vs

 1 : Are the PyPy programs faster? At a glance.

Each chart bar shows, for one unidentified benchmark, how much the fastest PyPy program used compared to the fastest Python 2 program.

(Memory use is only compared for tasks that require memory to be allocated.)


These are not the only compilers and interpreters. These are not the only programs that could be written. These are not the only tasks that could be solved. These are just 10 tiny examples.

 2 : Are the PyPy programs faster? Approximately.

Each table row shows, for one named benchmark, how much the fastest PyPy program used compared to the fastest Python 2 program.

(Memory use is only compared for tasks that require memory to be allocated.)

 PyPy used what fraction? used how many times more? 
Benchmark Time Memory Code
 n-body †1/8 ±
 richards1/5 ±
 fannkuch-redux1/4 ±
 thread-ring1/4 ±
 jsonbench1/3 ±
 meteor-contest †1/3 ±
 fibonacci1/3 ±
 fasta1/3 ±
 pystone †1/2 ±
 iobench1/2 ±
 binary-trees1/2±
 chameneos-redux± ±
 k-nucleotide±?±
 spectral-norm †?
 templates ±
 regex-dna?±
 reverse-complement †?±
 pidigits †246×?±
 PyPy used what fraction? used how many times more? 
Time-used  |-  |---  25% median  75%  ---|  -|
(Elapsed secs)1/81/81/31/2246×

† possible mismatch - one-core program compared to multi-core program.

± read the measurements and then read the program source code.

 3 : Are the PyPy programs faster? Measurements.

These are not the only tasks that could be solved. These are just 10 tiny examples. These are not the only compilers and interpreters. These are not the only programs that could be written.

For each named benchmark, measurements of the fastest PyPy program are shown for comparison against measurements of the fastest Python 2 program.

Program Source Code CPU secs Elapsed secs Memory KB Code B ≈ CPU Load
 n-body 
PyPy6.596.6376,4081337  100% 5% 5% 6% †
Python 253.1753.336,6921337  30% 28% 64% 20% †
 richards 
PyPy0.240.251,3082423  0% 96% 0% 0%
Python 21.111.127,1002423  100% 0% 0% 3%
 fannkuch-redux 
PyPy2.941.0880,9801009  65% 67% 63% 88%
Python 217.084.6543,6201009  97% 96% 98% 97%
 thread-ring 
PyPy0.140.14?407  0% 0% 0% 100%
Python 20.550.557,060407  100% 4% 0% 0%
 jsonbench 
PyPy1.311.3382,192322  98% 4% 6% 3%
Python 24.564.5717,080322  3% 100% 3% 3%
 meteor-contest 
PyPy0.970.9884,7881485  18% 100% 20% 15% †
Python 22.642.657,1561485  3% 100% 1% 2% †
 fibonacci 
PyPy0.250.261,332181  100% 0% 4% 0%
Python 20.670.677,240181  1% 100% 3% 0%
 fasta 
PyPy2.562.5980,976900  99% 3% 2% 2%
Python 26.646.6513,444900  3% 100% 4% 4%
 pystone 
PyPy0.140.14?2301  100% 60% 57% 8% †
Python 20.230.248522301  0% 96% 4% 0% †
 iobench 
PyPy24.0624.1486,328370  75% 5% 24% 9%
Python 237.3937.426,992370  2% 100% 2% 3%
 binary-trees 
PyPy1.780.79319,260743  52% 71% 66% 51%
Python 23.771.1958,912743  91% 82% 83% 88%
 chameneos-redux 
PyPy131.7266.6875,1561192  52% 50% 53% 52%
Python 2152.7883.777,1001192  44% 45% 42% 39%
 k-nucleotide 
PyPy0.110.11?593  0% 100% 0% 0%
Python 20.080.08?593  100% 0% 0% 11%
 spectral-norm 
PyPy0.190.20?594  11% 18% 0% 100% †
Python 20.080.08?394  0% 13% 0% 89% †
 templates 
PyPy2.832.8696,380307  100% 2% 2% 4%
Python 21.091.0915,876307  4% 4% 2% 100%
 regex-dna 
PyPy0.130.13?501  7% 0% 0% 100%
Python 20.030.03?501  0% 0% 100% 0%
 reverse-complement 
PyPy0.090.09?455  13% 40% 0% 100% †
Python 20.010.01?431  0% 100% 0% 0% †
 pidigits 
PyPy3.403.4587,640322  67% 61% 32% 30% †
Python 20.010.01?380  0% 0% 100% 0% †
 fasta-redux 
PyPy0.230.251,3241115  92% 0% 4% 0%
No program
 mandelbrot 
No program
Python 275.6320.23176,5243832  95% 100% 97% 96%
 fib50
   No programs
 binary-trees-redux
   No programs

† possible mismatch - one-core program compared to multi-core program.

 4 : Are there other PyPy programs for these benchmarks?

Remember - those are just the fastest PyPy and Python 2 programs measured on this OS/machine. Check if there are other implementations of these benchmark programs for PyPy.

Maybe one of those other PyPy programs is fastest on a different OS/machine.

 5 : Are there other faster programs for these benchmarks?

Remember - those are just the fastest PyPy and Python 2 programs measured on this OS/machine. Check if there are faster implementations of these benchmark programs for other programming languages.

Maybe one of those other programs is fastest on a different OS/machine.

 PyPy : the old PyPy 

 

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