Rasmus Munk Larsen b47c777993 Block transposeInPlace() when the matrix is real and square. This yields a large speedup because we transpose in registers (or L1 if we spill), instead of one packet at a time, which in the worst case makes the code write to the same cache line PacketSize times instead of once.
rmlarsen@rmlarsen4:.../eigen_bench/google3$ benchy --benchmarks=.*TransposeInPlace.*float.* --reference=srcfs experimental/users/rmlarsen/bench:matmul_bench
 10 / 10 [====================================================================================================================================================================================================================] 100.00% 2m50s
(Generated by http://go/benchy. Settings: --runs 5 --benchtime 1s --reference "srcfs" --benchmarks ".*TransposeInPlace.*float.*" experimental/users/rmlarsen/bench:matmul_bench)

name                                       old time/op             new time/op             delta
BM_TransposeInPlace<float>/4               9.84ns ± 0%             6.51ns ± 0%  -33.80%          (p=0.008 n=5+5)
BM_TransposeInPlace<float>/8               23.6ns ± 1%             17.6ns ± 0%  -25.26%          (p=0.016 n=5+4)
BM_TransposeInPlace<float>/16              78.8ns ± 0%             60.3ns ± 0%  -23.50%          (p=0.029 n=4+4)
BM_TransposeInPlace<float>/32               302ns ± 0%              229ns ± 0%  -24.40%          (p=0.008 n=5+5)
BM_TransposeInPlace<float>/59              1.03µs ± 0%             0.84µs ± 1%  -17.87%          (p=0.016 n=5+4)
BM_TransposeInPlace<float>/64              1.20µs ± 0%             0.89µs ± 1%  -25.81%          (p=0.008 n=5+5)
BM_TransposeInPlace<float>/128             8.96µs ± 0%             3.82µs ± 2%  -57.33%          (p=0.008 n=5+5)
BM_TransposeInPlace<float>/256              152µs ± 3%               17µs ± 2%  -89.06%          (p=0.008 n=5+5)
BM_TransposeInPlace<float>/512              837µs ± 1%              208µs ± 0%  -75.15%          (p=0.008 n=5+5)
BM_TransposeInPlace<float>/1k              4.28ms ± 2%             1.08ms ± 2%  -74.72%          (p=0.008 n=5+5)
2020-04-28 16:08:16 +00:00
2018-03-11 10:01:44 -04:00
2020-02-28 20:46:53 +00:00
2012-07-15 10:20:59 -04:00

Eigen is a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms.

For more information go to http://eigen.tuxfamily.org/.

For pull request, bug reports, and feature requests, go to https://gitlab.com/libeigen/eigen.

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