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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
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
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
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
Rasmus Munk Larsen
e80ec24357 Remove unused packet op "preduxp". 2020-04-23 18:17:14 +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
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
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
Rasmus Munk Larsen
4fd5d1477b Fix packetmath test build for AVX. 2020-03-27 17:05:39 +00:00
Rasmus Munk Larsen
55c8fe8d0f Fix bug in 52d54278be 2020-03-27 16:41:15 +00:00
Joel Holdsworth
52d54278be Additional NEON packet-math operations 2020-03-26 20:18:19 +00:00
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
Joel Holdsworth
d5c665742b Add absolute_difference coefficient-wise binary Array function 2020-03-19 17:45:20 +00:00
Joel Holdsworth
54aa8fa186 Implement integer square-root for NEON 2020-03-19 17:05:13 +00:00
Joel Holdsworth
88337acae2 test/packetmath: Add tests for all integer types 2020-03-10 22:46:19 +00:00
Joel Holdsworth
9e68977578 test/packetmath: Made negate non-mandatory 2020-03-10 22:46:19 +00:00
Rasmus Munk Larsen
6ac37768a9 Revert "add some static checks for packet-picking logic" 
This reverts commit 7769600245
 2020-02-25 01:07:04 +00:00
Rasmus Munk Larsen
87cfa4862f Revert "Disable test in test/vectorization_logic.cpp, which is currently failing with AVX." 
This reverts commit b625adffd8
 2020-02-25 01:04:56 +00:00
Rasmus Munk Larsen
b625adffd8 Disable test in test/vectorization_logic.cpp, which is currently failing with AVX. 2020-02-24 23:28:25 +00:00
Francesco Mazzoli
7769600245 add some static checks for packet-picking logic 2020-02-07 18:16:16 +01:00
Christoph Hertzberg
1d0c45122a Removing executable bit from file mode 2020-01-11 15:02:29 +01:00
Christoph Hertzberg
35219cea68 Bug #1790: Make areApprox check numext::isnan instead of bitwise equality (NaNs don't have to be bitwise equal). 2020-01-11 14:57:22 +01:00
Srinivas Vasudevan
2e099e8d8f Added special_packetmath test and tweaked bounds on tests. 
Refactor shared packetmath code to header file.
(Squashed from PR !38)
 2020-01-11 10:31:21 +00:00
Christoph Hertzberg
8333e03590 Use data.data() instead of &data (since it is not obvious that Array is trivially copyable) 2020-01-09 11:38:19 +01:00
Ilya Tokar
19876ced76 Bug #1785: Introduce numext::rint. 
This provides a new op that matches std::rint and previous behavior of
pround. Also adds corresponding unsupported/../Tensor op.
Performance is the same as e. g. floor (tested SSE/AVX).
 2020-01-07 21:22:44 +00:00
Everton Constantino
eedb7eeacf Protecting integer_types's long long test with a check to see if we have CXX11 support. 2020-01-07 14:35:35 +00:00
Christoph Hertzberg
870e53c0f2 Bug #1788: Fix rule-of-three violations inside the stable modules. 
This fixes deprecated-copy warnings when compiling with GCC>=9
Also protect some additional Base-constructors from getting called by user code code (#1587)
 2019-12-19 17:30:11 +01:00
Christoph Hertzberg
6965f6de7f Fix unit-test which I broke in previous fix 2019-12-19 13:42:14 +01:00
Christoph Hertzberg
72166d0e6e Fix some maybe-unitialized warnings 2019-12-18 18:26:20 +01:00
Christoph Hertzberg
5a3eaf88ac Workaround class-memaccess warnings on newer GCC versions 2019-12-18 16:37:26 +01:00
Rasmus Munk Larsen
a566074480 Improve accuracy of fast approximate tanh and the logistic functions in Eigen, such that they preserve relative accuracy to within a few ULPs where their function values tend to zero (around x=0 for tanh, and for large negative x for the logistic function). 
This change re-instates the fast rational approximation of the logistic function for float32 in Eigen (removed in 66f07efeae), but uses the more accurate approximation 1/(1+exp(-1)) ~= exp(x) below -9. The exponential is only calculated on the vectorized path if at least one element in the SIMD input vector is less than -9.

This change also contains a few improvements to speed up the original float specialization of logistic:
  - Introduce EIGEN_PREDICT_{FALSE,TRUE} for __builtin_predict and use it to predict that the logistic-only path is most likely (~2-3% speedup for the common case).
  - Carefully set the upper clipping point to the smallest x where the approximation evaluates to exactly 1. This saves the explicit clamping of the output (~7% speedup).

The increased accuracy for tanh comes at a cost of 10-20% depending on instruction set.

