A Guide to Analyzing Python Performance
Introduction Coarse grain timing with time Fine grain timing with a timing context manager Line-by-line timing and execution frequency with a profiler How much memory does it use? IPython shortcuts for line_profiler and memory_profiler Where’s the memory leak? Which objects are the most common? Which objects have been added or deleted? What is referencing this leaky object? Effort vs precision Ref
NumPyPy progress report - running benchmarks
An important thing to notice is the data dependency of the inline version causes a huge slowdown for the C versions. This is not a severe disadvantage for us though -- the brain-dead Python version takes longer and PyPy is not able to take advantage of the knowledge that the data is independent. The results are in the same ballpark as the C versions -- 15% - 170% slower, but the algorithm one choo
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