key-value stores rather than use these off-the-shelf solutions.

Why key-value stores are necessary

Let's look at how the key-value store interface relates to disks. Disks present a range of blocks that can be read or written at their block addresses. Disks can be thought of like arrays in programming. They have O(1) lookup and update time complexity but inserting or removing a value before the end of the array is O(n) because subsequent elements need to be copied. They are efficient for dense datasets where every element is populated but inefficient for sparse datasets that involve insertion and removal.

Workloads that involve insertion or removal are not practical when the cost is O(n) for realistic sizes of n. That's why programs often use in-memory data structures like hash tables or balanced trees instead of arrays. Key-value stores can be thought of as the on-disk equivalent to these in-memory data structures. Inserting or removing values from a key-value store takes sub-linear time, perhaps O(log n) or even better amortized time. We won't go into the data structures used to implement key-value stores, but B+ trees and Log-Structured Merge-Trees are popular choices.

This gives us an intuition about when key-value stores are needed and why they are an effective tool. Now let's look at how file systems and databases can be built on top of key-value stores next.

Building a file system on a key-value store

First let's start with how data is stored in files. A file system locates file data on disk by translating file offsets to Logical Block Addresses (LBAs). This is necessary because file data may not be stored contiguously on disk and files can be sparse with unallocated "holes" where nothing has been written yet. Thus, each file can be implemented as a key-value store with <Offset, <LBA, Length>> key-value pairs that comprise the translations needed to locate data on disk:

Reading and writing to the file involves looking up Offset -> LBA translations and inserting new translations when new blocks are allocated for the file. This is a good fit for a key-value store, but it's not the only place where file systems employ key-value stores.

File systems track free blocks that are not in used by files or metadata so that the block allocator can quickly satisfy allocation requests. This can be implemented as a key-value store with <LBA, Length> key-value pairs representing all free LBA ranges.

If the block allocator needs to satisfy contiguous allocation requests then a second key-value store with <Length, LBA> key-value pairs can serve as an efficient lookup or index. A best-fit allocator uses this key-value store by looking up the requested contiguous allocation size. Either an free LBA range of the matching size will be found or the next ordered key can be traversed when lookup fails to find a bigger free range capable of satisfying this allocation request. This is an important pattern with key-value stores: we can have one main key-value store plus one or more indices that are derived from the same key-value pairs but use a different datum as the key than the primary key-value store, allowing efficient lookups and ordered traversal. The same pattern will come up in databases too.

Next, let's look at how to represent directory metadata in a key-value store. Files are organized into a hierarchy of directories (or folders). The file system stores the directory entries belonging to each directory. Each directory can be organized as a key-value store with filenames as keys and inode numbers as values. Path traversal consists of looking up directory entries in each directory along file path components like home, user, and file in the path /home/user/file. When a file is created, a new directory entry is inserted. When a file is deleted, its directory entry is removed. The contents of a directory can be listed by traversing the keys.

Some file systems like BTRFS use key-value stores for other on-disk structures such as snapshots, checksums, etc, too. There is even a root key-value store in BTRS from which all these other key-value stores can be looked up. We'll see that the same concept of a "forest of trees" or a root key-value store that points to other key-value stores also appears in databases below.

Building a database on a key-value store

The core concept in relational databases is the table, which contains the rows of the data we wish to store. The table columns are the various fields that are stored by each row. One or more columns make up the primary key by which table lookups are typically performed. The table can be implemented as a key-value store using the primary key columns as the key and the remainder of the columns as the value:

This key-value store can look up rows in the table by their Id. What if we want to look up a row by Username instead?

To enable efficient lookups by Username, a secondary key-value store called an index maintains a mapping from Username to Id. The index does not duplicate all the columns in the table, just the Username and Id. To perform a query like SELECT * FROM Users WHERE Username = 'codd', the index is first used to look up the Id and then the remainder of the columns are looked up from the table.

SQLite's file format documentation shows the details of how data is organized along these lines and the power of key-value stores. The file format has a header the references the "table b-tree" that points to the roots of all tables. This means there is an entry point key-value store that points to all the other key-value stores associated with tables, indices, etc in the database. This is similar to the forest of trees we saw in the BTRFS file system where the key-value store acts as the central data structure tying everything together.

Conclusion

If a disk is like an array in programming, then a key-value store is like a dict. It offers a convenient interface for storing and retrieving sparse data with good performance. Both file systems and databases are abundant with sparse data and therefore fit naturally on top of key-value store. The actual key-value store implementations inside file systems and databases may be specialized variants of B-trees and other data structures that don't even call themselves key-value stores, but the fundamental abstraction upon which file systems and databases are built is the key-value store.

This post is about the AioContext lock removal in QEMU 9.0 (planned for release in 2024), how we got here, and what it means for multi-threaded code in QEMU.

Early QEMU as a single-threaded program

Until 2009 QEMU was largely a single-threaded program. This had the benefit that the code didn't need to consider thread-safety and was thus simpler and less bug-prone. The main loop interleaved running the next piece of guest code and handling external events such as timers, disk I/O, and network I/O. This architecture had the downside that emulating multi-processor guests was bottlenecked by the single host CPU on which QEMU ran. There was no parallelism and this became problematic as multi-processor guests became popular.

Multi-threading with vCPU threads and the Big QEMU Lock

The architecture was modified to support running dedicated vCPU threads for KVM guests. This made parallelism possible for multi-processor guests but the feature was initially only available for KVM guests. The Multi-Threaded TCG (MTTCG) feature eventually allowed translated code to also take advantage of vCPU threads in 2016.

A straightforward approach to making all existing code thread-safe was taken: the Big QEMU Lock (BQL) was introduced to serialize access to QEMU's internal state. The BQL is a single global mutex that is used to protect the majority of QEMU's internal state. KVM vCPU threads do not need access to QEMU's internal state while executing guest code, so they don't hold the BQL most of the time. The main loop thread drops the BQL while blocking in ppoll(2) and this allows vCPU threads to acquire the lock when they come out of guest code.