The benchmarks below repeated calls

   u = v.logistic()  (u = v.tanh(), respectively)

where u and v are of type Eigen::ArrayXf, have length 8k, and v contains random numbers in [-1,1].

Benchmark numbers for logistic:

Before:
Benchmark                  Time(ns)        CPU(ns)     Iterations
-----------------------------------------------------------------
SSE
BM_eigen_logistic_float        4467           4468         155835  model_time: 4827
AVX
BM_eigen_logistic_float        2347           2347         299135  model_time: 2926
AVX+FMA
BM_eigen_logistic_float        1467           1467         476143  model_time: 2926
AVX512
BM_eigen_logistic_float         805            805         858696  model_time: 1463

After:
Benchmark                  Time(ns)        CPU(ns)     Iterations
-----------------------------------------------------------------
SSE
BM_eigen_logistic_float        2589           2590         270264  model_time: 4827
AVX
BM_eigen_logistic_float        1428           1428         489265  model_time: 2926
AVX+FMA
BM_eigen_logistic_float        1059           1059         662255  model_time: 2926
AVX512
BM_eigen_logistic_float         673            673        1000000  model_time: 1463

Benchmark numbers for tanh:

Before:
Benchmark                  Time(ns)        CPU(ns)     Iterations
-----------------------------------------------------------------
SSE
BM_eigen_tanh_float        2391           2391         292624  model_time: 4242
AVX
BM_eigen_tanh_float        1256           1256         554662  model_time: 2633
AVX+FMA
BM_eigen_tanh_float         823            823         866267  model_time: 1609
AVX512
BM_eigen_tanh_float         443            443        1578999  model_time: 805

After:
Benchmark                  Time(ns)        CPU(ns)     Iterations
-----------------------------------------------------------------
SSE
BM_eigen_tanh_float        2588           2588         273531  model_time: 4242
AVX
BM_eigen_tanh_float        1536           1536         452321  model_time: 2633
AVX+FMA
BM_eigen_tanh_float        1007           1007         694681  model_time: 1609
AVX512
BM_eigen_tanh_float         471            471        1472178  model_time: 805
 2019-12-16 21:33:42 +00:00
Ilya Tokar
06e99aaf40 Bug 1785: fix pround on x86 to use the same rounding mode as std::round. 
This also adds pset1frombits helper to Packet[24]d.
Makes round ~45% slower for SSE: 1.65µs ± 1% before vs 2.45µs ± 2% after,
stil an order of magnitude faster than scalar version: 33.8µs ± 2%.
 2019-12-12 17:38:53 -05:00
Srinivas Vasudevan
88062b7fed Fix implementation of complex expm1. Add tests that fail with previous implementation, but pass with the current one. 2019-12-12 01:56:54 +00:00
Joel Holdsworth
1b6e0395e6 Added io test 2019-12-11 18:22:57 +00:00
Gael Guennebaud
6358599ecb Fix QuaternionBase::cast for quaternion map and wrapper. 2019-12-03 14:51:14 +01:00
Gael Guennebaud
7745f69013 bug #1776: fix vector-wise STL iterator's operator-> using a proxy as pointer type. 
This changeset fixes also the value_type definition.
 2019-12-03 14:40:15 +01:00
Joel Holdsworth
743c925286 test/packetmath: Silence alignment warnings 2019-11-05 19:06:12 +00:00
Hans Johnson
8c8cab1afd STYLE: Convert CMake-language commands to lower case 
Ancient CMake versions required upper-case commands.  Later command names
became case-insensitive.  Now the preferred style is lower-case.
 2019-10-31 11:36:37 -05:00
Hans Johnson
6fb3e5f176 STYLE: Remove CMake-language block-end command arguments 
Ancient versions of CMake required else(), endif(), and similar block
termination commands to have arguments matching the command starting the block.
This is no longer the preferred style.
 2019-10-31 11:36:27 -05:00
Rasmus Munk Larsen
f1e8307308 1. Fix a bug in psqrt and make it return 0 for +inf arguments. 
2. Simplify handling of special cases by taking advantage of the fact that the
   builtin vrsqrt approximation handles negative, zero and +inf arguments correctly.
   This speeds up the SSE and AVX implementations by ~20%.
3. Make the Newton-Raphson formula used for rsqrt more numerically robust:

Before: y = y * (1.5 - x/2 * y^2)
After: y = y * (1.5 - y * (x/2) * y)

Forming y^2 can overflow for very large or very small (denormalized) values of x, while x*y ~= 1. For AVX512, this makes it possible to compute accurate results for denormal inputs down to ~1e-42 in single precision.

4. Add a faster double precision implementation for Knights Landing using the vrsqrt28 instruction and a single Newton-Raphson iteration.

