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* Update `things you can do` message using cmake commandsGravatar Nicolas Mellado2020-06-16
| | | | Print cmake commands instead of make commands, which should work for any generator.
* Run two independent chains, when reducing tensors.Gravatar Ilya Tokar2020-06-16
| | | | | | | | | | | | | | | | | | | | | | | | | | Running two chains exposes more instruction level parallelism, by allowing to execute both chains at the same time. Results are a bit noisy, but for medium length we almost hit theoretical upper bound of 2x. BM_fullReduction_16T/3 [using 16 threads] 17.3ns ±11% 17.4ns ± 9% ~ (p=0.178 n=18+19) BM_fullReduction_16T/4 [using 16 threads] 17.6ns ±17% 17.0ns ±18% ~ (p=0.835 n=20+19) BM_fullReduction_16T/7 [using 16 threads] 18.9ns ±12% 18.2ns ±10% ~ (p=0.756 n=20+18) BM_fullReduction_16T/8 [using 16 threads] 19.8ns ±13% 19.4ns ±21% ~ (p=0.512 n=20+20) BM_fullReduction_16T/10 [using 16 threads] 23.5ns ±15% 20.8ns ±24% -11.37% (p=0.000 n=20+19) BM_fullReduction_16T/15 [using 16 threads] 35.8ns ±21% 26.9ns ±17% -24.76% (p=0.000 n=20+19) BM_fullReduction_16T/16 [using 16 threads] 38.7ns ±22% 27.7ns ±18% -28.40% (p=0.000 n=20+19) BM_fullReduction_16T/31 [using 16 threads] 146ns ±17% 74ns ±11% -49.05% (p=0.000 n=20+18) BM_fullReduction_16T/32 [using 16 threads] 154ns ±19% 84ns ±30% -45.79% (p=0.000 n=20+19) BM_fullReduction_16T/64 [using 16 threads] 603ns ± 8% 308ns ±12% -48.94% (p=0.000 n=17+17) BM_fullReduction_16T/128 [using 16 threads] 2.44µs ±13% 1.22µs ± 1% -50.29% (p=0.000 n=17+17) BM_fullReduction_16T/256 [using 16 threads] 9.84µs ±14% 5.13µs ±30% -47.82% (p=0.000 n=19+19) BM_fullReduction_16T/512 [using 16 threads] 78.0µs ± 9% 56.1µs ±17% -28.02% (p=0.000 n=18+20) BM_fullReduction_16T/1k [using 16 threads] 325µs ± 5% 263µs ± 4% -19.00% (p=0.000 n=20+16) BM_fullReduction_16T/2k [using 16 threads] 1.09ms ± 3% 0.99ms ± 1% -9.04% (p=0.000 n=20+20) BM_fullReduction_16T/4k [using 16 threads] 7.66ms ± 3% 7.57ms ± 3% -1.24% (p=0.017 n=20+20) BM_fullReduction_16T/10k [using 16 threads] 65.3ms ± 4% 65.0ms ± 3% ~ (p=0.718 n=20+20)
* Fix pscatter and pgather for Altivec Complex doubleGravatar Pedro Caldeira2020-06-16
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* Fix unused variable warning on ArmGravatar David Tellenbach2020-06-15
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* Fix #1818: SparseLU: add methods nnzL() and nnzU()Gravatar Sebastien Boisvert2020-06-11
| | | | | | Now this compiles without errors: $ clang++ -I ../../ test_sparseLU.cpp -std=c++03
* Fix #1911: add benchmark for move semantics with fixed-size matrixGravatar Sebastien Boisvert2020-06-11
| | | | | | | | | | | $ clang++ -O3 bench/bench_move_semantics.cpp -I. -std=c++11 \ -o bench_move_semantics $ ./bench_move_semantics float copy semantics: 1755.97 ms float move semantics: 55.063 ms double copy semantics: 2457.65 ms double move semantics: 55.034 ms
* Remove HasCast and fix packetmath cast tests.Gravatar Antonio Sanchez2020-06-11
| | | | | | | | | | | The use of the `packet_traits<>::HasCast` field is currently inconsistent with `type_casting_traits<>`, and is unused apart from within `test/packetmath.cpp`. In addition, those packetmath cast tests do not currently reflect how casts are performed in practice: they ignore the `SrcCoeffRatio` and `TgtCoeffRatio` fields, assuming a 1:1 ratio. Here we remove the unsed `HasCast`, and modify the packet cast tests to better reflect their usage.