Multi-threading with IOThreads and the AioContext lock

Although the vCPU bottleneck had been solved, device emulation still ran with the BQL held. This meant that only a single QEMU thread could process I/O requests at a time. For I/O bound workloads this was a bottleneck and especially disk I/O performance suffered due to this limitation. My first attempt at removing the bottleneck in 2012 amounted to writing a new "dataplane" code path outside the BQL, but it lacked the features that users needed like disk image file formats, I/O throttling, etc because it couldn't use the existing code that relied on the BQL. The long term solution would be introducing thread-safety to the existing code and that led to the creation of the AioContext lock.

The AioContext lock was like a mini-BQL but for an event loop (QEMU calls this an AioContext) instead of the entire program. Initially the event loop would acquire the lock while running event handlers, thereby ensuring mutual exclusion for all handlers associated with the event loop. Another thread could acquire the lock to stop the event loop from running and safely access variables. This was a crude approach though and propagated the BQL way of thinking further. QEMU began to suffer from deadlocks and race conditions now that multi-threading was possible. Although I wrote developer documentation about how the model worked, it became tricky to gain confidence in the safety of the code as the whole QEMU block layer needed to grapple with AioContext locking and did so incompletely and inconsistently.

The upshot of all of this was that disk I/O processing could run in a dedicated event loop thread (QEMU calls this an IOThread) while the QEMU monitor could acquire the AioContext lock for a brief moment to inspect the emulated disk for an "info block" monitor command, for example. Unlike the earlier "dataplane" approach, it was now possible for the QEMU block layer to run outside the BQL and instead rely on the AioContext lock.

Removing the AioContext lock

Paolo Bonzini had the idea to gradually eliminate the AioContext lock in favor of fine-grained locks because we kept hitting problems with the AioContext lock that I described above. His insight was to change the model so that handler functions would explicitly take their AioContext's lock instead acquiring the lock around the entire event loop iteration. The advantage to letting handlers take the lock was that they could also replace it with another mechanism. Eventually it would be possible to move away from the AioContext lock.

What came after was a multi-year journey that I credit to Paolo's vision. Emanuele Giuseppe Esposito worked with Paolo on putting fine-grained locking into practice and on sorting through the entire QEMU block layer to determine under which threads and locks variables were accessed. This was a massive effort and required a lot of persistence. Kevin Wolf figured out how to use clang's Thread Safety Analysis (TSA) to check some of the locking rules at compile time. Kevin also spent a lot of time protecting the block driver graph with a reader/writer lock so that in-flight I/O does not crash amidst modifications to the graph. Emanuele and Kevin gave a talk at KVM Forum 2023 about the larger QEMU multi-queue block layer effort and the slides are available here (PDF).

Once everything that previously relied on the AioContext lock had switched to another form of thread-safety, it was possible to remove the AioContext lock as nothing used it anymore. The BQL is still widely used and covers global state that is accessed from few threads. Code that can run in any IOThread now uses its own locks or other mechanisms. The complexity of the codebase is still roughly the same as with the AioContext lock, but now there are fine-grained locks, which are easier to understand and there are fewer undocumented locking assumptions that led to deadlocks and races in the past.

Conclusion

QEMU's AioContext lock enabled multi-threading but was also associated with deadlocks and race conditions due to its ambient nature. From QEMU 9.0 onwards, QEMU will switch to fine-grained locks that are more localized and make thread-safety more explicit. Changing locking in a large program is time-consuming and difficult. It took a multi-person multi-year effort to complete this work, but it forms the basis for further work including the QEMU multi-queue block layer effort that push multi-threading further in QEMU.

Update: The video is now available here and the slides are available here (PDF).

I will be at Linux Plumbers Conference 2023 to present "Trust, confidentiality, and hardening: the virtio lessons" at 2:30pm on Wednesday, November 15th. Michael Tsirkin and I prepared this talk about the evolution of the trust model of the Linux VIRTIO drivers. It explores how the drivers have been hardened in response to new use cases for VIRTIO, including Linux VDUSE, hardware VIRTIO devices, and Confidential Computing.

Details are available on the LPC schedule. Come watch to talk to find out how drivers work when you can't trust the hypervisor!

We’d like to announce the availability of the QEMU 8.2.0 release. This release contains 3200+ commits from 238 authors.

You can grab the tarball from our download page. The full list of changes are available in the changelog.

Highlights include:

• New virtio-sound device emulation
• New virtio-gpu rutabaga device emulation used by Android emulator
• New hv-balloon for dynamic memory protocol device for Hyper-V guests
• New Universal Flash Storage device emulation
• Network Block Device (NBD) 64-bit offsets for improved performance
• dump-guest-memory now supports the standard kdump format
• ARM: Xilinx Versal board now models the CFU/CFI, and the TRNG device
• ARM: CPU emulation support for cortex-a710 and neoverse-n2
• ARM: architectural feature support for PACQARMA3, EPAC, Pauth2, FPAC, FPACCOMBINE, TIDCP1, MOPS, HBC, and HPMN0
• HPPA: CPU emulation support for 64-bit PA-RISC 2.0
• HPPA: machine emulation support for C3700, including Astro memory controller and four Elroy PCI bridges
• LoongArch: ISA support for LASX extension and PRELDX instruction
• LoongArch: CPU emulation support for la132
• RISC-V: ISA/extension support for AIA virtualization support via KVM, and vector cryptographic instructions
• RISC-V: Numerous extension/instruction cleanups, fixes, and reworks
• s390x: support for vfio-ap passthrough of crypto adapter for protected virtualization guests
• Tricore: support for TC37x CPU which implements ISA v1.6.2
• Tricore: support for CRCN, FTOU, FTOHP, and HPTOF instructions
• x86: Zen support for PV console and network devices
• and lots more…

Thank you to everybody who contributed to this release, whether that was by writing code, reporting bugs, improving documentation, testing, or providing the project with CI resources. We couldn’t do these without you!

