gilstats.py
viztracer
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gilstats.py | viztracer | |
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1 | 5 | |
10 | 4,363 | |
- | - | |
0.0 | 7.7 | |
over 3 years ago | 1 day ago | |
Python | Python | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
gilstats.py
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Tracing and visualizing the Python GIL with perf and VizTracer
Note: We also see that pthread_cond_timedwait is called, this is what https://github.com/sumerc/gilstats.py uses for a eBPF tool, in case you are interested in other mutexes.
viztracer
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Ask HN: C/C++ developer wanting to learn efficient Python
* https://github.com/gaogaotiantian/viztracer get a timeline of execution vs call-stack (great to discover what's happening deep inside pandas)
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GCC Profiler Internals
Do not use bad instrumenting profilers. A good modern tracing-based instrumenting profiler provides so much more actionable information and insights into where problems are than a sampling profiler it is ridiculous.
As a example consider viztracer [1] for Python. By using a aggregate visualizer such as a flame graph you can figure out what is taking the most time then you can use a tracing visualizer to figure out the exact call stacks and system execution and state that caused it. Not only that, a tracing visualizer lets you diagnose whole system performance and makes it trivial to identify 1 in 1000 anomalous execution patterns (with a 4k screen a anomalous execution pattern stands out like a 4 pixel dead spot). In addition you also get vastly less biased information for parallel execution and get easy insights into parallel execution slowdowns, interference, contention, and blocking behaviors.
The only advantages highlighted in your video that still apply to a good instrumenting profiler are:
1. Multi-language support.
2. Performance counters (though that is solved by doing manual tracking after you know the hotspots and causes).
3. Overhead (if you are using low sampling frequency). Even then a good tracing instrumentation implementation should only incur low double-digit percent overhead and maybe 100% overhead in truly pathological cases involving only small functions where the majority of the execution time is literally spent in function call overhead.
4. No need for recompilation, but you are already looking to make performance changes and test so you already intend to rebuild frequently to test those experiments. In addition, the relative difference in information is so humongous that this is not even worth contemplating unless it is a hard requirement like evaluating something in the field.
[1] https://github.com/gaogaotiantian/viztracer
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Memray is a memory profiler for Python by Bloomberg
Actually it has explicit support for async task based reporting:
https://github.com/gaogaotiantian/viztracer#async-support
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Tracing and visualizing the Python GIL with perf and VizTracer
Let us run perf on this, similarly to what we did to example0.py. However, we add the argument -k CLOCK_MONOTONIC so that we use the same clock as VizTracer and ask VizTracer to generate a JSON, instead of an HTML file: