[pypy-dev] Profiling pypy code
arigo at tunes.org
Sun Apr 1 11:30:43 CEST 2012
On Wed, Mar 28, 2012 at 16:35, Timothy Baldridge <tbaldridge at gmail.com> wrote:
> What should I look into for benchmarking actual times spent
> in each loop?
As a first approximation, getting only the execution counts is enough.
Basically the work flow is: pick one of the most often executed
loops, look into it, and be scared by the amount of cruft left ---
calls to RPython functions, notably. Then try to remove them.
Getting the execution times is not very useful at first, because the
typical execution counts are not flat at all: often, the first few
loops are run 100's or 1000's of times more often than all the others.
At least it is so when looking at the interpreter running small
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