What is W^X?

If this sounds familiar to you, it probably is. It means that memory should be either writable ("W", typically data), or executeable ("X", typically code), but not both. Elsewhere in the software industry this is standard security practice since ages. Now it starts to take off for UEFI firmware too.

This is a deep dive into recent changes, in both code (firmware) and administration (secure boot signing), the consequences this has for the linux, and the current state of affairs.

Changes in the UEFI spec and edk2

All UEFI memory allocations carry a memory type (EFI_MEMORY_TYPE). UEFI tracks since day one whenever a memory allocation is meant for code or data, among a bunch of other properties such as boot service vs. runtime service memory.

For a long time it didn't matter much in practice. The concept of virtual memory does not exist for UEFI. IA32 builds even run with paging disabled (and this is unlikely to change until the architecture disappears into irrelevance). Other architectures use identity mappings.

While UEFI does not use address translation, nowdays it can use page tables to enforce memory attributes, including (but not limited to) write and execute permissions. When configured to do so it will set code pages to R-X and data pages to RW- instead of using RWX everywhere, so code using memory types incorrectly will trigger page faults.

New in the UEFI spec (added in version 2.10) is the EFI_MEMORY_ATTRIBUTE_PROTOCOL. Sometimes properties of memory regions need to change, and this protocol can be used to do so. One example is a self-uncompressing binary, where the memory region the binary gets unpacked to initially must be writable. Later (parts of) the memory region must be flipped from writable to executeable.

As of today (Dec 2023) edk2 has a EFI_MEMORY_ATTRIBUTE_PROTOCOL implementation for the ARM and AARCH64 architectures, so this is present in the ArmVirt firmware builds but not in the OVMF builds.

Changed secure boot signing requirements

In an effort to improve firmware security in general and especially for secure boot Microsoft changed the requirements for binaries they are willing to sign with their UEFI CA key.

One key requirement added is that the binary layout must allow to enforce memory attributes with page tables, i.e. PE binary sections must be aligned to page size (4k). Sections also can't be both writable and executable. And the application must be able to deal with data section being mapped as not executable (NX_COMPAT).

These requirements apply to the binary itself (i.e. shim.efi for linux systems) and everything loaded by the binary (i.e. grub.efi, fwupd.efi and the linux kernel).

Where does linux stand?

We had and party still have a bunch of problems in all components involved in the linux boot process, i.e. shim.efi, grub.efi and the efi stub of the linux kernel.

Some are old bugs such as memory types not being used correctly, which start to cause problems due to the firmware becoming more strict. Some are new problems due to Microsoft raising the bar for PE binaries, typically sections not being page-aligned. The latter are easily fixed in most cases, often it is just a matter of adding alignment to the right places in the linker scripts.

Lets have closer look at the components one by one:

shim.efi

shim added code to use the new EFI_MEMORY_ATTRIBUTE_PROTOCOL before it was actually implemented by any firmware. Then this was released completely untested. Did not work out very well, we got a nice time bomb, and edk2 implementing EFI_MEMORY_ATTRIBUTE_PROTOCOL for arm triggered it ...

Fixed in main branch, no release yet.

Getting new shim.efi binaries signed by Microsoft depends on the complete boot chain being compilant with the new requirements, which prevents shim bugfixes being shipped to users right now.

That should be solved soon though, see the kernel section below.

grub.efi

grub.efi used to use memory types incorrectly.

Fixed upstream years ago, case closed.

Well, in theory. Upstream grub development goes at glacial speeds, so all distros carry a big stack of downstream patches. Not surprisingly that leads to upstream fixes being absorbed slowly and also to bugs getting reintroduced.

So, in practice we still have buggy grub versions in the wild. It is getting better though.

The linux kernel

The linux kernel efi stub had it's fair share of bugs too. On non-x86 architectures (arm, riscv, ...) all issues have been fixed a few releases ago. They all share much of the efi stub code base and also use the same self-decompressing method (CONFIG_EFI_ZBOOT=y).

On x86 this all took a bit longer to sort out. For historical reasons x86 can't use the zboot approach used by the other architectures. At least as long as we need hybrid BIOS/UEFI kernels, which most likely will be a number of years still.

The final x86 patch series has been merged during the 6.7 merge window. So we should have a fixed stable kernel in early January 2024, and distros picking up the new kernel in the following weeks or months. Which in turn should finally unblock shim updates.

There should be enough time to get everything sorted for the spring distro releases (Fedora 40, Ubuntu 24.04).

edk2 config options

edk2 has a bunch of config options to fine tune the firmware behavior, both compile time and runtime. The relevant ones for the problems listed above are:

PcdDxeNxMemoryProtectionPolicy

Compile time option. Use the --pcd switch for the edk2 build script to set these. It's bitmask, with one bit for each memory type, specifying whenever the firmware shoud apply memory protections for that particular memory type, by setting the flags in the page tables accordingly.

Strict configuration is PcdDxeNxMemoryProtectionPolicy = 0xC000000000007FD5. This is also the default for ArmVirt builds.

Bug compatible configuration is PcdDxeNxMemoryProtectionPolicy = 0xC000000000007FD1. This excludes the EfiLoaderData memory type from memory protections, so using EfiLoaderData allocations for code will not trigger page faults. Which is an very common pattern seen in boot loader bugs.

PcdUninstallMemAttrProtocol

Compile time options, for ArmVirt only. Brand new, committed to the edk2 repo this week (Dec 12th 2023). When set to TRUE the EFI_MEMORY_ATTRIBUTE_PROTOCOL will be unistalled. Default is FALSE.

