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Benchmark measurements below are for computing ```c.noalias() = a.transpose() * b;``` for square RowMajor matrices of varying size. Measured improvement with AVX+FMA: name old time/op new time/op delta BM_MatMul_ATB/8 139ns ± 1% 129ns ± 1% -7.49% (p=0.008 n=5+5) BM_MatMul_ATB/32 1.46µs ± 1% 1.22µs ± 0% -16.72% (p=0.008 n=5+5) BM_MatMul_ATB/64 8.43µs ± 1% 7.41µs ± 0% -12.04% (p=0.008 n=5+5) BM_MatMul_ATB/128 56.8µs ± 1% 52.9µs ± 1% -6.83% (p=0.008 n=5+5) BM_MatMul_ATB/256 407µs ± 1% 395µs ± 3% -2.94% (p=0.032 n=5+5) BM_MatMul_ATB/512 3.27ms ± 3% 3.18ms ± 1% ~ (p=0.056 n=5+5) Measured improvement for AVX512: name old time/op new time/op delta BM_MatMul_ATB/8 167ns ± 1% 154ns ± 1% -7.63% (p=0.008 n=5+5) BM_MatMul_ATB/32 1.08µs ± 1% 0.83µs ± 3% -23.58% (p=0.008 n=5+5) BM_MatMul_ATB/64 6.21µs ± 1% 5.06µs ± 1% -18.47% (p=0.008 n=5+5) BM_MatMul_ATB/128 36.1µs ± 2% 31.3µs ± 1% -13.32% (p=0.008 n=5+5) BM_MatMul_ATB/256 263µs ± 2% 242µs ± 2% -7.92% (p=0.008 n=5+5) BM_MatMul_ATB/512 1.95ms ± 2% 1.91ms ± 2% ~ (p=0.095 n=5+5) BM_MatMul_ATB/1k 15.4ms ± 4% 14.8ms ± 2% ~ (p=0.095 n=5+5) |
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.. | ||
src | ||
Cholesky | ||
CholmodSupport | ||
Core | ||
Dense | ||
Eigen | ||
Eigenvalues | ||
Geometry | ||
Householder | ||
IterativeLinearSolvers | ||
Jacobi | ||
KLUSupport | ||
LU | ||
MetisSupport | ||
OrderingMethods | ||
PardisoSupport | ||
PaStiXSupport | ||
QR | ||
QtAlignedMalloc | ||
Sparse | ||
SparseCholesky | ||
SparseCore | ||
SparseLU | ||
SparseQR | ||
SPQRSupport | ||
StdDeque | ||
StdList | ||
StdVector | ||
SuperLUSupport | ||
SVD | ||
UmfPackSupport |