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Author SHA1 Message Date
Steven Peters
953ca5ba2f Spline.h: fix spelling "spang" -> "span" 2019-02-08 06:23:24 +00:00
Eugene Zhulenev
59998117bb Don't do parallel_pack if we can use thread_local memory in tensor contractions 2019-02-07 09:21:25 -08:00
Gael Guennebaud
013cc3a6b3 Make GEMM fallback to GEMV for runtime vectors. 
This is a more general and simpler version of changeset 4c0fa6ce0f
 2019-02-07 16:24:09 +01:00
Gael Guennebaud
fa2fcb4895 Backed out changeset 4c0fa6ce0f 2019-02-07 16:07:08 +01:00
Gael Guennebaud
b3c4344a68 bug #1676: workaround GCC's bug in c++17 mode. 2019-02-07 15:21:35 +01:00
Rasmus Larsen
3091c03898 Merged in ezhulenev/eigen-01 (pull request PR-581) 
Parallelize tensor contraction only by sharding dimension and use 'thread-local' memory for packing

Approved-by: Rasmus Larsen <rmlarsen@google.com>
Approved-by: Gael Guennebaud <g.gael@free.fr>
 2019-02-05 22:45:20 +00:00
Eugene Zhulenev
8491127082 Do not reduce parallelism too much in contractions with small number of threads 2019-02-04 12:59:33 -08:00
Eugene Zhulenev
eb21bab769 Parallelize tensor contraction only by sharding dimension and use 'thread-local' memory for packing 2019-02-04 10:43:16 -08:00
Eugene Zhulenev
6d0f6265a9 Remove duplicated comment line 2019-02-04 10:30:25 -08:00
Eugene Zhulenev
690b2c45b1 Fix GeneralBlockPanelKernel Android compilation 2019-02-04 10:29:15 -08:00
Gael Guennebaud
871e2e5339 bug #1674: disable GCC's unsafe-math-optimizations in sin/cos vectorization (results are completely wrong otherwise) 2019-02-03 08:54:47 +01:00
Rasmus Larsen
e7b481ea74 Merged in rmlarsen/eigen (pull request PR-578) 
Speed up Eigen matrix*vector and vector*matrix multiplication.

Approved-by: Eugene Zhulenev <ezhulenev@google.com>
 2019-02-02 01:53:44 +00:00
Sameer Agarwal
b55b5c7280 Speed up row-major matrix-vector product on ARM 
The row-major matrix-vector multiplication code uses a threshold to
check if processing 8 rows at a time would thrash the cache.

This change introduces two modifications to this logic.

1. A smaller threshold for ARM and ARM64 devices.

The value of this threshold was determined empirically using a Pixel2
phone, by benchmarking a large number of matrix-vector products in the
range [1..4096]x[1..4096] and measuring performance separately on
small and little cores with frequency pinning.

On big (out-of-order) cores, this change has little to no impact. But
on the small (in-order) cores, the matrix-vector products are up to
700% faster. Especially on large matrices.

The motivation for this change was some internal code at Google which
was using hand-written NEON for implementing similar functionality,
processing the matrix one row at a time, which exhibited substantially
better performance than Eigen.

With the current change, Eigen handily beats that code.

