Google also continues to invest heavily in the shared infrastructure of the Open Source project to "keep the lights on", including having thousands of servers endlessly running millions of tests, responding to hundreds of incoming bugs per day, ensuring the important ones get fixed, and constantly investing in code health to keep the whole project maintainable. This work represents hundreds of millions of US dollars in annual investment just for maintenance costs before any new feature, innovation or other business priorities can be addressed.
Sustainable funding of critical open source infrastructure remains a hot industry-wide topic of discussion and over the years we’ve heard from many companies and developers about how critical the Chromium project is to their work. They’ve also shared how they would like to support the continued health of the project, beyond direct engineering support.
Today Google is pleased to announce our partnership with The Linux Foundation and the launch of the Supporters of Chromium-based Browsers. The goal of this initiative is to foster a sustainable environment of open-source contributions towards the health of the Chromium ecosystem and financially support a community of developers who want to contribute to the project, encouraging widespread support and continued technological progress for Chromium embedders.
The Supporters of Chromium-based Browsers fund willbe managed by the Linux Foundation, following their long established practices for open governance, prioritizing transparency, inclusivity, and community-driven development. We’re thrilled to have Meta, Microsoft, and Opera on-board as the initial members to pledge their support.
We welcome this additional investment into Chromium’s commons and we’re looking forward to working with the other members of the Supporters of Chromium-based Browsers to ensure that it meets the needs of the wider Chromium community. At the same time, we remain committed to being the responsible steward of the Chromium project and to the massive investment necessary to keep Chromium working well for the entire web industry.
Once the handshake is confirmed, Server’s Preferred Address allows a server to indicate it would like the client to migrate to a different server IP. Though a QUIC connection is not bound to a single 4-tuple like TCP, this is the only type of migration in RFC9000 where the server can change its address.
So far, only Google’s Media CDN has widely enabled advertising an alternative address, but we expect more servers to adopt it soon. Testing has shown that this migration is successful over 99% of the time in Chrome and reduces average RTT by 40-80%.
Today’s The Fast and the Curious post covers how Chrome achieved best-in-class Speedometer scores on mobile devices, resulting in faster and smoother web experiences for Android users.
Chrome has always been about speed. Whether it's loading pages quickly, running complex web apps smoothly, or delivering a seamless browsing experience, performance is at the heart of our browser. And we're always looking for ways to make Chrome even faster.
Over the last two years, we have been hard at work on a number of performance improvements for Android devices. We're excited to share some of the progress we've made.
Speedometer on Android
One of the key metrics we use to track Chrome's performance is the Speedometer benchmark. This benchmark is developed in collaboration with other major web browser engines and measures how quickly Chrome can complete interactions with web pages, including parsing/rendering HTML or CSS and running JavaScript.
Since the release of Chrome M112, we've seen a significant increase in Speedometer 2.1 scores on Android devices [1]. In fact, on many devices, scores more than doubled, with the newest Snapdragon® 8 Elite Mobile Platform setting new records for Speedometer performance on mobile devices! These huge accomplishments are a testament to the work not only of the Chrome and Android teams, but also our silicon and SoC partners.
Since Chrome M112, Speedometer 2.1 scores have more than doubled on many Android devices. [1]
How Did We Do It?
The improvements resulted from several changes, including:
Build optimizations: We've made a number of changes to the way Chrome is built, which has resulted in faster code execution tuned to modern premium Android devices and SoCs.
V8 and Blink improvements: Many improvements to the JavaScript engine (V8) and the rendering engine (Blink) have further boosted performance.
Scheduling, OS and SoCs: We worked closely with Android partners to optimize the way Chrome interacts with the operating system and its thread scheduling to make the best use of the silicon on the devices.
Let's take a closer look at each of these areas.
Build optimizations
The Android device ecosystem is very diverse. From entry-level phones to the newest premium ones, Chrome needs to run well on all devices. Up until last year, we shipped the same Chrome build to all these different Android devices. The memory and disk size constraints on entry-level devices resulted in Chrome having to prioritize a small binary size. Consequently, many modern build optimizations were out of reach, as they resulted in much larger binaries.
With M113, Chrome was finally able to ship a separate higher-performance build targeting premium Android devices via the Google Play Store. While we still ship a more binary-size-constrained build to other devices, this approach allowed us to land some of those modern optimizations into the new premium build:
By targeting 64-bit Arm instead of 32-bit Arm, we can make use of more efficient Arm instruction set features and larger 64-bit operations.
Since binary size is less relevant on premium devices with large disks and sufficient memory, we can now compile C++ code optimized for speed (-O2 / -O3) rather than size (-Oz).