Benchmark results: https://bitbucket.org/snippets/rmlarsen/5LBq9o
 2019-11-15 17:09:46 -08:00
Gael Guennebaud
8af045a287 bug #1774: fix VectorwiseOp::begin()/end() return types regarding constness. 2019-11-14 11:45:52 +01:00
Gael Guennebaud
8496f86f84 Enable CompleteOrthogonalDecomposition::pseudoInverse with non-square fixed-size matrices. 2019-11-13 21:16:53 +01:00
Gael Guennebaud
e7d8ba747c bug #1752: make is_convertible equivalent to the std c++11 equivalent and fallback to std::is_convertible when c++11 is enabled. 2019-10-10 17:41:47 +02:00
Gael Guennebaud
fb557aec5c bug #1752: disable some is_convertible tests for recent compilers. 2019-10-10 11:40:21 +02:00
Gael Guennebaud
36da231a41 Disable an expected warning in unit test 2019-10-08 16:28:14 +02:00
Gael Guennebaud
87427d2eaa PR 719: fix real/imag namespace conflict 2019-10-08 09:15:17 +02:00
Rasmus Larsen
d38e6fbc27 Merged in rmlarsen/eigen (pull request PR-704) 
Add generic PacketMath implementation of the Error Function (erf).
 2019-09-24 23:40:29 +00:00
Christoph Hertzberg
efd9867ff0 bug #1746: Removed implementation of standard copy-constructor and standard copy-assign-operator from PermutationMatrix and Transpositions to allow malloc-less std::move. Added unit-test to rvalue_types 2019-09-24 11:09:58 +02:00
Rasmus Munk Larsen
6de5ed08d8 Add generic PacketMath implementation of the Error Function (erf). 2019-09-19 12:48:30 -07:00
Srinivas Vasudevan
df0816b71f Merging eigen/eigen. 2019-09-16 19:33:29 -04:00
Srinivas Vasudevan
6e215cf109 Add Bessel functions to SpecialFunctions. 
- Split SpecialFunctions files in to a separate BesselFunctions file.

In particular add:
    - Modified bessel functions of the second kind k0, k1, k0e, k1e
    - Bessel functions of the first kind j0, j1
    - Bessel functions of the second kind y0, y1
 2019-09-14 12:16:47 -04:00
Srinivas Vasudevan
facdec5aa7 Add packetized versions of i0e and i1e special functions. 
- In particular refactor the i0e and i1e code so scalar and vectorized path share code.
  - Move chebevl to GenericPacketMathFunctions.


A brief benchmark with building Eigen with FMA, AVX and AVX2 flags

Before:

CPU: Intel Haswell with HyperThreading (6 cores)
Benchmark                  Time(ns)        CPU(ns)     Iterations
-----------------------------------------------------------------
BM_eigen_i0e_double/1            57.3           57.3     10000000
BM_eigen_i0e_double/8           398            398        1748554
BM_eigen_i0e_double/64         3184           3184         218961
BM_eigen_i0e_double/512       25579          25579          27330
BM_eigen_i0e_double/4k       205043         205042           3418
BM_eigen_i0e_double/32k     1646038        1646176            422
BM_eigen_i0e_double/256k   13180959       13182613             53
BM_eigen_i0e_double/1M     52684617       52706132             10
BM_eigen_i0e_float/1             28.4           28.4     24636711
BM_eigen_i0e_float/8             75.7           75.7      9207634
BM_eigen_i0e_float/64           512            512        1000000
BM_eigen_i0e_float/512         4194           4194         166359
BM_eigen_i0e_float/4k         32756          32761          21373
BM_eigen_i0e_float/32k       261133         261153           2678
BM_eigen_i0e_float/256k     2087938        2088231            333
BM_eigen_i0e_float/1M       8380409        8381234             84
BM_eigen_i1e_double/1            56.3           56.3     10000000
BM_eigen_i1e_double/8           397            397        1772376
BM_eigen_i1e_double/64         3114           3115         223881
BM_eigen_i1e_double/512       25358          25361          27761
BM_eigen_i1e_double/4k       203543         203593           3462
BM_eigen_i1e_double/32k     1613649        1613803            428
BM_eigen_i1e_double/256k   12910625       12910374             54
BM_eigen_i1e_double/1M     51723824       51723991             10
BM_eigen_i1e_float/1             28.3           28.3     24683049
BM_eigen_i1e_float/8             74.8           74.9      9366216
BM_eigen_i1e_float/64           505            505        1000000
BM_eigen_i1e_float/512         4068           4068         171690
BM_eigen_i1e_float/4k         31803          31806          21948
BM_eigen_i1e_float/32k       253637         253692           2763
BM_eigen_i1e_float/256k     2019711        2019918            346
BM_eigen_i1e_float/1M       8238681        8238713             86