Setting this to TRUE will work around the shim bug.

opt/org.tianocore/UninstallMemAttrProtocol

Runtime option, for ArmVirt only. Also new. Can be set using -fw_cfg on the qemu command line: -fw_cfg name=opt/org.tianocore/UninstallMemAttrProtocol,string=y|n. This is a runtime override for PcdUninstallMemAttrProtocol. Works for both enabling and disabling the shim bug workaround.

In the future PcdDxeNxMemoryProtectionPolicy will probably disappear in favor of memory profiles, which will allow to configure the same settings (plus a few more) at runtime.

Hands on, part #1 — using fedora edk2 builds

The default builds in the edk2-ovmf and edk2-aarch64 packages are configured to be bug compatible, so VMs should boot fine even in case the guests are using a buggy boot chain.

While this is great for end users it doesn't help much for bootloader development and testing, so there are alternatives. The edk2-experimental package comes with a collection of builds better suited for that use case, configured with strict memory protections and (on aarch64) EFI_MEMORY_ATTRIBUTE_PROTOCOL enabled, so you can see buggy builds actually crash and burn. 🔥

AARCH64 architecture

For AARCH64 this is /usr/share/edk2/experimental/QEMU_EFI-strictnx-pflash.raw. The magic words for libvirt are:

<domain type='kvm'>
[ ... ]
  <os>
    <type arch='aarch64' machine='virt'>hvm</type>
    <loader readonly='yes' type='pflash'>/usr/share/edk2/experimental/QEMU_EFI-strictnx-pflash.raw</loader>
    <nvram template='/usr/share/edk2/aarch64/vars-template-pflash.raw'/>
  </os>
[ ... ]

If a page fault happens you will get this line ...

  Synchronous Exception at 0x00000001367E6578

... on the serial console, followed by a stack trace and register dump.

X64 architecture

For X64 this is /usr/share/edk2/experimental/OVMF_CODE_4M.secboot.strictnx.qcow2. Needs edk2-20231122-12.fc39 or newer. The magic words for libvirt are:

<domain type='kvm'>
[ ... ]
  <os>
    <type arch='x86_64' machine='q35'>hvm</type>
    <loader readonly='yes' secure='yes' type='pflash' format='qcow2'>/usr/share/edk2/experimental/OVMF_CODE_4M.secboot.strictnx.qcow2</loader>
    <nvram template='/usr/share/edk2/ovmf/OVMF_VARS_4M.secboot.qcow2' format='qcow2'/>
  </os>
[ ... ]

It is also a good idea to add a debug console to capture the firmware log:

    <serial type='null'>
      <log file='/path/to/firmware.log' append='off'/>
      <target type='isa-debug' port='1'>
        <model name='isa-debugcon'/>
      </target>
      <address type='isa' iobase='0x402'/>
    </serial>

If you are lucky the page fault is logged there, also with an register dump. If you are not so lucky the VM will just reset and reboot.

Hands on, part #2 — using virt-firmware

The virt-firmware project is a collection of python modules and scripts for working with efi variables, efi varstores and also pe binaries. In case your distro hasn't packages you can install it using pip like most python packages.

virt-fw-vars

The virt-fw-vars utility can work with efi varstores. For example it is used to create the OVMF_VARS*secboot* files, enrolling the secure boot certificates into the efi security databases.

The simplest operation is to print the variable store:

virt-fw-vars --input /usr/share/edk2/ovmf/OVMF_VARS_4M.secboot.qcow2 \
             --print --verbose | less

When updating edk2 varstores virt-fw-vars always needs both input and output files. If you want change an existing variable store both input and output can point to the same file. For example you can turn on shim logging for an existing libvirt guest this way:

virt-fw-vars --input /var/lib/libvirt/qemu/nvram/${guest}_VARS.qcow2 \
             --output /var/lib/libvirt/qemu/nvram/${guest}_VARS.qcow2 \
             --set-shim-verbose

The next virt-firmware version will get a new --inplace switch to avoid listing the file twice on the command line for this use case.

If you want start from scratch you can use an empty variable store from /usr/share/edk2 as input. For example when creating a new variable store template with the test CA certificate (shipped with pesign.rpm) enrolled additionally:

dnf install -y pesign
certutil -L -d /etc/pki/pesign-rh-test -n "Red Hat Test CA" -a \
             | openssl x509 -text > rh-test-ca.txt
virt-fw-vars --input /usr/share/edk2/ovmf/OVMF_VARS_4M.qcow2 \
             --output OVMF_VARS_4M.secboot.rhtest.qcow2 \
             --enroll-redhat --secure-boot \
             --add-db OvmfEnrollDefaultKeys rh-test-ca.txt

The test CA will be used by all Fedora, CentOS Stream and RHEL build infrastructure to sign unofficial builds, for example when doing scratch builds in koji or when building rpms locally on your developer workstation. If you want test such builds in a VM, with secure boot enabled, this is a convenient way to do it.

pe-inspect

Useful for having a look at EFI binaries is pe-inspect. If this isn't present try pe-listsigs. Initially the utility only listed the signatures, but was extended over time to show more information, so I added the pe-inspect alias later on.

