Profiling
Profiling on Beyond Market Intelligence: a running collection of 4 stories we have gathered and hand-picked because they are worth your time. Every post here touches on profiling in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around profiling, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Presentation: Keeping ChatGPT Fast as AI Development Accelerates
As AI development accelerates, maintaining speed and scalability presents a hidden challenge—systemic performance costs beyond simply adding GPUs. In this presentation, Martin Spier of OpenAI reveals how agentic workflows, while boosting code change volume, impact product performance at global scale. He shares how deploying always-on AI agents can automate critical optimization tasks like profiling and regression detection. Discover strategies for continuous performance management—a vital consideration as demonstrated by Cloudflare’s recent introduction of Cloudflare Computer, a runtime designed specifically for AI agents.
Android Studio Quail 2 Redesigns Agent Mode, Streamlines AI-Assisted Coding
Android Studio Quail 2 delivers a significant advancement in AI-assisted coding, now stable and ready for adoption. The expanded Gemini/AI Agent Mode allows for parallel conversations within the IDE, streamlining workflows and reflecting Google’s commitment to AI integration. This release prioritizes developer productivity with enhanced debugging and profiling tools, alongside simplified access to experimental features. Discover how these improvements empower you to build more efficiently – a shift mirrored by the increasing prevalence of AI-generated content, as seen with Deezer’s recent surge in daily uploads.
PyTorch model running 170x slower on T4 vs A100. What could cause a bottleneck this extreme? [D]
A recent report highlights a stark performance disparity: a PyTorch model experienced a 170x slowdown when running on an NVIDIA T4 versus an A100 GPU. This extreme bottleneck, observed with a point-tracking model processing 47 frames at 256x256 resolution, suggests factors beyond typical generational hardware differences. With 99% GPU utilization and pure FP32 precision, potential causes include inefficient 4D correlation volume calculations or transformer layer performance. Further profiling is recommended to pinpoint the specific bottleneck.
Presentation: The Rust High Performance Talk You Did Not Expect
Gain a fresh perspective on high-performance development with Ruth Linehan’s presentation, "The Rust High Performance Talk You Did Not Expect." Linehan details a surprising shift: migrating high-performance caching services from Kotlin to Rust dramatically improved delivery speed and reduced engineering overhead. Discover how Rust's borrow checker and compile-time safety enhance developer workflows and optimize concurrent code—backed by practical examples using Criterion and flamegraphs. For more on modern development tools, explore our article, "Google Cloud Workbench Notebooks Extension Connects VS Code to Google Cloud's Jupyter Notebooks.”