

PyPy: JIT progress, 50% faster baseline than CPython - empone
http://morepypy.blogspot.com/2009/06/jit-progress.html

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silentbicycle
...in long-running loops. I wonder how large the start-up costs are, though?
Granted, non-long-running loops are by definition _not running for long_ , so
it's ok if they're a bit less efficient, but 2x faster in hotpots is still
different from 2x faster overall.

FWIW, LuaJIT runs everything 2-4x faster for me, and more so with long-running
loops. Lua's semantics are simpler, though, so it's easier to determine which
optimizations are applicable when. (The only downsides for LuaJIT are that
it's only for i386, and that it uses about a third more space.)

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amix
50% faster on a long running for loop is not the same as 50% faster baseline
from CPython... At least from my knowledge PyPy's RPython is not faster than
CPython.

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jnoller
50% faster... on selected benchmarks. Run pypy through the unladen-swallow
performance benchmarks (very real-world tests) and get some more accurate
numbers.

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vegai
Why do people always benchmark JVM-ported languages to be lot faster than the
main version, but in actual production use they turn out to be much slower?