Below is the output for an 6.6 x86 kernel, you can see it does not have the patches to page-align the sections:

# file: /boot/vmlinuz-6.6.4-200.fc39.x86_64
#    section: file 0x00000200 +0x00003dc0  virt 0x00000200 +0x00003dc0  r-x (.setup)
#    section: file 0x00003fc0 +0x00000020  virt 0x00003fc0 +0x00000020  r-- (.reloc)
#    section: file 0x00003fe0 +0x00000020  virt 0x00003fe0 +0x00000020  r-- (.compat)
#    section: file 0x00004000 +0x00df6cc0  virt 0x00004000 +0x05047000  r-x (.text)
#    sigdata: addr 0x00dfacc0 +0x00000d48
#       signature: len 0x5da, type 0x2
#          certificate
#             subject CN: Fedora Secure Boot Signer
#             issuer  CN: Fedora Secure Boot CA
#       signature: len 0x762, type 0x2
#          certificate
#             subject CN: kernel-signer
#             issuer  CN: fedoraca

pe-inspect also knows the names for a number of special sections and supports decoding and pretty-printing them, for example here:

# file: /usr/lib/systemd/boot/efi/systemd-bootx64.efi
#    section: file 0x00000400 +0x00011a00  virt 0x00001000 +0x0001191f  r-x (.text)
#    section: file 0x00011e00 +0x00003a00  virt 0x00013000 +0x00003906  r-- (.rodata)
#    section: file 0x00015800 +0x00000400  virt 0x00017000 +0x00000329  rw- (.data)
#    section: file 0x00015c00 +0x00000200  virt 0x00018000 +0x00000030  r-- (.sdmagic)
#       #### LoaderInfo: systemd-boot 254.7-1.fc39 ####
#    section: file 0x00015e00 +0x00000200  virt 0x00019000 +0x00000049  r-- (.osrel)
#    section: file 0x00016000 +0x00000200  virt 0x0001a000 +0x000000de  r-- (.sbat)
#       sbat,1,SBAT Version,sbat,1,https://github.com/rhboot/shim/blob/main/SBAT.md
#       systemd,1,The systemd Developers,systemd,254,https://systemd.io/
#       systemd.fedora,1,Fedora Linux,systemd,254.7-1.fc39,https://bugzilla.redhat.com/
#    section: file 0x00016200 +0x00000200  virt 0x0001b000 +0x00000084  r-- (.reloc)

virt-fw-sigdb

The last utility I want introduce is virt-fw-sigdb, which can create, parse and modify signature databases. The signature database format is used by the firmware to store certificates and hashes in EFI variables. But sometimes the format used for files too. virt-firmware has the functionality anyway, so I've added a small frontend utility to work with those files.

One file in signature database format is /etc/pki/ca-trust/extracted/edk2/cacerts.bin which contains the list of of trusted CAs in sigature database format. Can be used to pass the CA list to the VM firmware for TLS connections (https network boot).

Shim also uses that format when compiling multiple certificates into the built-in VENDOR_DB or VENDOR_DBX databases.

Final remarks

Thats it for today folks. Hope you find this useful.

While Linux on IBM Z and LinuxONE could be used as a target for Ansible scripts ever since, the backend had to be run on other architectures. But no longer so: Starting today, the entire Red Hat Ansible Automation Platform is becoming available on IBM Z and LinuxONE!

See here for more details, and here for the formal announcement from Red Hat.

Like many people over the last few months I've been playing with a number of Large Language Models (LLMs). LLMs are perhaps best typified by the current media star ChatGPT. It is hard to avoid the current media buzz while every tech titan is developing their "AI" play and people are exposed to tools where the label of Artificial Intelligence is liberally applied. The ability of these models to spit out competent comprehensible text is seemingly a step change in ability compared to previous generations of tech.

I thought I would try and collect some of my thoughts and perspectives on this from the point of view of a systems programmer. For those not familiar with the term is refers to the low level development of providing platforms for the applications people actually use. In my case a lot of the work I do on QEMU which involves emulating the very lowest level instructions a computer can do: the simple arithmetic and comparison of numbers that all code is eventually expressed as.

Magic numbers and computing them

I claim no particular expertise on machine learning so expect this to be a very superficial explanation of whats going on.

In normal code the CPU tends to execute a lot of different instruction sequences as a program runs through solving the problem you have set it. The code that calculates where to draw your window will be different to the code checking the network for new data, or the logic that stores information safely on your file system. Each of those tasks is decomposed and abstracted into simpler and simpler steps until eventually it is simple arithmetic dictating what the processor should do do next. You occasionally see hot spots where a particular sequence of instructions are doing a lot of heavy lifting. There is a whole discipline devoted to managing computational complexity and ensuring algorithms are as efficient as possible.

However the various technologies that are currently wowing the world work very differently. They are models of various networks represented by a series of magic numbers or "weights" arranged in a hierarchical structure of interconnected matrices. While there is a lot of nuance to how problems are encoded and fed into these models fundamentally the core piece of computation is multiplying a bunch of numbers with another bunch of numbers feeding their results into the next layer of the network. At the end of the process the model spits out a prediction of the most likely next word is going to be. After selecting one the cycle repeats taking to account our expanded context to predict the most likely next word.

The "models" that drive these things are described mostly by the number of parameters they have. This encompasses the number of inputs and outputs they have and the number of numbers in between. For example common small open source models start at 3 billion parameters with 7, 13 and 34 billion also being popular sizes. Beyond that it starts getting hard to run models locally on all but the most tricked out desktop PCs. As a developer my desktop is pretty beefy (32 cores, 64Gb RAM) and can chew through computationally expensive builds pretty easily. However as I can't off-load processing onto my GPU a decent sized model will chug out a few words a second while maxing out my CPU. The ChatGPT v4 model is speculated to run about 1.7 trillion parameters which needs to be run on expensive cloud hardware - I certainly don't envy OpenAI their infrastructure bill.

Of course the computational power needed to run these models is a mere fraction of what it took to train them. In fact the bandwidth and processing requirements are so large it pays to develop custom silicon that is really good at multiplying large amounts of numbers and not much else. You can get a lot more bang for your buck compared to running those calculations on a general purpose CPU designed for tackling a wide range of computation problems.

