The LRU lock and mmap_sem
The kernel's memory-management subsystem has to manage a great deal of concurrency; that leads to an ongoing series of locking challenges that sometimes seem intractable. Two recurring locking issues — the LRU locks and the mmap_sem lock — were the topic of sessions held during the memory-management track of the 2018 Linux Storage, Filesystem, and Memory-Management Summit. In both cases, it quickl
Spinlocks and Read-Write Locks
Spinlocks and Read-Write Locks Most parallel programming in some way will involve the use of locking at the lowest levels. Locks are primitives that provide mutual exclusion that allow data structures to remain in consistent states. Without locking, multiple threads of execution may simultaneously modify a data structure. Without a carefully thought out (and usually complex) lock-free algorithm, t
Why does one NGINX worker take all the load?
Why does one NGINX worker take all the load?2017-10-23 Scaling up TCP servers is usually straightforward. Most deployments start by using a single process setup. When the need arises more worker processes are added. This is a scalability model for many applications, including HTTP servers like Apache, NGINX or Lighttpd. CC BY-SA 2.0 image by Paul Townsend Increasing the number of worker processes
on mutex performance and WTF::Lock | Nathan's Blog
One of the things I’ve been doing this quarter is removing Gecko’s dependence on NSPR locks. Gecko’s (non-recursive) mutexes and condition variables now use platform-specific constructs, rather than indirecting through NSPR. This change makes things smaller, especially on POSIX platforms, and uses no dynamic memory allocation, so there are fewer untested failure paths. I haven’t rigorously benc
Consistent Hashing with Bounded Loads
Designing algorithms for balanced allocation of clients to servers in dynamic settings is a challenging problem for a variety of reasons. Both servers and clients may be added and/or removed from the system periodically, and the main objectives of allocation algorithms are: the uniformity of the allocation, and the number of moves after adding or removing a server or a client. The most popular sol
Scalability! But at what COST?
Michael Isard, Derek Murray, and I recently sent in a HotOS submission (it’s not blind, so no harm talking about it, we think). The subject is hinted at from the post title (stolen from the paper title): Big data systems may scale well, but this can often be just because they introduce a lot of overhead. Rather than making your computation go faster, the systems introduce substantial overheads whi
Neil J. Gunther - Wikipedia
Neil Gunther (born 15 August 1950) is a computer information systems researcher best known internationally for developing the open-source performance modeling software Pretty Damn Quick and developing the Guerrilla approach to computer capacity planning and performance analysis. He has also been cited for his contributions to the theory of large transients in computer systems and packet networks,
Databases at 14.4Mhz
Failing at scaling When we started building FoundationDB five years ago, my co-founder and I wrote down our big hairy goals for the distributed key-value database we were dreaming up: Fast, strict ACID transactions Even during failure scenarios Even across multiple keys on different nodes Read latencies below 500 microseconds Commit (write) latencies below 2 milliseconds Scalability to 100,000,000
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The Secret to 10 Million Concurrent Connections -The Kernel is the Problem, Not the Solution - High Scalability -
Now that we have the C10K concurrent connection problem licked, how do we level up and support 10 million concurrent connections? Impossible you say. Nope, systems right now are delivering 10 million concurrent connections using techniques that are as radical as they may be unfamiliar. To learn how it’s done we turn to Robert Graham, CEO of Errata Security, and his absolutely fantastic talk at Shm
The Tail at Scale – Communications of the ACM
Software techniques that tolerate latency variability are vital to building responsive large-scale Web services. Systems that respond to user actions quickly (within 100ms) feel more fluid and natural to users than those that take longer.3 Improvements in Internet connectivity and the rise of warehouse-scale computing systems2 have enabled Web services that provide fluid responsiveness while consu
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Applied Performance Theory
Some more details on Read-Log-Update
GitHub - lasp-lang/partisan: High-performance, high-scalability distributed computing for the BEAM.
Userspace RCU
ffwd: delegation is (much) faster than you think
Queueing Theory
Gmane -- Mail To News And Back Again
Service Architectures at Scale: Lessons from Google and eBay
InfiniSQL
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