Supercharge performance across the full customer journey by connecting Kroger’s shopper insights with Google’s AI and scale.
Hi, everyone! We've just released Chrome 146 (146.0.76380.164) for Android. It'll become available on Google Play over the next few days.
This release includes stability and performance improvements. You can see a full list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.
The Stable channel has been updated to 146.0.7680.164/165 for Windows/Mac and 146.0.7680.164 for Linux, which will roll out over the coming days/weeks. A full list of changes in this build is available in the Log
Interested in switching release channels? Find out how here. If you find a new issue, please let us know by filing a bug. The community help forum is also a great place to reach out for help or learn about common issues.
Srinivas Sista
In our previous post, we introduced the updated XProf and the Cloud Diagnostics XProf library, which are designed to help developers identify model bottlenecks and optimize memory usage. As machine learning workloads on TPUs continue to grow in complexity—spanning both massive training runs and large-scale inference—developers require even deeper visibility into how their code interacts with the underlying hardware.
Today, we are exploring three advanced capabilities designed to provide "flight recorder" visibility and instruction-level insights: Continuous Profiling Snapshots, the Utilization Viewer, and LLO Bundle Visualization.
Standard profiling often relies on "sampling mode," where users manually trigger high-fidelity traces for short, predefined durations. While effective for general optimization, this traditional approach can miss transient anomalies, intermittent stragglers, or unexpected performance regressions that occur during long-running training jobs.
To address this visibility gap, XProf is introducing Continuous Profiling Snapshots. This feature functions as an "always-on" flight recorder for your TPU workloads.
How it works: Continuous profiling snapshots (Google Colab) operates quietly in the background with minimal system overhead (approximately 7µs per packet CPU overhead). It utilizes a host-side circular buffer of roughly 2GB to seamlessly retain the last ~90 seconds of performance data. This architecture allows developers to snapshot performance data programmatically precisely when an anomaly occurs, bypassing the overhead and unpredictability of traditional one-shot profiling.
Raw performance counters are powerful, but interpreting thousands of raw hardware metrics can be a daunting, time-consuming process. The new Utilization Viewer bridges the gap between raw data streams and actionable optimization strategies.
This tool translates raw performance counter values into easily understandable utilization percentages for specific hardware components, such as the TensorCore (TC), SparseCore (SC), and High Bandwidth Memory (HBM).
From Counters to Insights: Instead of requiring developers to manually analyze a raw list of event counts, the Utilization Viewer automatically derives high-level metrics. For example, it can translate raw bus activity into a clear utilization percentage (e.g., displaying an average MXU bus utilization of 7.3%). This immediate clarity allows you to determine at a glance whether your model is compute-bound or memory-bound.
For advanced users and kernel developers utilizing Pallas, we are now exposing Low-Level Operations (LLO) bundle data. LLO bundles represent the specific machine instructions issued to the TPU's functional units during every clock cycle.
This feature is critical for "Instruction Scheduling" verification—ensuring that the compiler is honoring your programming intentions and correctly re-ordering instructions to maximize hardware performance.
New Visualizations via Trace View Integration: You can now visualize LLO bundles directly within the trace viewer. Through dynamic instrumentation, XProf inserts traces exactly when a bundle executes. This provides exact execution times and block utilization metrics, rather than relying on static compiler estimates.
Why it matters: Accessing this level of granularity enables hyper-specific bottleneck analysis. For instance, developers can now identify idle cycles within the Matrix Multiplication Unit (MXU) pipeline, making it easier to spot and resolve latency between vmatmul and vpop instructions.
Whether you are trying to capture a fleeting performance regression with Continuous Profiling, verifying kernel efficiency with LLO Bundles, or assessing overall hardware saturation with the Utilization Viewer, these new features bring internal-grade Google tooling directly to the open-source community. These tools are engineered to provide the absolute transparency required to optimize high-scale ML workloads.
Get started by checking out the updated resources:
Today we’re announcing two huge improvements for Display & Video 360 bulk tools.
First, we are launching support for Display & Video 360 API, Structured Data Files, and Bid Manager API products in the existing “Google Advertising and Measurement Community” Discord server! To join, just click this invite link and follow the onboarding guide.
Our team will be on this server regularly to interact with the Display & Video 360 community, answering questions and responding to feedback. We’ll also let you know what’s new with our products in the #announce-display-video-360 channel.
Second, we have made a significant update to the Display & Video 360 API and Structured Data Files documentation. The updated site is designed to make it easier to learn how to perform common tasks with Task guides, browse important conceptual information with Concept guides, and review reference material with updated navigation that highlights currently-supported versions and features of the API and Structured Data Files.
If you have feedback on these changes or the content of any of the guides, please click the Send Feedback button at the bottom of any page in the documentation.
If you have any questions or want to discuss this post, you can now reach out to us on our “Google Advertising and Measurement Community” Discord server.
- Trevor Mulchay, Display & Video 360 API Team
Learn more about the evolving threat landscape based on Mandiant's new M-Trends 2026 cybersecurity report.
An overview of how Gemini models bring unmatched value to Google Marketing Platform.
For World Water Day we’re releasing our 2026 Water Stewardship Project Portfolio summary report and sharing details on new partnerships.