* Fix #1757: remove the word 'suicide'Gravatar Sebastien Boisvert2020-06-11
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* Implement scalar_cmp_with_cast_opGravatar ShengYang12020-06-09
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* Fix static analyzer warning in SelfadjointProduct.h.Gravatar Rasmus Munk Larsen2020-06-08
| | | | Fix compiler warnings in GeneralBlockPanelKernel.h.
* Update FindComputeCpp.cmake to fix build problems on WindowsGravatar Thales Sabino2020-06-05
| | | | | - Use standard types in SYCL/PacketMath.h to avoid compilation problems on Windows - Add EIGEN_HAS_CONSTEXPR to cxx11_tensor_argmax_sycl.cpp to fix build problems on Windows
* Revert ".gitlab-ci.yml: initial commit"Gravatar David Tellenbach2020-06-05
| | | | | This reverts commit 95177362edc9c814a102c8a2236695c632892232 to disable GitLab CI temporarily.
* Fix broken packetmath test for logistic on Arm.Gravatar Rasmus Munk Larsen2020-06-04
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* Fix typo in previous update to generic predux_any.Gravatar Rasmus Munk Larsen2020-06-04
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* Avoid implicit float equality comparison in generic predux_any, but use ↵Gravatar Rasmus Munk Larsen2020-06-04
| | | | numext::not_equal_strict to avoid breaking builds that compile with -Werror=float-equal.
* Fix compilation error in logistic packet op.Gravatar Rasmus Munk Larsen2020-06-03
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* Update run instructions for benchCholeskyGravatar n0mend2020-06-01
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* Bug #1777: make the scalar and packet path consistent for the logistic ↵Gravatar Gael Guennebaud2020-05-31
| | | | function + respective unit test
* Fix #556: warnings with mingwGravatar Gael Guennebaud2020-05-31
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* Bug #1767: increase required cmake version to 3.5.0Gravatar Gael Guennebaud2020-05-31
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* Fix #1833: compilation issue of "array!=scalar" with c++20Gravatar Gael Guennebaud2020-05-30
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* Save one extra temporary when assigning a sparse product to a row-major ↵Gravatar Gael Guennebaud2020-05-30
| | | | sparse matrix
* .gitlab-ci.yml: initial commitGravatar Christoph Junghans2020-05-29
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* Add support for PacketBlock<Packet8s,4> and PacketBlock<Packet16uc,4> ↵Gravatar Kan Chen2020-05-29
| | | | ptranspose on NEON
* Disable test for 32-bit systems (e.g. ARM, i386)Gravatar Antonio Sánchez2020-05-28
| | | | | | | Both i386 and 32-bit ARM do not define __uint128_t. On most systems, if __uint128_t is defined, then so is the macro __SIZEOF_INT128__. https://stackoverflow.com/questions/18531782/how-to-know-if-uint128-t-is-defined1
* Fix incorrect usage of `if defined(EIGEN_ARCH_PPC)` => `if EIGEN_ARCH_PPC`Gravatar Yong Tang2020-05-28
| | | | | | | | | | | | | | This PR tries to fix an incorrect usage of `if defined(EIGEN_ARCH_PPC)` in `Eigen/Core` header. In `Eigen/src/Core/util/Macros.h`, EIGEN_ARCH_PPC was explicitly defined as either 0 or 1. As a result `if defined(EIGEN_ARCH_PPC)` will always be true. This causes issues when building on non PPC platform and `MatrixProduct.h` is not available. This fix changes `if defined(EIGEN_ARCH_PPC)` => `if EIGEN_ARCH_PPC`. Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
* Fix #1874: it works on both MSVC 2017 and other platforms.Gravatar Kan Chen2020-05-21
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* Add pscatter for Packet16{u}c (int8)Gravatar Pedro Caldeira2020-05-20
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* Guard usage of decltype since it's a C++11 featureGravatar David Tellenbach2020-05-20
| | | | This fixes https://gitlab.com/libeigen/eigen/-/issues/1897
* Add guard around specialization for bool, which is only currently ↵Gravatar Rasmus Munk Larsen2020-05-19
| | | | implemented for SSE.