The Value of Numbers

Because of the massive investment in synthesising these magic numbers they themselves become worth something. The "magic sauce" behind a model is more about how it was trained and what data was used to do it. We already know its possible to encode societies biases into models due to sloppy selection of the input data. One of the principle criticisms of proprietary generative models is how opaque the training methods are making it hard to judge their safety. The degree to which models may regurgitate data without any transformation is hard to quantify when you don't know what went into it.

As I'm fundamentally more interested in knowing how the technology I use works under the hood its fortunate there is a growing open source community working on building their own models. Credit should be given to Meta who made their language model LLaMA 2 freely available on fairly permissive terms. Since then there has been an explosion of open source projects that can run the models (e.g: llama.cpp, Ollama) and provide front-ends (e.g: Oobabooga's text generation UI, Ellama front-end for Emacs) for them.

Smaller Magic Numbers

The principle place where this work is going on is Hugging Face. Think of it as the GitHub of the machine learning community. It provides an environment for publishing and collaborating on data sets and models as well hosting and testing their effectiveness in various benchmarks. This make experimenting with models accessible to developers who aren't part of the well funded research divisions of the various tech titans. Datasets for example come with cards which describe the sources that went into these multi-terabyte files.

One example of a such is the RedPajama dataset. This is an open source initiative to recreate the LLaMA training data which combines data from the open web and well as numerous permissively licensed source such as Wikipedia, GitHub, StackExchange and ArXiv. This dataset has been used to train models like OpenLLaMA in an attempt to provide an unencumbered version of Meta's LLaMA 2. However training up these foundational models is an expensive and time consuming task, the real action is taking these models and then fine tuning them for particular tasks.

To fine tune a model you first take a general purpose model and further train it against data with a specific task in mind. The purpose of this is not only to make your new model better suited for a particular task but also to optimise the number of calculations that model has to do to achieve acceptable results. This is also where the style of prompting will be set as you feed the model examples of the sort of questions and answers you want it to give.

The are further stages that be applied including "alignment" where you ensure results are broadly in tune with the values of the organisation. This is the reason the various chatbots around won't readily cough up the recipe to build nukes or make it easier to explicitly break the law. This can be augmented with Reinforcement Learning through Human Feedback (RHLF) which is practically the purpose of every CAPTCHA you'll have filled in over the last 25 years online.

Finally the model can be quantised to make it more manageable. This takes advantage of the fact that a lot of the numbers will be have a negligible effect on the result for a wide range of inputs. In those cases there is no point storing them at full precision. As computation is a function of the number of bits of information being processed this also reduces the cost of computation. While phones and other devices are increasingly including dedicated hardware to process these models they are still constrained by physics - and the more you process the more heat you need to dissipate, the more battery you use and the more bandwidth you consume. Obviously the more aggressively you quantise the models the worse it will perform so there is an engineering trade off to make. Phones work best with multiple highly tuned models solving specific tasks as efficiently as possible. Fully flexible models giving a J.A.R.V.I.S like experience will probably always need to run in the cloud where thermal management is simply an exercise in plumbing.

Making magic numbers work for you

Before we discuss using models I want to discuss 3 more concepts: "prompts", "context" and "hallucinations".

The prompt is the closest thing there is to "programming" the model. The prompt can be purely explicit or include other inputs behind the scenes. For example the prompt can instruct the model to be friendly or terse, decorate code snippets with markdown, make changes as diffs or in full functions. Generally the more explicit your prompt is about what you want the better the result you get from the model. Prompt engineering has the potential to be one of those newly created job titles that will have to replace the jobs obsoleted by advancing AI. One of the ways to embed AI APIs into your app is to create a task specific prompt that will be put in front of user input that guides the results to what you want.

The "context" is the rest of the input into the model. That could be the current conversation in a chat or the current page of source code in a code editor. The larger the context the more reference the model has for its answer although that does come at the cost of even more composition as the context makes for more input parameters into the model.

In a strong candidate for 2023's word of the year "hallucination" describes the quirky and sometime unsettling behaviour of models outputting weird sometimes contradictory information. They will sincerely and confidently answer questions with blatant lies or start regurgitating training data when given certain prompts. It is a salient reminder that the statistical nature of these generative models will mean they occasionally spout complete rubbish. They are also very prone to following the lead of their users - the longer you chat with a model the more likely it is to end up agreeing with you.

So lets talk about what these models can and can't do. As a developer one of the areas I'm most interested in is their ability to write code. Systems code especially is an exercise in precisely instructing a computer what to do in explicit situations. I'd confidently predicted my job would be one of the last to succumb to the advance of AI as systems aren't something you can get "mostly" right. It was quite a shock when I first saw quite how sophisticated the generated code can be.

Code Review

One of the first things I asked ChatGPT to do was review a function I'd written. It manged to make 6 observations about the code, 3 of which where actual logic problems I'd missed and 3 where general points about variable naming and comments. The prompt is pretty important though. If not constrained to point out actual problems LLMs tend to have a tendency to spit out rather generic advice about writing clean well commented code.

They can be super useful when working with an unfamiliar language or framework. If you are having trouble getting something to work it might be faster to ask an LLM how to fix your function that spending time reading multiple StackOverflow answers to figure out what you've misunderstood. If compiler errors are confusing supplying the message alongside the code can often be helpful in understanding whats going on.

Writing Code

However rather than just suggesting changes one very tempting use case is writing code from scratch based on a description of what you want. Here the context is very important, the more detail you provide the better chance of generating something useful. My experience has been that the solutions are usually fairly rudimentary and can often benefit from a manual polishing step once you have something working.

For my QEMU KVM Forum 2023 Keynote I got ChatGPT to write the first draft of a number of my data processing scripts. However it missed obvious optimisations by repeatedly reading values inside inner loops that made the scripts slower than they needed to be.