Furthermore, we tweaked the inlining thresholds used by the compiler to enable more inlining in hot code (within and across modules), while updating the model and policy used by another compiler pass (MLGO) to reduce inlining in cold code.
We now also apply profile-guided optimization (PGO) techniques to the build to further improve the code layout and optimization level for hot code.
Finally, we improved cross-function code ordering by aligning Chrome's orderfile generation with the new 64-bit build. We also now include Speedometer 3, the latest version of the industry-standard browser speed benchmark, in the workloads used to generate the orderfile.
Together, these build optimizations account for more than half of the overall Speedometer score improvements. This progress was facilitated by our collaboration with Arm, who contributed valuable insights and improvements, including to identify and address inefficiencies in Chrome's PGO setup and inlining.
V8 and Blink improvements
Chrome continuously improves the performance of its JavaScript and web rendering engines, V8 and Blink. Most optimizations are small in individual impact, but stacked together, these improvements add up and contributed most of the remaining Speedometer impact! Notable ones include:
V8 launched its Sparkplug compiler tier, a super fast baseline compiler that sits right above its Ignition interpreter and generates non-optimized code very quickly. Later, V8 also launched Maglev, a new mid-tier compiler that generates semi-optimized code. It takes longer to do so than Sparkplug, but much less time than Turbofan, V8's ultra-optimizing compiler tier. All together, this new tiering hierarchy allows V8 to tier up more gradually, improving both performance and power consumption.
We landed many other incremental optimizations, e.g. to V8 and our parsing, style, layout, and text rendering engines.
Scheduling and OS
To achieve the best possible performance, Android partners invest heavily in tuning the operating system's thread scheduling and frequency scaling policies, as well as improving the performance of the Silicon itself.
We worked closely with our partners to improve their tuning for Chrome and Speedometer. In particular, our collaboration with Qualcomm was very fruitful: By combining optimized scheduling policies with improved hardware performance, their newest Snapdragon 8 Elite mobile platform realized a 60-80% improvement in Speedometer 3.0 compared to its predecessor, resulting in class-leading web performance. This collaboration also highlighted important bottlenecks in Chrome's code, such as the need for improved PGO and opportunities in V8.
Speedometer 3.0 on Snapdragon 8 Gen 3 (left) compared to Snapdragon 8 Elite (right), Chrome M131
Why do these improvements matter?
Faster Speedometer scores translate to improvements in real user interactions with web content, such as faster page loads and interactions. Back at M112, loading a Google Docs document on Pixel Tablet took more than 50% longer than it does today -- that's the effect of a doubled Speedometer score!
Chrome M112 vs. M129 on Pixel Tablet, loading a Google Doc (frame count)
[1] Speedometer 3 was released during M122, so results from Speedometer 2.1 are provided for a full picture. Measurements shown in graphs were taken on Pixel Tablet.
Today’s The Fast and the Curious post explores how Chrome achieved the highest score on the new Speedometer 3.0, an upgraded browser benchmarking tool to optimize the performance of Web applications. Try out Chrome today!
Speedometer 3.0 is a recently published benchmark for measuring browser performance that was created as an industry collaboration between companies like Google, Apple, Mozilla, Intel, and Microsoft. This benchmark helped us identify areas in which we could optimize Chrome to deliver a faster browser experience to all our users.
Here’s a closer look at how we further optimized Chrome to achieve the highest score ever Speedometer 3, by carefully tracking its recent performance over time as the updated benchmark was being developed. Since the inception of Speedometer 3 in May 2022, we've driven a 72% increase in Chrome’s Speedometer score - translating into performance gains for our users:
Optimizing workloads
By looking at the workloads in Speedometer and in which functions Chrome was spending the most time, we were able to make targeted optimizations to those functions that each drove an increase in Chrome’s score. For example, the SpaceSplitString function is used heavily to turn space-separated strings such as those in “class=’foo bar’ ” into a list representation. In this function we removed some unnecessary bound checks. When we detect that there are duplicated stylesheets, we dedupe them and reference a single stylesheet instance. We made an optimization to reduce the cost of drawing paths and arcs by tuning memory allocations. When creating form editors we detected some unnecessary processing that occurs when form elements are created. Within querySelector, we were able to detect what selector was commonly used and create a hot-path for that.
We previously shared how we optimized innerHTML using specialized fast paths for parsing, an implementation that also made its way into WebKit. Some workloads in Speedometer 3 use DOMParser so we extended the same optimization for another 1% gain.
We worked with the Harfbuzz maintainer to also optimize how Chrome renders AAT fonts such as those used by Apple Mac OS system fonts. Text starts as a processed stream of unicode characters that is then transformed into a glyph stream that is then run through a state machine defined in the AAT font. The optimization allows us to determine more quickly whether glyphs actually participate in the rules for the state machine, leading to speed-ups when processing text using AAT.