* - Vectorizing MMA packing.Gravatar Everton Constantino2020-05-19
| | | | | - Optimizing MMA kernel. - Adding PacketBlock store to blas_data_mapper.
* Add newline at the end of StlIterators.h.Gravatar Rasmus Munk Larsen2020-05-15
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* Fix #1874: workaround MSVC 2017 compilation issue.Gravatar Gael Guennebaud2020-05-15
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* Add missing packet ops for bool, and make it pass the same packet op unit ↵Gravatar Rasmus Munk Larsen2020-05-14
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | tests as other arithmetic types. This change also contains a few minor cleanups: 1. Remove packet op pnot, which is not needed for anything other than pcmp_le_or_nan, which can be done in other ways. 2. Remove the "HasInsert" enum, which is no longer needed since we removed the corresponding packet ops. 3. Add faster pselect op for Packet4i when SSE4.1 is supported. Among other things, this makes the fast transposeInPlace() method available for Matrix<bool>. Run on ************** (72 X 2994 MHz CPUs); 2020-05-09T10:51:02.372347913-07:00 CPU: Intel Skylake Xeon with HyperThreading (36 cores) dL1:32KB dL2:1024KB dL3:24MB Benchmark Time(ns) CPU(ns) Iterations ----------------------------------------------------------------------- BM_TransposeInPlace<float>/4 9.77 9.77 71670320 BM_TransposeInPlace<float>/8 21.9 21.9 31929525 BM_TransposeInPlace<float>/16 66.6 66.6 10000000 BM_TransposeInPlace<float>/32 243 243 2879561 BM_TransposeInPlace<float>/59 844 844 829767 BM_TransposeInPlace<float>/64 933 933 750567 BM_TransposeInPlace<float>/128 3944 3945 177405 BM_TransposeInPlace<float>/256 16853 16853 41457 BM_TransposeInPlace<float>/512 204952 204968 3448 BM_TransposeInPlace<float>/1k 1053889 1053861 664 BM_TransposeInPlace<bool>/4 14.4 14.4 48637301 BM_TransposeInPlace<bool>/8 36.0 36.0 19370222 BM_TransposeInPlace<bool>/16 31.5 31.5 22178902 BM_TransposeInPlace<bool>/32 111 111 6272048 BM_TransposeInPlace<bool>/59 626 626 1000000 BM_TransposeInPlace<bool>/64 428 428 1632689 BM_TransposeInPlace<bool>/128 1677 1677 417377 BM_TransposeInPlace<bool>/256 7126 7126 96264 BM_TransposeInPlace<bool>/512 29021 29024 24165 BM_TransposeInPlace<bool>/1k 116321 116330 6068
* Added support for reverse iterators for Vectorwise operations.Gravatar Felipe Attanasio2020-05-14
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* Indexed view should have RowMajorBit when there is staticly a single rowGravatar Christopher Moore2020-05-14
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* Resolve "IndexedView of a vector should allow linear access"Gravatar Christopher Moore2020-05-13
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* Add KLU support to spbenchsolverGravatar Mark Eberlein2020-05-11
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* Altivec template functions to better code reusabilityGravatar Pedro Caldeira2020-05-11
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* Eigen moved the `scanLauncehr` function inside the internal namespace.Gravatar mehdi-goli2020-05-11
| | | | | | | This commit applies the following changes: - Moving the `scamLauncher` specialization inside internal namespace to fix compiler crash on TensorScan for SYCL backend. - Replacing `SYCL/sycl.hpp` to `CL/sycl.hpp` in order to follow SYCL 1.2.1 standard. - minor fixes: commenting out an unused variable to avoid compiler warnings.
