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 †1/10 ±
 richards †1/7 ±
 fannkuch-redux1/5 ±
 fib501/4 ±
 thread-ring1/4 ±
 jsonbench1/4 ±
 meteor-contest1/3 ±
 fasta1/3 ±
 iobench †1/3 ±
 pystone1/3 ±
 fibonacci1/2 ±
 binary-trees±±±
 chameneos-redux± ±
 k-nucleotide±?±
 templates † ±
 spectral-norm?
 regex-dna?±
 pidigits †37×?±
 PyPy used what fraction? used how many times more? 
Time-used  |-  |---  25% median  75%  ---|  -|
(Elapsed secs)1/101/101/41/3±37×

† 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 
PyPy7.947.9573,7961337  0% 0% 100% 1% †
Python 275.5275.536,7121337  8% 11% 8% 94% †
 richards 
PyPy0.300.301,5722423  100% 0% 3% 0% †
Python 21.971.976,3242423  16% 43% 100% 34% †
 fannkuch-redux 
PyPy4.201.3479,3841009  75% 91% 72% 77%
Python 228.307.3544,4681009  98% 97% 97% 97%
 fib50 
PyPy0.940.9477,652136  1% 1% 0% 100%
Python 24.044.0537,928136  3% 4% 2% 100%
 thread-ring 
PyPy0.190.19?407  0% 0% 100% 5%
Python 20.770.776,432407  4% 0% 4% 100%
 jsonbench 
PyPy1.781.7879,076322  100% 0% 1% 1%
Python 26.986.9815,396322  2% 3% 1% 100%
 meteor-contest 
PyPy1.101.1082,0961485  0% 100% 0% 2%
Python 23.773.776,6001485  100% 5% 0% 3%
 fasta 
PyPy3.193.1978,120900  0% 100% 0% 0%
Python 210.1110.1112,696900  2% 100% 2% 4%
 iobench 
PyPy16.6616.6683,964370  80% 1% 21% 1% †
Python 244.5144.526,372370  14% 72% 99% 36% †
 pystone 
PyPy0.140.14?2301  100% 0% 0% 0%
Python 20.360.361,4562301  3% 6% 100% 10%
 fibonacci 
PyPy0.380.381,608181  3% 0% 97% 3%
Python 20.780.796,716181  9% 100% 3% 4%
 binary-trees 
PyPy3.061.3286,804743  58% 54% 67% 72%
Python 25.991.6459,252743  91% 98% 91% 92%
 chameneos-redux 
PyPy137.5476.2577,0601192  39% 40% 37% 37%
Python 2166.7992.188,4121192  35% 30% 48% 50%
 k-nucleotide 
PyPy0.150.15?593  100% 0% 0% 0%
Python 20.120.12?593  100% 17% 0% 0%
 templates 
PyPy3.483.4891,356307  2% 100% 1% 1% †
Python 21.611.6216,812307  89% 11% 17% 5% †
 spectral-norm 
PyPy0.180.18?594  5% 100% 0% 0%
Python 20.080.08?394  0% 0% 100% 0%
 regex-dna 
PyPy0.370.341,576612  12% 15% 12% 76%
Python 20.090.14?612  8% 8% 31% 23%
 pidigits 
PyPy0.860.8687,824378  9% 99% 0% 1% †
Python 20.020.02?378  0% 33% 100% 0% †
 mandelbrot 
No program
Python 2101.2628.56166,3123832  99% 96% 97% 99%
 fasta-redux 
PyPy0.360.361,6041115  3% 0% 28% 72%
No program
 reverse-complement
   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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