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Author SHA1 Message Date
Rasmus Munk Larsen
9b411757ab Add missing packet ops for bool, and make it pass the same packet op unit 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
 2020-05-14 22:39:13 +00:00
Felipe Attanasio
d640276d31 Added support for reverse iterators for Vectorwise operations. 2020-05-14 22:38:20 +00:00
Christopher Moore
fa8fd4b4d5 Indexed view should have RowMajorBit when there is staticly a single row 2020-05-14 22:11:19 +00:00
Christopher Moore
a187ffea28 Resolve "IndexedView of a vector should allow linear access" 2020-05-13 19:24:42 +00:00
Mark Eberlein
ba9d18b938 Add KLU support to spbenchsolver 2020-05-11 21:50:27 +00:00
Pedro Caldeira
5fdc179241 Altivec template functions to better code reusability 2020-05-11 21:04:51 +00:00
mehdi-goli
d3e81db6c5 Eigen moved the scanLauncehr function inside the internal namespace. 
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.
 2020-05-11 16:10:33 +01:00
Rasmus Munk Larsen
c1d944dd91 Remove packet ops pinsertfirst and pinsertlast that are only used in a 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)
 2020-05-08 15:41:50 -07:00
David Tellenbach
5c4e19fbe7 Possibility to specify user-defined default cache sizes for GEBP kernel 
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.
 2020-05-08 12:54:36 +02:00
Rasmus Munk Larsen
225ab040e0 Remove unused packet op "palign". 
Clean up a compiler warning in c++03 mode in AVX512/Complex.h.
 2020-05-07 17:14:26 -07:00
Rasmus Munk Larsen
74ec8e6618 Make size odd for transposeInPlace test to make sure we hit the scalar path. 2020-05-07 17:29:56 +00:00
Rasmus Munk Larsen
49f1aeb60d Remove traits declaring NEON vectorized casts that do not actually have packet op implementations. 2020-05-07 09:49:22 -07:00
Rasmus Munk Larsen
2fd8a5a08f Add parallelization of TensorScanOp for types without packet ops. 
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)
 2020-05-06 14:48:37 -07:00
Rasmus Munk Larsen
0e59f786e1 Fix accidental copy of loop variable. 2020-05-05 21:35:38 +00:00
Rasmus Munk Larsen
7b76c85daf Vectorize and parallelize TensorScanOp. 
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%
 2020-05-05 00:19:43 +00:00
Xiaoxiang Cao
a74a278abd Fix confusing template param name for Stride fwd decl. 2020-04-30 01:43:05 +00:00
Rasmus Munk Larsen
923ee9aba3 Fix the embarrassingly incomplete fix to the embarrassing bug in blocked transpose. 2020-04-29 17:27:36 +00:00
Rasmus Munk Larsen
a32923a439 Fix (embarrassing) bug in blocked transpose. 2020-04-29 17:02:27 +00:00
Rasmus Munk Larsen
1e41406c36 Add missing transpose in cleanup loop. Without it, we trip an assertion in debug mode. 2020-04-29 01:30:51 +00:00
Rasmus Munk Larsen
fbe7916c55 Fix compilation error with Clang on Android: _mm_extract_epi64 fails to compile. 2020-04-29 00:58:41 +00:00
Clément Grégoire
82f54ad144 Fix perf monitoring merge function 2020-04-28 17:02:59 +00:00
Rasmus Munk Larsen
ab773c7e91 Extend support for Packet16b: 
* 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)
 2020-04-28 16:12:47 +00:00
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
Pedro Caldeira
29f0917a43 Add support to vector instructions to Packet16uc and Packet16c 2020-04-27 12:48:08 -03:00
Rasmus Munk Larsen
e80ec24357 Remove unused packet op "preduxp". 2020-04-23 18:17:14 +00:00
René Wagner
0aebe19aca BooleanRedux.h: Add more EIGEN_DEVICE_FUNC qualifiers. 
This enables operator== on Eigen matrices in device code.
 2020-04-23 17:25:08 +02:00
Eugene Zhulenev
3c02fefec5 Add async evaluation support to TensorSlicingOp. 
Device::memcpy is not async-safe and might lead to deadlocks. Always evaluate slice expression in async mode.