* Remove packet ops pinsertfirst and pinsertlast that are only used in a ↵Gravatar Rasmus Munk Larsen2020-05-08
| | | | | | | | | | | | | | | | single place, and can be replaced by other ops when constructing the first/final packet in linspaced_op_impl::packetOp. I cannot measure any performance changes for SSE, AVX, or AVX512. name old time/op new time/op delta BM_LinSpace<float>/1 1.63ns ± 0% 1.63ns ± 0% ~ (p=0.762 n=5+5) BM_LinSpace<float>/8 4.92ns ± 3% 4.89ns ± 3% ~ (p=0.421 n=5+5) BM_LinSpace<float>/64 34.6ns ± 0% 34.6ns ± 0% ~ (p=0.841 n=5+5) BM_LinSpace<float>/512 217ns ± 0% 217ns ± 0% ~ (p=0.421 n=5+5) BM_LinSpace<float>/4k 1.68µs ± 0% 1.68µs ± 0% ~ (p=1.000 n=5+5) BM_LinSpace<float>/32k 13.3µs ± 0% 13.3µs ± 0% ~ (p=0.905 n=5+4) BM_LinSpace<float>/256k 107µs ± 0% 107µs ± 0% ~ (p=0.841 n=5+5) BM_LinSpace<float>/1M 427µs ± 0% 427µs ± 0% ~ (p=0.690 n=5+5)
* Possibility to specify user-defined default cache sizes for GEBP kernelGravatar David Tellenbach2020-05-08
| | | | | | | | | | | | | | Some architectures have no convinient way to determine cache sizes at runtime. Eigen's GEBP kernel falls back to default cache values in this case which might not be correct in all situations. This patch introduces three preprocessor directives `EIGEN_DEFAULT_L1_CACHE_SIZE` `EIGEN_DEFAULT_L2_CACHE_SIZE` `EIGEN_DEFAULT_L3_CACHE_SIZE` to give users the possibility to set these default values explicitly.
* Remove unused packet op "palign".Gravatar Rasmus Munk Larsen2020-05-07
| | | | Clean up a compiler warning in c++03 mode in AVX512/Complex.h.
* Make size odd for transposeInPlace test to make sure we hit the scalar path.Gravatar Rasmus Munk Larsen2020-05-07
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* Remove traits declaring NEON vectorized casts that do not actually have ↵Gravatar Rasmus Munk Larsen2020-05-07
| | | | packet op implementations.
