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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.
* Remove traits declaring NEON vectorized casts that do not actually have ↵Gravatar Rasmus Munk Larsen2020-05-07
| | | | packet op implementations.
* 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
| | | | transpose.
* Fix (embarrassing) bug in blocked transpose.Gravatar Rasmus Munk Larsen2020-04-29
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* Add missing transpose in cleanup loop. Without it, we trip an assertion in ↵Gravatar Rasmus Munk Larsen2020-04-29
| | | | debug mode.
* Fix compilation error with Clang on Android: _mm_extract_epi64 fails to compile.Gravatar Rasmus Munk Larsen2020-04-29
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* Extend support for Packet16b:Gravatar Rasmus Munk Larsen2020-04-28
| | | | | | | | | | | | | | | | | * Add ptranspose<*,4> to support matmul and add unit test for Matrix<bool> * Matrix<bool> * work around a bug in slicing of Tensor<bool>. * Add tensor tests This speeds up matmul for boolean matrices by about 10x name old time/op new time/op delta BM_MatMul<bool>/8 267ns ± 0% 479ns ± 0% +79.25% (p=0.008 n=5+5) BM_MatMul<bool>/32 6.42µs ± 0% 0.87µs ± 0% -86.50% (p=0.008 n=5+5) BM_MatMul<bool>/64 43.3µs ± 0% 5.9µs ± 0% -86.42% (p=0.008 n=5+5) BM_MatMul<bool>/128 315µs ± 0% 44µs ± 0% -85.98% (p=0.008 n=5+5) BM_MatMul<bool>/256 2.41ms ± 0% 0.34ms ± 0% -85.68% (p=0.008 n=5+5) BM_MatMul<bool>/512 18.8ms ± 0% 2.7ms ± 0% -85.53% (p=0.008 n=5+5) BM_MatMul<bool>/1k 149ms ± 0% 22ms ± 0% -85.40% (p=0.008 n=5+5)
* Block transposeInPlace() when the matrix is real and square. This yields a ↵Gravatar Rasmus Munk Larsen2020-04-28
| | | | | | | | | | | | | | | | | | | | 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)
* Add support to vector instructions to Packet16uc and Packet16cGravatar Pedro Caldeira2020-04-27
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* Remove unused packet op "preduxp".Gravatar Rasmus Munk Larsen2020-04-23
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* BooleanRedux.h: Add more EIGEN_DEVICE_FUNC qualifiers.Gravatar René Wagner2020-04-23
| | | | This enables operator== on Eigen matrices in device code.
* Add Packet8s and Packet8us to support signed/unsigned int16/short Altivec ↵Gravatar Pedro Caldeira2020-04-21
| | | | vector operations
* Fix bug in ptrue for Packet16b.Gravatar Rasmus Munk Larsen2020-04-20
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* Add partial vectorization for matrices and tensors of bool. This speeds up ↵Gravatar Rasmus Munk Larsen2020-04-20
| | | | | | | | | | | | | | | | | | | | | | | | | boolean operations on Tensors by up to 25x. Benchmark numbers for the logical and of two NxN tensors: name old time/op new time/op delta BM_booleanAnd_1T/3 [using 1 threads] 14.6ns ± 0% 14.4ns ± 0% -0.96% BM_booleanAnd_1T/4 [using 1 threads] 20.5ns ±12% 9.0ns ± 0% -56.07% BM_booleanAnd_1T/7 [using 1 threads] 41.7ns ± 0% 10.5ns ± 0% -74.87% BM_booleanAnd_1T/8 [using 1 threads] 52.1ns ± 0% 10.1ns ± 0% -80.59% BM_booleanAnd_1T/10 [using 1 threads] 76.3ns ± 0% 13.8ns ± 0% -81.87% BM_booleanAnd_1T/15 [using 1 threads] 167ns ± 0% 16ns ± 0% -90.45% BM_booleanAnd_1T/16 [using 1 threads] 188ns ± 0% 16ns ± 0% -91.57% BM_booleanAnd_1T/31 [using 1 threads] 667ns ± 0% 34ns ± 0% -94.83% BM_booleanAnd_1T/32 [using 1 threads] 710ns ± 0% 35ns ± 0% -95.01% BM_booleanAnd_1T/64 [using 1 threads] 2.80µs ± 0% 0.11µs ± 0% -95.93% BM_booleanAnd_1T/128 [using 1 threads] 11.2µs ± 0% 0.4µs ± 0% -96.11% BM_booleanAnd_1T/256 [using 1 threads] 44.6µs ± 0% 2.5µs ± 0% -94.31% BM_booleanAnd_1T/512 [using 1 threads] 178µs ± 0% 10µs ± 0% -94.35% BM_booleanAnd_1T/1k [using 1 threads] 717µs ± 0% 78µs ± 1% -89.07% BM_booleanAnd_1T/2k [using 1 threads] 2.87ms ± 0% 0.31ms ± 1% -89.08% BM_booleanAnd_1T/4k [using 1 threads] 11.7ms ± 0% 1.9ms ± 4% -83.55% BM_booleanAnd_1T/10k [using 1 threads] 70.3ms ± 0% 17.2ms ± 4% -75.48%
* Move eigen_packet_wrapper to GenericPacketMath.h and use it for ↵Gravatar Rasmus Munk Larsen2020-04-15
| | | | | | | SSE/AVX/AVX512 as it is already used for NEON. This will allow us to define multiple packet types backed by the same vector type, e.g., __m128i. Use this machanism to define packets for half and clean up the packet op implementations.
