mirror of
https://github.com/42wim/matterbridge
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| .. | ||
| arith_decl.go | ||
| fermat.go | ||
| fft.go | ||
| LICENSE | ||
| README | ||
| scan.go | ||
This library is a toy proof-of-concept implementation of the well-known Schonhage-Strassen method for multiplying integers. It is not expected to have a real life usecase outside number theory computations, nor is it expected to be used in any production system. If you are using it in your project, you may want to carefully examine the actual requirement or problem you are trying to solve. # Comparison with the standard library and GMP Benchmarking math/big vs. bigfft Number size old ns/op new ns/op delta 1kb 1599 1640 +2.56% 10kb 61533 62170 +1.04% 50kb 833693 831051 -0.32% 100kb 2567995 2693864 +4.90% 1Mb 105237800 28446400 -72.97% 5Mb 1272947000 168554600 -86.76% 10Mb 3834354000 405120200 -89.43% 20Mb 11514488000 845081600 -92.66% 50Mb 49199945000 2893950000 -94.12% 100Mb 147599836000 5921594000 -95.99% Benchmarking GMP vs bigfft Number size GMP ns/op Go ns/op delta 1kb 536 1500 +179.85% 10kb 26669 50777 +90.40% 50kb 252270 658534 +161.04% 100kb 686813 2127534 +209.77% 1Mb 12100000 22391830 +85.06% 5Mb 111731843 133550600 +19.53% 10Mb 212314000 318595800 +50.06% 20Mb 490196000 671512800 +36.99% 50Mb 1280000000 2451476000 +91.52% 100Mb 2673000000 5228991000 +95.62% Benchmarks were run on a Core 2 Quad Q8200 (2.33GHz). FFT is enabled when input numbers are over 200kbits. Scanning large decimal number from strings. (math/big [n^2 complexity] vs bigfft [n^1.6 complexity], Core i5-4590) Digits old ns/op new ns/op delta 1e3 9995 10876 +8.81% 1e4 175356 243806 +39.03% 1e5 9427422 6780545 -28.08% 1e6 1776707489 144867502 -91.85% 2e6 6865499995 346540778 -94.95% 5e6 42641034189 1069878799 -97.49% 10e6 151975273589 2693328580 -98.23%