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Rasmus Munk Larsen 4c0fa6ce0f Speed up Eigen matrix*vector and vector*matrix multiplication. 
This change speeds up Eigen matrix * vector and vector * matrix multiplication for dynamic matrices when it is known at runtime that one of the factors is a vector.

The benchmarks below test

c.noalias()= n_by_n_matrix * n_by_1_matrix;
c.noalias()= 1_by_n_matrix * n_by_n_matrix;
respectively.

Benchmark measurements:

SSE:
Run on *** (72 X 2992 MHz CPUs); 2019-01-28T17:51:44.452697457-08:00
CPU: Intel Skylake Xeon with HyperThreading (36 cores) dL1:32KB dL2:1024KB dL3:24MB
Benchmark                          Base (ns)  New (ns) Improvement
------------------------------------------------------------------
BM_MatVec/64                            1096       312    +71.5%
BM_MatVec/128                           4581      1464    +68.0%
BM_MatVec/256                          18534      5710    +69.2%
BM_MatVec/512                         118083     24162    +79.5%
BM_MatVec/1k                          704106    173346    +75.4%
BM_MatVec/2k                         3080828    742728    +75.9%
BM_MatVec/4k                        25421512   4530117    +82.2%
BM_VecMat/32                             352       130    +63.1%
BM_VecMat/64                            1213       425    +65.0%
BM_VecMat/128                           4640      1564    +66.3%
BM_VecMat/256                          17902      5884    +67.1%
BM_VecMat/512                          70466     24000    +65.9%
BM_VecMat/1k                          340150    161263    +52.6%
BM_VecMat/2k                         1420590    645576    +54.6%
BM_VecMat/4k                         8083859   4364327    +46.0%

AVX2:
Run on *** (72 X 2993 MHz CPUs); 2019-01-28T17:45:11.508545307-08:00
CPU: Intel Skylake Xeon with HyperThreading (36 cores) dL1:32KB dL2:1024KB dL3:24MB
Benchmark                          Base (ns)  New (ns) Improvement
------------------------------------------------------------------
BM_MatVec/64                             619       120    +80.6%
BM_MatVec/128                           9693       752    +92.2%
BM_MatVec/256                          38356      2773    +92.8%
BM_MatVec/512                          69006     12803    +81.4%
BM_MatVec/1k                          443810    160378    +63.9%
BM_MatVec/2k                         2633553    646594    +75.4%
BM_MatVec/4k                        16211095   4327148    +73.3%
BM_VecMat/64                             925       227    +75.5%
BM_VecMat/128                           3438       830    +75.9%
BM_VecMat/256                          13427      2936    +78.1%
BM_VecMat/512                          53944     12473    +76.9%
BM_VecMat/1k                          302264    157076    +48.0%
BM_VecMat/2k                         1396811    675778    +51.6%
BM_VecMat/4k                         8962246   4459010    +50.2%

AVX512:
Run on *** (72 X 2993 MHz CPUs); 2019-01-28T17:35:17.239329863-08:00
CPU: Intel Skylake Xeon with HyperThreading (36 cores) dL1:32KB dL2:1024KB dL3:24MB
Benchmark                          Base (ns)  New (ns) Improvement
------------------------------------------------------------------
BM_MatVec/64                             401       111    +72.3%
BM_MatVec/128                           1846       513    +72.2%
BM_MatVec/256                          36739      1927    +94.8%
BM_MatVec/512                          54490      9227    +83.1%
BM_MatVec/1k                          487374    161457    +66.9%
BM_MatVec/2k                         2016270    643824    +68.1%
BM_MatVec/4k                        13204300   4077412    +69.1%
BM_VecMat/32                             324       106    +67.3%
BM_VecMat/64                            1034       246    +76.2%
BM_VecMat/128                           3576       802    +77.6%
BM_VecMat/256                          13411      2561    +80.9%
BM_VecMat/512                          58686     10037    +82.9%
BM_VecMat/1k                          320862    163750    +49.0%
BM_VecMat/2k                         1406719    651397    +53.7%
BM_VecMat/4k                         7785179   4124677    +47.0%
Currently watchingStop watching
2019-01-31 14:24:08 -08:00
bench Add recent gemm related changesets and various cleanups in perf-monitoring 2019-01-29 11:53:47 +01:00
blas Fix numerous shadow-warnings for GCC<=4.8 2018-08-28 18:32:39 +02:00
cmake Simplify handling of tests that must fail to compile. 2018-12-12 15:48:36 +01:00
debug MIsc. source and comment typos 2018-03-11 10:01:44 -04:00
demos
doc Slightly extend discussions on auto and move the content of the Pit falls wiki page here. 2019-01-30 13:09:21 +01:00
Eigen Speed up Eigen matrix*vector and vector*matrix multiplication. 2019-01-31 14:24:08 -08:00
failtest PR 572: Add initializer list constructors to Matrix and Array (include unit tests and doc) 2019-01-21 16:25:57 +01:00
lapack Enable "old" CMP0026 policy (not perfect, but better than dozens of warning) 2018-12-08 18:59:51 +01:00
scripts Simplify handling and non-splitted tests and include split_test_helper.h instead of re-generating it. This also allows us to modify it without breaking existing build folder. 2018-07-16 18:55:40 +02:00
test bug #1669: fix PartialPivLU/inverse with zero-sized matrices. 2019-01-29 10:27:13 +01:00
unsupported Workaround lack of support for arbitrary packet-type in Tensor by manually loading half/quarter packets in tensor contraction mapper. 2019-01-30 16:48:01 +01:00
.hgeol
.hgignore ignore all *build* sub directories 2017-12-14 14:22:14 +01:00
CMakeLists.txt Bypass inline asm for non compatible compilers. 2019-01-23 23:43:13 +01:00
COPYING.BSD
COPYING.GPL
COPYING.LGPL
COPYING.MINPACK
COPYING.MPL2
COPYING.README
CTestConfig.cmake Optimize the product of a householder-sequence with the identity, and optimize the evaluation of a HouseholderSequence to a dense matrix using faster blocked product. 2018-07-11 17:16:50 +02:00
CTestCustom.cmake.in Allow to filter out build-error messages 2018-07-24 20:12:49 +02:00
eigen3.pc.in
INSTALL
README.md Add links where to make PRs and report bugs into README.md 2018-04-13 21:05:28 +00:00
signature_of_eigen3_matrix_library

README.md

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 please only use the official repository at https://bitbucket.org/eigen/eigen.

For bug reports and feature requests go to http://eigen.tuxfamily.org/bz.