* Fix typo in TypeCasting.hGravatar Rasmus Munk Larsen2020-04-14
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* Fix big in vectorized casting ofGravatar Rasmus Munk Larsen2020-04-14
| | | | | | {uint8, int8} -> {int16, uint16, int32, uint32, float} {uint16, int16} -> {int32, uint32, int64, uint64, float} for NEON. These conversions were advertised as vectorized, but not actually implemented.
* CommaInitializer wrongfully asserted for 0-sized blocksGravatar Christoph Hertzberg2020-04-13
| | | | commainitialier unit-test never actually called `test_block_recursion`, which also was not correctly implemented and would have caused too deep template recursion.
* Fixed commainitializer test.Gravatar Antonio Sanchez2020-04-10
| | | | | | The removed `finished()` call was responsible for enforcing that the initializer was provided the correct number of values. Putting it back in to restore previous behavior.
* Speed up matrix multiplication for small to medium size matrices by using ↵Gravatar Rasmus Munk Larsen2020-04-07
| | | | | | | | | | | | | | | | | | | | | | | | | | | | half- or quarter-packet vectorized loads in gemm_pack_rhs if they have size 4, instead of dropping down the the scalar path. 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)
* Missing struct definition in NumTraitsGravatar Antonio Sanchez2020-04-07
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* Add numeric_limits min and max for boolGravatar Akshay Naresh Modi2020-04-06
| | | | This will allow (among other things) computation of argmax and argmin of bool tensors
* Bugfix: conjugate_gradient did not compile with lazy-evaluated RealScalarGravatar Bernardo Bahia Monteiro2020-03-29
| | | | | | | | | | | | | | | | | The error generated by the compiler was: no matching function for call to 'maxi' RealScalar threshold = numext::maxi(tol*tol*rhsNorm2,considerAsZero); The important part in the following notes was: candidate template ignored: deduced conflicting types for parameter 'T'" ('codi::Multiply11<...>' vs. 'codi::ActiveReal<...>') EIGEN_ALWAYS_INLINE T maxi(const T& x, const T& y) I am using CoDiPack to provide the RealScalar type. This bug was introduced in bc000deaa Fix conjugate-gradient for very small rhs
* Fix bug in ↵Gravatar Rasmus Munk Larsen2020-03-27
| | | | https://gitlab.com/libeigen/eigen/-/commit/52d54278beefee8b2f19dcca4fd900916154e174
* NEON: Fixed MSVC types definitionsGravatar Joel Holdsworth2020-03-26
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* Additional NEON packet-math operationsGravatar Joel Holdsworth2020-03-26
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* Adhere to recommended load/store intrinsics for pp64leGravatar Everton Constantino2020-03-23
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* Fixing float32's pround halfway criteria to match STL's criteria.Gravatar Everton Constantino2020-03-21
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* Fixed:Gravatar Alessio M2020-03-21
| | | | | - access violation when initializing 0x0 matrices - exception can be thrown during stack unwind while comma-initializing a matrix if eigen_assert if configured to throw
* Update VectorwiseOp.h to allow Plugins similar to MatrixBase.h or ArrayBase.hGravatar dlazenby2020-03-20
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* Bug https://gitlab.com/libeigen/eigen/-/issues/1415: add missing ↵Gravatar Masaki Murooka2020-03-20
| | | | EIGEN_DEVICE_FUNC to diagonal_product_evaluator_base.
* Remove reference to non-existent unary_op_base class.Gravatar Rasmus Munk Larsen2020-03-19
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* Add missing arguments to numext::absdiff().Gravatar Rasmus Munk Larsen2020-03-19
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* Add absolute_difference coefficient-wise binary Array functionGravatar Joel Holdsworth2020-03-19
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* Reenabling packetmath unsigned tests, adding dummy pabs for relevant unsignedGravatar Everton Constantino2020-03-19
| | | | types.
* Add shift_left<N> and shift_right<N> coefficient-wise unary Array functionsGravatar Joel Holdsworth2020-03-19
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* Implement integer square-root for NEONGravatar Joel Holdsworth2020-03-19
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* Update NullaryFunctors.hGravatar Allan Leal2020-03-16
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* Fixing HIP breakage caused by the recent commit that introduces Packet4h2 as ↵Gravatar Deven Desai2020-03-12
| | | | the Eigen::Half packet type
* NEON: Added int64_t and uint64_t packet mathGravatar Joel Holdsworth2020-03-10
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* NEON: Added int8_t and uint8_t packet mathGravatar Joel Holdsworth2020-03-10
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* NEON: Added int16_t and uint16_t packet mathGravatar Joel Holdsworth2020-03-10
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* NEON: Added uint32_t packet mathGravatar Joel Holdsworth2020-03-10
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* NEON: Implemented half-size vectorsGravatar Joel Holdsworth2020-03-10
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* NEON: Set packet_traits<double> flagsGravatar Joel Holdsworth2020-03-10
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