2. Make the logic for choosing number of simultaneous rows apply
unifiormly to 8, 4 and 2 rows instead of just 8 rows.

Since the default threshold for non-ARM devices is essentially
unchanged (32000 -> 32 * 1024), this change has no impact on non-ARM
performance. This was verified by running the same set of benchmarks
on a Xeon desktop.
 2019-02-01 15:23:53 -08:00
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
Gael Guennebaud
7ef879f6bf GEBP: improves pipelining in the 1pX4 path with FMA. 
Prior to this change, a product with a LHS having 8 rows was faster with AVX-only than with AVX+FMA.
With AVX+FMA I measured a speed up of about x1.25 in such cases.
 2019-01-30 23:45:12 +01:00
Gael Guennebaud
de77bf5d6c Fix compilation with ARM64. 2019-01-30 16:48:20 +01:00
Gael Guennebaud
d586686924 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
Gael Guennebaud
eb4c6bb22d Fix conflicts and merge 2019-01-30 15:57:08 +01:00
Gael Guennebaud
e3622a0396 Slightly extend discussions on auto and move the content of the Pit falls wiki page here. 
http://eigen.tuxfamily.org/index.php?title=Pit_Falls
 2019-01-30 13:09:21 +01:00
Gael Guennebaud
df12fae8b8 According to https://gcc.gnu.org/bugzilla/show_bug.cgi?id=89101, the previous GCC issue is fixed in GCC trunk (will be gcc 9). 2019-01-30 11:52:28 +01:00
Gael Guennebaud
3775926bba ARM64 & GEBP: add specialization for double +30% speed up 2019-01-30 11:49:06 +01:00
Gael Guennebaud
be5b0f664a ARM64 & GEBP: Make use of vfmaq_laneq_f32 and workaround GCC's issue in generating good ASM 2019-01-30 11:48:25 +01:00
Christoph Hertzberg
a7779a9b42 Hide some annoying unused variable warnings in g++8.1 2019-01-29 16:48:21 +01:00
Gael Guennebaud
efe02292a6 Add recent gemm related changesets and various cleanups in perf-monitoring 2019-01-29 11:53:47 +01:00
Gael Guennebaud
8a06c699d0 bug #1669: fix PartialPivLU/inverse with zero-sized matrices. 2019-01-29 10:27:13 +01:00
Gael Guennebaud
a2a07e62b9 Fix compilation with c++03 (local class cannot be template arguments), and make SparseMatrix::assignDiagonal truly protected. 2019-01-29 10:10:07 +01:00
Gael Guennebaud
f489f44519 bug #1574: implement "sparse_matrix =,+=,-= diagonal_matrix" with smart insertion strategies of missing diagonal coeffs. 2019-01-28 17:29:50 +01:00
Gael Guennebaud
803fa79767 Move evaluator<SparseCompressedBase>::find(i,j) to a more general and reusable SparseCompressedBase::lower_bound(i,j) functiion 2019-01-28 17:24:44 +01:00
Gael Guennebaud
53560f9186 bug #1672: fix unit test compilation with MSVC by adding overloads of test_is* for long long (and factorize copy/paste code through a macro) 2019-01-28 13:47:28 +01:00
Christoph Hertzberg
c9825b967e Renaming even more I identifiers 2019-01-26 13:22:13 +01:00
Christoph Hertzberg
5a52e35f9a Renaming some more I identifiers 2019-01-26 13:18:21 +01:00
Rasmus Munk Larsen
71429883ee Fix compilation error in NEON GEBP specializaition of madd. 2019-01-25 17:00:21 -08:00
Christoph Hertzberg
934b8a1304 Avoid I as an identifier, since it may clash with the C-header complex.h 2019-01-25 14:54:39 +01:00
Gael Guennebaud
ec8a387972 cleanup 2019-01-24 10:24:45 +01:00
Gael Guennebaud
6908ce2a15 More thoroughly check variadic template ctor of fixed-size vectors 2019-01-24 10:24:28 +01:00
David Tellenbach
237b03b372 PR 574: use variadic template instead of initializer_list to implement fixed-size vector ctor from coefficients. 2019-01-23 00:07:19 +01:00
Christoph Hertzberg
bd6dadcda8 Tell doxygen that cxx11 math is available 2019-01-24 00:14:02 +01:00
Gael Guennebaud
c64d5d3827 Bypass inline asm for non compatible compilers. 2019-01-23 23:43:13 +01:00
Christoph Hertzberg
e16913a45f Fix name of tutorial snippet. 2019-01-23 10:35:06 +01:00
Gael Guennebaud
80f81f9c4b Cleanup SFINAE in Array/Matrix(initializer_list) ctors and minor doc editing. 2019-01-22 17:08:47 +01:00
David Tellenbach
db152b9ee6 PR 572: Add initializer list constructors to Matrix and Array (include unit tests and doc) 
- {1,2,3,4,5,...} for fixed-size vectors only
- {{1,2,3},{4,5,6}} for the general cases
- {{1,2,3,4,5,....}} is allowed for both row and column-vector
 2019-01-21 16:25:57 +01:00
Gael Guennebaud
543529da6a Add more extensive tests of Array ctors, including {} variants 2019-01-22 15:30:50 +01:00
nluehr
92774f0275 Replace host_define.h with cuda_runtime_api.h 2019-01-18 16:10:09 -06:00
Gael Guennebaud
d18f49cbb3 Fix compilation of unit tests with gcc and c++17 2019-01-18 11:12:42 +01:00
Christoph Hertzberg
da0a41b9ce Mask unused-parameter warnings, when building with NDEBUG 2019-01-18 10:41:14 +01:00
Rasmus Munk Larsen
2eccbaf3f7 Add missing logical packet ops for GPU and NEON. 2019-01-17 17:45:08 -08:00
Christoph Hertzberg
d575505d25 After fixing bug #1557, boostmultiprec_7 failed with NumericalIssue instead of NoConvergence (all that matters here is no Success) 2019-01-17 19:14:07 +01:00
Gael Guennebaud
ee3662abc5 Remove some useless const_cast 2019-01-17 18:27:49 +01:00
Gael Guennebaud
0fe6b7d687 Make nestByValue works again (broken since 3.3) and add unit tests. 2019-01-17 18:27:25 +01:00
Gael Guennebaud
4b7cf7ff82 Extend reshaped unit tests and remove useless const_cast 2019-01-17 17:35:32 +01:00