profiler
2 stories filed under profiler on Beyond Market Intelligence. The newest of them: “Explore How Python 3.15's New Profiler Uncovers Hidden Bottlenecks” and “Building a GPT-2 KV Cache to Measure Real AI Performance Gains”. Python 3.15's new profiler doesn't just show you what's slow, it reveals why. Building a GPT-2 KV cache in PyTorch as a learning project is exactly the kind of hands-on work that reveals where the math meets reality. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every profiler story on Beyond Market Intelligence, newest first.
Explore How Python 3.15's New Profiler Uncovers Hidden Bottlenecks
Python 3.15's new profiler doesn't just show you what's slow, it reveals why. Unlike cProfile, which skims surface-level call counts, this tool dives deeper to expose hidden bottlenecks in memory and execution flow that traditional profilers miss. For analysts already tackling messy data, our guide on building smarter curves without noise complements this performance insight perfectly. Whether optimizing code or cleaning datasets, the path to faster, cleaner work starts with seeing what's actually happening under the hood.
Building a GPT-2 KV Cache to Measure Real AI Performance Gains
Building a GPT-2 KV cache in PyTorch as a learning project is exactly the kind of hands-on work that reveals where the math meets reality. The numbers tell a fascinating story. At context length 1,000, the FLOP estimate suggests a 1,000x reduction with caching, yet actual per-token latency only drops about 6x. That gap is worth exploring. The memory measurements are impressively clean, with requested bytes matching the formula to zero after accounting for a constant cuBLAS workspace offset.