* Add parallelization of TensorScanOp for types without packet ops.Gravatar Rasmus Munk Larsen2020-05-06
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Clean up the code a bit and do a few micro-optimizations to improve performance for small tensors. Benchmark numbers for Tensor<uint32_t>: name old time/op new time/op delta BM_cumSumRowReduction_1T/8 [using 1 threads] 76.5ns ± 0% 61.3ns ± 4% -19.80% (p=0.008 n=5+5) BM_cumSumRowReduction_1T/64 [using 1 threads] 2.47µs ± 1% 2.40µs ± 1% -2.77% (p=0.008 n=5+5) BM_cumSumRowReduction_1T/256 [using 1 threads] 39.8µs ± 0% 39.6µs ± 0% -0.60% (p=0.008 n=5+5) BM_cumSumRowReduction_1T/4k [using 1 threads] 13.9ms ± 0% 13.4ms ± 1% -4.19% (p=0.008 n=5+5) BM_cumSumRowReduction_2T/8 [using 2 threads] 76.8ns ± 0% 59.1ns ± 0% -23.09% (p=0.016 n=5+4) BM_cumSumRowReduction_2T/64 [using 2 threads] 2.47µs ± 1% 2.41µs ± 1% -2.53% (p=0.008 n=5+5) BM_cumSumRowReduction_2T/256 [using 2 threads] 39.8µs ± 0% 34.7µs ± 6% -12.74% (p=0.008 n=5+5) BM_cumSumRowReduction_2T/4k [using 2 threads] 13.8ms ± 1% 7.2ms ± 6% -47.74% (p=0.008 n=5+5) BM_cumSumRowReduction_8T/8 [using 8 threads] 76.4ns ± 0% 61.8ns ± 3% -19.02% (p=0.008 n=5+5) BM_cumSumRowReduction_8T/64 [using 8 threads] 2.47µs ± 1% 2.40µs ± 1% -2.84% (p=0.008 n=5+5) BM_cumSumRowReduction_8T/256 [using 8 threads] 39.8µs ± 0% 28.3µs ±11% -28.75% (p=0.008 n=5+5) BM_cumSumRowReduction_8T/4k [using 8 threads] 13.8ms ± 0% 2.7ms ± 5% -80.39% (p=0.008 n=5+5) BM_cumSumColReduction_1T/8 [using 1 threads] 59.1ns ± 0% 80.3ns ± 0% +35.94% (p=0.029 n=4+4) BM_cumSumColReduction_1T/64 [using 1 threads] 3.06µs ± 0% 3.08µs ± 1% ~ (p=0.114 n=4+4) BM_cumSumColReduction_1T/256 [using 1 threads] 175µs ± 0% 176µs ± 0% ~ (p=0.190 n=4+5) BM_cumSumColReduction_1T/4k [using 1 threads] 824ms ± 1% 844ms ± 1% +2.37% (p=0.008 n=5+5) BM_cumSumColReduction_2T/8 [using 2 threads] 59.0ns ± 0% 90.7ns ± 0% +53.74% (p=0.029 n=4+4) BM_cumSumColReduction_2T/64 [using 2 threads] 3.06µs ± 0% 3.10µs ± 0% +1.08% (p=0.016 n=4+5) BM_cumSumColReduction_2T/256 [using 2 threads] 176µs ± 0% 189µs ±18% ~ (p=0.151 n=5+5) BM_cumSumColReduction_2T/4k [using 2 threads] 836ms ± 2% 611ms ±14% -26.92% (p=0.008 n=5+5) BM_cumSumColReduction_8T/8 [using 8 threads] 59.3ns ± 2% 90.6ns ± 0% +52.79% (p=0.008 n=5+5) BM_cumSumColReduction_8T/64 [using 8 threads] 3.07µs ± 0% 3.10µs ± 0% +0.99% (p=0.016 n=5+4) BM_cumSumColReduction_8T/256 [using 8 threads] 176µs ± 0% 80µs ±19% -54.51% (p=0.008 n=5+5) BM_cumSumColReduction_8T/4k [using 8 threads] 827ms ± 2% 180ms ±14% -78.24% (p=0.008 n=5+5)
* Fix accidental copy of loop variable.Gravatar Rasmus Munk Larsen2020-05-05
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* Vectorize and parallelize TensorScanOp.Gravatar Rasmus Munk Larsen2020-05-05
