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October 7, 2020 02:33
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Compare 2x2 matrix multiplication methods in Julia
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using BenchmarkTools. LinearAlgebra | |
function naive_mul!(C,A,B) | |
C[1,1] = A[1,1] * B[1,1] + A[1,2] * B[2,1] | |
C[1,2] = A[1,1] * B[1,2] + A[1,2] * B[2,2] | |
C[2,1] = A[2,1] * B[1,1] + A[2,2] * B[2,1] | |
C[2,2] = A[2,1] * B[1,2] + A[2,2] * B[2,2] | |
return nothing | |
end | |
function strassen_mul!(C,A,B) | |
M1 = (A[1,1] + A[2,2]) * (B[1,1] + B[2,2]) | |
M2 = (A[2,1] + A[2,2]) * B[1,1] | |
M3 = A[1,1] * (B[1,2] - B[2,2]) | |
M4 = A[2,2] * (B[2,1] - B[1,1]) | |
M5 = (A[1,1] + A[1,2]) * B[2,2] | |
M6 = (A[2,1] - A[1,1]) * (B[1,1] + B[1,2]) | |
M7 = (A[1,2] - A[2,2]) * (B[2,1] + B[2,2]) | |
C[1,1] = M1 + M4 - M5 + M7 | |
C[1,2] = M3 + M5 | |
C[2,1] = M2 + M4 | |
C[2,2] = M1 - M2 + M3 + M6 | |
return nothing | |
end | |
A = rand(2,2) | |
B = rand(2,2) | |
C = zeros(2,2) | |
@benchmark mul!($C,$A,$B) | |
# BenchmarkTools.Trial: | |
# memory estimate: 0 bytes | |
# allocs estimate: 0 | |
# -------------- | |
# minimum time: 16.749 ns (0.00% GC) | |
# median time: 17.271 ns (0.00% GC) | |
# mean time: 17.642 ns (0.00% GC) | |
# maximum time: 46.273 ns (0.00% GC) | |
# -------------- | |
# samples: 10000 | |
# evals/sample: 998 | |
@benchmark naive_mul!($C,$A,$B) | |
# BenchmarkTools.Trial: | |
# memory estimate: 0 bytes | |
# allocs estimate: 0 | |
# -------------- | |
# minimum time: 12.518 ns (0.00% GC) | |
# median time: 12.635 ns (0.00% GC) | |
# mean time: 12.886 ns (0.00% GC) | |
# maximum time: 52.074 ns (0.00% GC) | |
# -------------- | |
# samples: 10000 | |
# evals/sample: 999 | |
@benchmark strassen_mul!($C,$A,$B) | |
# BenchmarkTools.Trial: | |
# memory estimate: 0 bytes | |
# allocs estimate: 0 | |
# -------------- | |
# minimum time: 14.810 ns (0.00% GC) | |
# median time: 14.869 ns (0.00% GC) | |
# mean time: 15.177 ns (0.00% GC) | |
# maximum time: 47.266 ns (0.00% GC) | |
# -------------- | |
# samples: 10000 | |
# evals/sample: 998 |
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