If the task is a straight transformation they are very good. Ask an LLM to convert a function in one language into another and it will do a pretty good job - and probably with less mistakes than your first attempt. However there are limitations. For example I asked a model to convert some Aarch64 assembler into the equivalent 32 bit Arm assembler. It did a very good job of the mechanical part of that but missed the subtle differences in how to setup the MMU. This resulted in code which compiled but didn't work until debugged by a human who was paying close attention to the architecture documentation as they went.

One of the jobs LLM's are very well suited for is writing code that matches an existing template. For example if you are mechanically transforming a bunch of enums into a function to convert them to strings you need only do a few examples before there is enough context for the LLM to reliably figure out what you are doing. LLM's are a lot more powerful than a simple template expansion because you don't need to explicitly define a template first. The same is true of tasks like generating test fixtures for your code.

There is a potential trap however with using LLMs to write code. As there is no source code and the proprietary models are fairly cagey about exactly what data the models where trained on there are worries about them committing copyright infringement. There are active debates ongoing in the open source community (e.g. on qemu-devel) about the potential ramifications of a model regurgitating its training data. Without clarity on what license that data has there is a risk of contaminating projects with code of an unknown province. While I'm sure these issues will be resolved in time it's certainly a problem you need to be cognisant off.

Prose

Writing prose is a much more natural problem territory for LLM's and an area where low-effort text generation will be rapidly replaced by generative models like ChatGPT. "My" previous blog post was mostly written by a ChatGPT based on a simple brief and a few requests for rewrites in a chat session. While it made the process fairly quick the result comes across as a little bland and "off". I find there is a tendency for LLM's to fall back on fairly obvious generalisations and erase any unique authorial voice there may have been.

However if you give enough structure its very easy to get an LLM to expand on a bullet list into more flowery prose. They are more powerful when being fed a large piece of text and asked to summarise key information in a more accessible way.

They are certainly an easy way to give a first pass review of your writing although I try to re-phrase things myself rather than accept suggestions verbatim to keep my voice coming through the text.

Final Thoughts

The recent advances in LLM's and the public's exposure to popular tools like ChatGPT have certainly propelled the topic of AI in the zeitgeist. While we are almost certainly approaching the "Peak of Inflated Expectations" stage of the hype cycle they will undoubtedly be an important step on the road to the eventual goal of Artificial General Intelligence (AGI). We are still a long way from being able to ask computers to solve complex problems they way they can in for example in Star Trek. However in their current form they will certainly have a big impact on the way we work over the next decade or so.

It's important as a society we learn about how they are built, what their limitations are and understand the computational cost and resultant impact on the environment. It will be awhile before I'd want to trust a set of magic numbers over a carefully developed algorithm to actuate the control surfaces on a plane I'm flying on. However they are already well placed to help us learn new information through interactive questioning and summarising random information on the internet. We must learn to recognise when we've gone down hallucinatory rabbit hole and verify what we've learned with reference to trusted sources.

The Open Mainframe Project has launched a new forum dedicated to Linux on Z. It can be found here, and is intended to complement existing facilities like the mailing lists hosted at Maris college. Any topic around Linux on Z, including virtualization as provided by z/VM and KVM, is fair game, and you may use it to ask quesitions, share useful hints and tips, or simply have a casual conversation about some aspect of the platform!

Both, Red Hat Enterprise Linux 8.9 and 9.1 are out! See the press release here, and Red Hat's blog entry here!

Both releases ship

• s390-tools v2.27 (renamed to s390utils)
• smc-tools v1.8.2
• openCryptoki v3.21

Further information can be found in the release notes for RHEL 8.9 and see RHEL 9.3.

The physical addess space is where all memory and most IO resources are located. PCI memory bars, PCI MMIO bars, platform devices like lapic, io-apic, hpet, tpm, ...

On your linux machine you can use lscpu to see the size of the physical address space:

$ lscpu
Architecture:            x86_64
  CPU op-mode(s):        32-bit, 64-bit
  Address sizes:         39 bits physical, 48 bits virtual
                         ^^^^^^^^^^^^^^^^
[ ... ]

In /proc/iomem you can see how the address space is used. Note that the actual addresses are only shown to root.

The physical address space problem on x86_64

The very first x86_64 processor (AMD Opteron) shipped with a physical address space of 40 bits (aka one TeraByte). So when qemu added support for the (back then) new architecture the qemu vcpu likewise got 40 bits of physical address space, probably assuming that this would be a safe baseline. It is still the default in qemu (version 8.1 as of today) for backward compatibility reasons.

Enter Intel. The first 64-bit processors shipped by Intel featured only 36 bits of physical address space. More recent Intel processors have 39, 42 or more physical address bits. Problem is this limit applies not only to the real physical addess space, but also to Extended Page Tables (EPT). Which means the physical address space of virtual machines is limited too.

So, the problem is the virtual machine firmware does not know how much physical address space it actually has. When checking CPUID it gets back 40 bits, but it could very well be it actually has only 36 bits.

Traditional firmware behavior

To address that problem the virtual machine firmware was very conservative with address space usage, to avoid crossing the unknown limit.

OVMF used to have a MMIO window with fixed size (32GB), which was based on the first multiple of 32GB after normal RAM. So a typical, smallish virtual machine had 0 -> 32GB for RAM and 32GB -> 64GB for IO, staying below the limit for 36 bits of physical address space (which equals 64GB).

VMs having more than 30GB of RAM will need address space above 32GB for RAM, which pushes the IO window above the 64GB limit. The assumtion that hosts which have enough physical memory to run such big virtual machines also have a physical address space larger than 64GB seems to have worked good enough.

Nevertheless the fixed 32G-sized IO window became increasingly problematic. Memory sizes are growing, not only for main memory, but also for device memory. GPUs have gigabytes of memory these days.

