[gambit-list] Draft note about some scheme benchmarks
Sven Hartrumpf
hartrumpf at gmx.net
Wed Nov 29 10:31:32 EST 2017
Hi Brad.
You wrote, 2017-11-28 13:41:
[...]
> I started a draft of a note about scheme benchmarking, in the specific
> context of the Gambit benchmarks, Larceny's R6RS benchmarks, and the
> R7RS benchmarks on ecraven's site.
>
> I'm not terribly happy with it as it stands, so I don't know how much
> more I'll develop it, but I'm soliciting comments on the current draft.
Many Scheme implementations perform differently for different ABIs:
x86-64 is often _slower_ than x32 ( https://en.wikipedia.org/wiki/X32_ABI ).
Here are fresh numbers (bigloo 4.3b (alpha), on r7rs-benchmarks,
https://github.com/ecraven/r7rs-benchmarks ; similar findings for Chicken):
(The final real number in each of the two columns is the runtime in seconds.)
x86-64 x32
browse:2000,5.19260288 2000,2.880740096
deriv:10000000,6.965362944 10000000,3.716836864
destruc:600:50:4000,5.549836032 600:50:4000,3.574226176
diviter:1000:1000000,6.392005888 1000:1000000,3.6317312
divrec:1000:1000000,10.001812992 1000:1000000,6.718710016
puzzle:1000,13.80742784 1000,8.90971008
triangl:22:1:50,4.011372032 22:1:50,2.866685952
tak:40:20:11:1,4.730241792 40:20:11:1,3.425227008
takl:40:20:12:1,2.600088064 40:20:12:1,2.011789056
ntakl:40:20:12:1,2.400090112 40:20:12:1,1.674188032
cpstak:40:20:11:1,24.854480896 40:20:11:1,12.031755008
fib:40:5,7.065795072 40:5,6.519035136
fibc:30:10,90.011451136 30:10,74.887549952
fibfp:35.0:10,5.812903936 35.0:10,6.09537408
sum:10000:200000,9.243782912 10000:200000,8.300467968
sumfp:1000000.0:500,11.34256896 1000000.0:500,12.28169088
fft:65536:100,4.25339392 65536:100,4.51265408
mbrot:75:1000,15.3834688 75:1000,15.12131712
nucleic:50,4.15466496 50,4.18435584
pnpoly:1000000,12.179164928 1000000,12.505131008
ray:50,10.29706496 50,8.56330112
simplex:1000000,4.91090176 1000000,5.237882112
ack:3:12:2,5.353505024 3:12:2,5.52949504
array1:1000000:500,4.774448896 1000000:500,4.565778944
string:500000:25,0.112798976 500000:25,0.135549952
sum1:25,0.554072064 25,0.521470976
cat:50,11.41799424 50,7.51307008
tail:25,4.632050944 25,3.492803072
read1:2500,1.66528896 read1:2500,1.621330944
conform:500,2.121995008 500,1.891009024
dynamic:500,3.746084864 500,3.786791168
earley:1,5.485622016 1,4.296999936
graphs:7:3,18.115545088 7:3,15.547826944
matrix:5:5:2500,3.149252096 5:5:2500,2.635922944
maze:20:7:10000,2.244920064 20:7:10000,1.58433792
mazefun:11:11:10000,4.158912 11:11:10000,3.929737984
nqueens:13:10,14.757040128 13:10,13.200544
paraffins:23:10,2.04002816 23:10,1.92710784
parsing:2500,2.118763008 2500,2.19452416
peval:2000,1.818642176 2000,1.665608192
primes:1000:10000,3.791961088 1000:10000,2.637708032
quicksort:10000:2500,4.817225216 10000:2500,4.884603136
scheme:100000,3.935532032 100000,3.266596096
slatex:500,3782.291475968 500,2936.8582912
nboyer:5:1,3.554392832 5:1,2.905526016
sboyer:5:1,1.169430016 5:1,1.055747072
gcbench:20:1,2.098480896 20:1,2.074130176
mperm:20:10:2:1,7.560857856 20:10:2:1,7.810764032
bv2string:1000:1000:100,3.837786112 1000:1000:100,2.59586816
So, ABI might become an interesting aspect of your evaluation.
Ciao
Sven
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