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My journey to stable benchmark, part 2 (deadcode)

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  6. Please enter a valid ZIP Code. It helps to get more reliable numbers. I even wrote a Python script implementing --repeat run perf multiple times, parse the output , before seeing that it was already a builtin feature!

    Valgrind is a great tool known to detect memory leaks, but it also contains gems like the Cachegrind tool which simulates the CPU caches. Sadly, I also failed to see anything obvious in cache misses between the reference python and the patched python. I also tried strace and ltrace tools to try to see a difference in the execution of the reference and the patched pythons.

    I saw different memory addresses, but no major difference which can explain a difference of the timing. Morever, the hotcode simply does not call any syscall nor library function. It's pure CPU-bound code. I used GCC to build to code. I also tried different optimization levels: -O0 , -O1 , -O2 and -O3.

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    I also tried -fomit-pointer omit frame pointer to record the callgraph with perf record. But it turns out that, in some situations, optimizing for space can also produce faster code. I continued to revert changes. A colleague suggested me to remove the body replace it with return; of added function: the code became faster. Ok, now I'm completely lost. To be clear, I don't expect that adding dead code would have any impact on the performance.

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    My email When CPython performance depends on dead code Python build system supports it in a single command: make profile-opt. It profiles the execution of the Python test suite.

    With system tuning and PGO compilation, benchmarks must now be stable this time, no?