Config options in qemu

Qemu has tree -cpu options to control physical address space advertized to the guest, for quite a while already.

host-phys-bits={on,off}

When enabled qemu will use the hosts physical address bits for the guest, i.e. the guest can see the actual limit. I recommend enable this everywhere.
Upstream default: off (except for -cpu host where it is on).
Some downstream linux distro builds flip this to on by default.

host-phys-bits-limit=bits

Is used only with host-phys-bits=on. Can be used to reduce the number of physical address space bits communicated to the guest. Useful for live migration compatibility in case your machine cluster has machines with different physical address space sizes.

phys-bits=bits

Is used only with host-phys-bits=off. Can be used to set the number of physical address space bits to any value you want, including non-working values. Use only if you know what you are doing, it's easy to shot yourself into the foot with this one.

Changes in OVMF

Recent OVMF versions (edk2-stable202211 and newer) try to figure the size of the physical address space using a heuristic: In case the physical address space bits value received via CPUID is 40 or below it is checked against known-good values, which are 36 and 39 for Intel processors and 40 for AMD processors. If that check passes or the number of bits is 41 or higher OVMF assumes qemu is configured with host-phys-bits=on and the value can be trusted.

In case there is no trustworthy phys-bits value OVMF will continue with the traditional behavior described above.

In case OVMF trusts the phys-bits value it will apply some OVMF-specific limitations before actually using it:

• The concept if virtual memory does not exist in UEFI, so the firmware will identity-map everything. Without 5-level paging (which is not yet supported in OVMF) at most 128TB (phys-bits=47) can be identity-mapped, so OVMF can not use more than that.
  The actual limit is phys-bits=46 (64TB) for now due to older linux kernels (4.15) having problems if OVMF uses phys-bits=47.
• In case gigabyte pages are not available OVMF will not use more than phys-bits=40 (1TB). This avoids high memory usage and long boot times due to OVMF creating lots of page tables for the identity mapping.

The final phys-bits value will be used to calculate the size of the physical address space available. The 64-bit IO window will be placed as high as possibe, i.e. at the end of the physical address space. The size of the IO window and also the size of the PCI bridge windows (for prefetchable 64-bit bars) will be scaled up with the physical address space, i.e. on machines with a larger physical address space you will also get larger IO windows.

Changes in SeaBIOS

Starting with version 1.16.3 SeaBIOS uses a heuristic simliar to OVMF to figure whenever there is a trustworthy phys-bits value.

If that is the case SeaBIOS will enable the 64-bit IO window by default and place it at the end of the address space like OVMF does. SeaBIOS will also scale the size of the IO window with the size of the address space.

Although the overall behavior is simliar there are some noteworthy differences:

• SeaBIOS will not enable the 64-bit IO window in case there is no RAM above 4G, for better compatibility with old -- possibly 32-bit -- guests.
• SeaBIOS will not enable the 64-bit IO window in case the CPU has no support for long mode (i.e. it is a 32-bit processor), likewise for better compatibility with old guests.
• SeaBIOS will limit phys-bits to 46, simliar to OVMF, likewise for better compatibility with old guests. SeaBIOS does not use paging though and does not care about support for gigabyte pages, it will never limit phys-bits to 40.
• SeaBIOS has a list of devices which will never be placed in the 64-bit IO window. This list includes devices where SeaBIOS drivers must be able to access the PCI bars. SeaBIOS runs in 32-bit mode so these PCI bars must be mapped below 4GB.

Changes in qemu

Starting with release 8.2 the firmware images bundled with upstream qemu are new enough to include the OVMF and SeaBIOS changes described above.

Live migration and changes in libvirt

The new firmware behavior triggered a few bugs elsewhere ...

When doing live migration the vcpu configuration on source and target host must be identical. That includes the size of the physical address space.

libvirt can calculate the cpu baseline for a given cluster, i.e. create a vcpu configuration which is compatible with all cluster hosts. That calculation did not include the size of the physical address space though.

With the traditional, very conservative firmware behavior this bug did not cause problems in practice, but with OVMF starting to use the full physical address space live migrations in heterogeneous clusters started to fail because of that.

In libvirt 9.5.0 and newer this has been fixed.

Trouble shooting tips

In general, it is a good idea to set the qemu config option host-phys-bits=on.

In case guests can't deal with PCI bars being mapped at high addresses the host-phys-bits-limit=bits option can be used to limit the address space usage. I'd suggest to stick to values seen in actual processors, so 40 for AMD and 39 for Intel are good candidates.

In case you are running 32-bit guests with alot of memory (which btw isn't a good idea performance-wise) you might need turn off long mode support to force the PCI bars being mapped below 4G. This can be done by simply using qemu-system-i386 instead of qemu-system-x86_64, or by explicitly setting lm=off in the -cpu options.

I am pleased to announce that a new release of the libvirt-glib package, version 5.0.0, is now available from

https://libvirt.org/sources/glib/

The packages are GPG signed with

Key fingerprint: DAF3 A6FD B26B 6291 2D0E 8E3F BE86 EBB4 1510 4FDF (4096R)

Changes in this release:

• Fix compatiblity with libxml2 >= 2.12.0
• Bump min libvirt version to 2.3.0
• Bump min meson to 0.56.0
• Require use of GCC >= 4.8 / CLang > 3.4 / XCode CLang > 5.1
• Mark USB disks as removable by default
• Add support for audio device backend config
• Add support for DBus graphics backend config
• Add support for controlling firmware feature flags
• Improve compiler flag handling in meson
• Extend library version script handling to FreeBSD
• Fix pointer sign issue in capabilities config API
• Fix compat with gnome.mkenums() in Meson 0.60.0
• Avoid compiler warnings from gi-ir-scanner generated code by not setting glib version constraints
• Be more robust about NULL GError parameters
• Disable unimportant cast alignment compiler warnings
• Use ‘pragma once’ in all header files
• Updated translations

Thanks to everyone who contributed to this new release.