 2020-04-22 19:55:01 +00:00
Pedro Caldeira
0c67b855d2 Add Packet8s and Packet8us to support signed/unsigned int16/short Altivec vector operations 2020-04-21 14:52:46 -03:00
Rasmus Munk Larsen
e8f40e4670 Fix bug in ptrue for Packet16b. 2020-04-20 21:45:10 +00:00
Rasmus Munk Larsen
2f6ddaa25c Add partial vectorization for matrices and tensors of bool. This speeds up 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%
 2020-04-20 20:16:28 +00:00
dlazenby
00f6340153 Update PreprocessorDirectives.dox - Added line for the new VectorwiseOp plugin directive (and re-alphabatized the plugin section) 2020-04-17 21:43:37 +00:00
Rasmus Munk Larsen
5ab87d8aba Move eigen_packet_wrapper to GenericPacketMath.h and use it for 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.
 2020-04-15 18:17:19 +00:00
Rasmus Munk Larsen
4aae8ac693 Fix typo in TypeCasting.h 2020-04-14 02:55:51 +00:00
Rasmus Munk Larsen
1d674003b2 Fix big in vectorized casting of 
{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.
 2020-04-14 02:11:06 +00:00
Changming Sun
b1aa07a8d3 Fix a bug in TensorIndexList.h 2020-04-13 18:22:03 +00:00
Christoph Hertzberg
d46d726e9d CommaInitializer wrongfully asserted for 0-sized blocks 
commainitialier unit-test never actually called `test_block_recursion`, which also was not correctly implemented and would have caused too deep template recursion.
 2020-04-13 16:41:20 +02:00
Antonio Sanchez
c854e189e6 Fixed commainitializer test. 
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.
 2020-04-10 13:53:26 -07:00
jangsoopark
39142904cc Resolve C4346 when building eigen on windows 2020-04-08 14:55:39 +09:00
Rasmus Munk Larsen
f0577a2bfd Speed up matrix multiplication for small to medium size matrices by using 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)
 2020-04-07 22:09:51 +00:00
Antonio Sanchez
8e875719b3 Replace norm() with squaredNorm() to address integer overflows 
For random matrices with integer coefficients, many of the tests here lead to
integer overflows. When taking the norm() of a row/column, the squaredNorm()
often overflows to a negative value, leading to domain errors when taking the
sqrt(). This leads to a crash on some systems. By replacing the norm() call by
a squaredNorm(), the values still overflow, but at least there is no domain
error.

Addresses https://gitlab.com/libeigen/eigen/-/issues/1856
 2020-04-07 19:48:28 +00:00
Antonio Sanchez
9dda5eb7d2 Missing struct definition in NumTraits 2020-04-07 09:01:11 -07:00
Akshay Naresh Modi
bcc0e9e15c Add numeric_limits min and max for bool 
This will allow (among other things) computation of argmax and argmin of bool tensors
 2020-04-06 23:38:57 +00:00
Bernardo Bahia Monteiro
54a0a9c9dd
Bugfix: conjugate_gradient did not compile with lazy-evaluated RealScalar 
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
 2020-03-29 19:44:12 -04:00
Rasmus Munk Larsen
4fd5d1477b Fix packetmath test build for AVX. 2020-03-27 17:05:39 +00:00
Rasmus Munk Larsen
393dbd8ee9 Fix bug in 52d54278be 2020-03-27 16:42:18 +00:00
Rasmus Munk Larsen
55c8fe8d0f Fix bug in 52d54278be 2020-03-27 16:41:15 +00:00
Joel Holdsworth
6d2dbfc453 NEON: Fixed MSVC types definitions 2020-03-26 20:19:58 +00:00
Joel Holdsworth
52d54278be Additional NEON packet-math operations 2020-03-26 20:18:19 +00:00
Everton Constantino
deb93ed1bf Adhere to recommended load/store intrinsics for pp64le 2020-03-23 15:18:15 -03:00
Aaron Franke
5c22c7a7de Make file formatting comply with POSIX and Unix standards 
UTF-8, LF, no BOM, and newlines at the end of files
 2020-03-23 18:09:02 +00:00