Picking the right code to focus on
An important strategy for achieving high performance is tiering up code, which is picking the right code to further optimize within the engine. Intel contributed profile guided tiering to V8 that remembers tiering decisions from the past such that if a function was stably tiered up in the past, we eagerly tier it up on future runs.
Improving garbage collection
Another area of changes that drove around 3% progression on Speedometer 3 was improvements around garbage collection. V8’s garbage collector has a long history of making use of renderer idle time to avoid interfering with actual application code. The recent changes follow this spirit by extending existing mechanisms to prefer garbage collection in idle time on otherwise very active renderers where possible. Specifically, DOM finalization code that is run on reclaiming objects is now also run in idle time. Previously, such operations would compete with regular application code over CPU resources. In addition, V8 now supports a much more compact layout for objects that wrap DOM elements, i.e., all objects that are exposed to JavaScript frameworks. The compact layout reduces memory pressure and results in less time spent on garbage collection.
Posted by Thomas Nattestad, Chrome Product Manager
On the Chrome team, we believe it’s not sufficient to be fast most of the time, we have to be fast all of the time. Today’s The Fast and the Curious post explores how we contributed to Core Web Vitals by surveying the field data of Chrome responding to user interactions across all websites, ultimately improving performance of the web.
As billions of people turn to the web to get things done every day, the browser becomes more responsible for hosting a multitude of apps at once, resource contention becomes a challenge. The multi-process Chrome browser contends for multiple resources: CPU and memory of course, but also its own queues of work between its internal services (in this article, the network service).
This is why we’ve been focused on identifying and fixing slow interactions from Chrome users’ field data, which is the authoritative source when it comes to real user experiences. We gather this field data by recording anonymized Perfetto traces on Chrome Canary, and report them using a privacy-preserving filter.
When looking at field data of slow interactions, one particular cause caught our attention: recurring synchronous calls to fetch the current site’s cookies from the network service.
Let’s dive into some history.
Cookies under an evolving web
Cookies have been part of the web platform since the very beginning. They are commonly created like this:
document.cookie = "user=Alice;color=blue"
And later retrieved like this:
// Assuming a `getCookie` helper method:
getCookie("user", document.cookie)
Its implementation was simple in single-process browsers, which kept the cookie jar in memory.
Over time, browsers became multi-process, and the process hosting the cookie jar became responsible for answering more and more queries. Because the Web Spec requires Javascript to fetch cookies synchronously, however, answering each document.cookie query is a blocking operation.
The operation itself is very fast, so this approach was generally fine, but under heavy load scenarios where multiple websites are requesting cookies (and other resources) from the network service, the queue of requests could get backed up.
We discovered through field traces of slow interactions that some websites were triggering inefficient scenarios with cookies being fetched multiple times in a row. We landed additional metrics to measure how often a GetCookieString() IPC was redundant (same value returned as last time) across all navigations. We were astonished to discover that 87% of cookie accesses were redundant and that, in some cases, this could happen hundreds of times per second.
The simple design of document.cookie was backfiring as JavaScript on the web was using it like a local value when it was really a remote lookup. Was this a classic computer science case of caching?! Not so fast!
The web spec allows collaborating domains to modify each other’s cookies. Hence, a simple cache per renderer process didn’t work, as it would have prevented writes from propagating between such sites (causing stale cookies and, for example, unsynchronized carts in ecommerce applications).
A new paradigm: Shared Memory Versioning
We solved this with a new paradigm which we called Shared Memory Versioning. The idea is that each value of document.cookie is now paired with a monotonically increasing version. Each renderer caches its last read of document.cookie alongside that version. The network service hosts the version of each document.cookie in shared memory. Renderers can thus tell whether they have the latest version without having to send an inter-process query to the network service.
This reduced cookie-related inter-process messages by 80% and made document.cookie accesses 60% faster 🥳.
Hypothesis testing
Improving an algorithm is nice, but what we ultimately care about is whether that improvement results in improving slow interactions for users. In other words, we need to test the hypothesis that stalled cookie queries were a significant cause of slow interactions.
To achieve this, we used Chrome’s A/B testing framework to study the effect and determined that it, combined with other improvements to reduce resource contention, improved the slowest interactions by approximately 5% on all platforms. This further resulted in more websites passing Core Web Vitals 🥳. All of this adds up to a more seamless web for users.
Timeline of the weighted average of the slowest interactions across the web on Chrome as this was released to 1% (Nov), 50% (Dec), and then all users (Feb).
Onward to a seamless web!
By Gabriel Charette, Olivier Li Shing Tat-Dupuis, Carlos Caballero Grolimund, and François Doray, from the Chrome engineering team