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | TensorScanOp is used in TensorFlow for a number of operations, such as cumulative logexp reduction and cumulative sum and product reductions. The benchmarks numbers below are for cumulative row- and column reductions of NxN matrices. name old time/op new time/op delta BM_cumSumRowReduction_1T/4 [using 1 threads ] 25.1ns ± 1% 35.2ns ± 1% +40.45% BM_cumSumRowReduction_1T/8 [using 1 threads ] 73.4ns ± 0% 82.7ns ± 3% +12.74% BM_cumSumRowReduction_1T/32 [using 1 threads ] 988ns ± 0% 832ns ± 0% -15.77% BM_cumSumRowReduction_1T/64 [using 1 threads ] 4.07µs ± 2% 3.47µs ± 0% -14.70% BM_cumSumRowReduction_1T/128 [using 1 threads ] 18.0µs ± 0% 16.8µs ± 0% -6.58% BM_cumSumRowReduction_1T/512 [using 1 threads ] 287µs ± 0% 281µs ± 0% -2.22% BM_cumSumRowReduction_1T/2k [using 1 threads ] 4.78ms ± 1% 4.78ms ± 2% ~ BM_cumSumRowReduction_1T/10k [using 1 threads ] 117ms ± 1% 117ms ± 1% ~ BM_cumSumRowReduction_8T/4 [using 8 threads ] 25.0ns ± 0% 35.2ns ± 0% +40.82% BM_cumSumRowReduction_8T/8 [using 8 threads ] 77.2ns ±16% 81.3ns ± 0% ~ BM_cumSumRowReduction_8T/32 [using 8 threads ] 988ns ± 0% 833ns ± 0% -15.67% BM_cumSumRowReduction_8T/64 [using 8 threads ] 4.08µs ± 2% 3.47µs ± 0% -14.95% BM_cumSumRowReduction_8T/128 [using 8 threads ] 18.0µs ± 0% 17.3µs ±10% ~ BM_cumSumRowReduction_8T/512 [using 8 threads ] 287µs ± 0% 58µs ± 6% -79.92% BM_cumSumRowReduction_8T/2k [using 8 threads ] 4.79ms ± 1% 0.64ms ± 1% -86.58% BM_cumSumRowReduction_8T/10k [using 8 threads ] 117ms ± 1% 18ms ± 6% -84.50% BM_cumSumColReduction_1T/4 [using 1 threads ] 23.9ns ± 0% 33.4ns ± 1% +39.68% BM_cumSumColReduction_1T/8 [using 1 threads ] 71.6ns ± 1% 49.1ns ± 3% -31.40% BM_cumSumColReduction_1T/32 [using 1 threads ] 973ns ± 0% 165ns ± 2% -83.10% BM_cumSumColReduction_1T/64 [using 1 threads ] 4.06µs ± 1% 0.57µs ± 1% -85.94% BM_cumSumColReduction_1T/128 [using 1 threads ] 33.4µs ± 1% 4.1µs ± 1% -87.67% BM_cumSumColReduction_1T/512 [using 1 threads ] 1.72ms ± 4% 0.21ms ± 5% -87.91% BM_cumSumColReduction_1T/2k [using 1 threads ] 119ms ±53% 11ms ±35% -90.42% BM_cumSumColReduction_1T/10k [using 1 threads ] 1.59s ±67% 0.35s ±49% -77.96% BM_cumSumColReduction_8T/4 [using 8 threads ] 23.8ns ± 0% 33.3ns ± 0% +40.06% BM_cumSumColReduction_8T/8 [using 8 threads ] 71.6ns ± 1% 49.2ns ± 5% -31.33% BM_cumSumColReduction_8T/32 [using 8 threads ] 1.01µs ±12% 0.17µs ± 3% -82.93% BM_cumSumColReduction_8T/64 [using 8 threads ] 4.15µs ± 4% 0.58µs ± 1% -86.09% BM_cumSumColReduction_8T/128 [using 8 threads ] 33.5µs ± 0% 4.1µs ± 4% -87.65% BM_cumSumColReduction_8T/512 [using 8 threads ] 1.71ms ± 3% 0.06ms ±16% -96.21% BM_cumSumColReduction_8T/2k [using 8 threads ] 97.1ms ±14% 3.0ms ±23% -96.88% BM_cumSumColReduction_8T/10k [using 8 threads ] 1.97s ± 8% 0.06s ± 2% -96.74%
* Fix confusing template param name for Stride fwd decl.Gravatar Xiaoxiang Cao2020-04-30
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* Fix the embarrassingly incomplete fix to the embarrassing bug in blocked ↵Gravatar Rasmus Munk Larsen2020-04-29
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