On Thu, Apr 28, 2011 at 11:10 PM, Stefan Behnel email@example.com wrote:
M.-A. Lemburg, 28.04.2011 22:23:
Stefan Behnel wrote:
DasIch, 28.04.2011 20:55:
the CPython benchmarks have an extensive set of microbenchmarks in the pybench package
Try not to care too much about pybench. There is some value in it, but some of its microbenchmarks are also tied to CPython's interpreter behaviour. For example, the benchmarks for literals can easily be considered dead code by other Python implementations so that they may end up optimising the benchmarked code away completely, or at least partially. That makes a comparison of the results somewhat pointless.
The point of the micro benchmarks in pybench is to be able to compare them one-by-one, not by looking at the sum of the tests.
If one implementation optimizes away some parts, then the comparison will show this fact very clearly - and that's the whole point.
Taking the sum of the micro benchmarks only has some meaning as very rough indicator of improvement. That's why I wrote pybench: to get a better, more details picture of what's happening, rather than trying to find some way of measuring "average" use.
This "average" is very different depending on where you look: for some applications method calls may be very important, for others, arithmetic operations, and yet others may have more need for fast attribute lookup.
I wasn't talking about "averages" or "sums", and I also wasn't trying to put down pybench in general. As it stands, it makes sense as a benchmark for CPython.
However, I'm arguing that a substantial part of it does not make sense as a benchmark for PyPy and others. With Cython, I couldn't get some of the literal arithmetic benchmarks to run at all. The runner script simply bails out with an error when the benchmarks accidentally run faster than the initial empty loop. I imagine that PyPy would eventually even drop the loop itself, thus leaving nothing to compare. Does that tell us that PyPy is faster than Cython for arithmetic? I don't think it does.
When I see that a benchmark shows that one implementation runs in 100% less time than another, I simply go *shrug* and look for a better benchmark to compare the two.
I second here what Stefan says. This sort of benchmarks might be useful for CPython, but they're not particularly useful for PyPy or for comparisons (or any other implementation which tries harder to optimize stuff away). For example a method call in PyPy would be inlined and completely removed if method is empty, which does not measure method call overhead at all. That's why we settled on medium-to-large examples where it's more of an average of possible scenarios than just one.