After:

CPU: Intel Haswell with HyperThreading (6 cores)
Benchmark                  Time(ns)        CPU(ns)     Iterations
-----------------------------------------------------------------
BM_eigen_i0e_double/1            15.8           15.8     44097476
BM_eigen_i0e_double/8            99.3           99.3      7014884
BM_eigen_i0e_double/64          777            777         886612
BM_eigen_i0e_double/512        6180           6181         100000
BM_eigen_i0e_double/4k        48136          48140          14678
BM_eigen_i0e_double/32k      385936         385943           1801
BM_eigen_i0e_double/256k    3293324        3293551            228
BM_eigen_i0e_double/1M     12423600       12424458             57
BM_eigen_i0e_float/1             16.3           16.3     43038042
BM_eigen_i0e_float/8             30.1           30.1     23456931
BM_eigen_i0e_float/64           169            169        4132875
BM_eigen_i0e_float/512         1338           1339         516860
BM_eigen_i0e_float/4k         10191          10191          68513
BM_eigen_i0e_float/32k        81338          81337           8531
BM_eigen_i0e_float/256k      651807         651984           1000
BM_eigen_i0e_float/1M       2633821        2634187            268
BM_eigen_i1e_double/1            16.2           16.2     42352499
BM_eigen_i1e_double/8           110            110        6316524
BM_eigen_i1e_double/64          822            822         851065
BM_eigen_i1e_double/512        6480           6481         100000
BM_eigen_i1e_double/4k        51843          51843          10000
BM_eigen_i1e_double/32k      414854         414852           1680
BM_eigen_i1e_double/256k    3320001        3320568            212
BM_eigen_i1e_double/1M     13442795       13442391             53
BM_eigen_i1e_float/1             17.6           17.6     41025735
BM_eigen_i1e_float/8             35.5           35.5     19597891
BM_eigen_i1e_float/64           240            240        2924237
BM_eigen_i1e_float/512         1424           1424         485953
BM_eigen_i1e_float/4k         10722          10723          65162
BM_eigen_i1e_float/32k        86286          86297           8048
BM_eigen_i1e_float/256k      691821         691868           1000
BM_eigen_i1e_float/1M       2777336        2777747            256


This shows anywhere from a 50% to 75% improvement on these operations.

I've also benchmarked without any of these flags turned on, and got similar
performance to before (if not better).

Also tested packetmath.cpp + special_functions to ensure no regressions.
 2019-09-11 18:34:02 -07:00
Gael Guennebaud
747c6a51ca bug #1736: fix compilation issue with A(all,{1,2}).col(j) by implementing true compile-time "if" for block_evaluator<>::coeff(i)/coeffRef(i) 2019-09-11 15:40:07 +02:00
Gael Guennebaud
031f17117d bug #1741: fix self-adjoint*matrix, triangular*matrix, and triangular^1*matrix with a destination having a non-trivial inner-stride 2019-09-11 15:04:25 +02:00
Gael Guennebaud
c06e6fd115 bug #1741: fix SelfAdjointView::rankUpdate and product to triangular part for destination with non-trivial inner stride 2019-09-10 23:29:52 +02:00
Gael Guennebaud
ea0d5dc956 bug #1741: fix C.noalias() = A*C; with C.innerStride()!=1 2019-09-10 16:25:24 +02:00
Srinivas Vasudevan
e38dd48a27 PR 681: Add ndtri function, the inverse of the normal distribution function. 2019-08-12 19:26:29 -04:00
Rasmus Munk Larsen
1187bb65ad Add more tests for corner cases of log1p and expm1. Add handling of infinite arguments to log1p such that log1p(inf) = inf. 2019-08-28 12:20:21 -07:00
Rasmus Munk Larsen
9aba527405 Revert changes to std_falback::log1p that broke handling of arguments less than -1. Fix packet op accordingly. 2019-08-27 15:35:29 -07:00
Rasmus Munk Larsen
b021cdea6d Clean up float16 a.k.a. Eigen::half support in Eigen. Move the definition of half to Core/arch/Default and move arch-specific packet ops to their respective sub-directories. 2019-08-27 11:30:31 -07:00
Rasmus Munk Larsen
a3298b22ec Implement vectorized versions of log1p and expm1 in Eigen using Kahan's formulas, and change the scalar implementations to properly handle infinite arguments. 
Depending on instruction set, significant speedups are observed for the vectorized path:
log1p wall time is reduced 60-93% (2.5x - 15x speedup)
expm1 wall time is reduced 0-85% (1x - 7x speedup)

The scalar path is slower by 20-30% due to the extra branch needed to handle +infinity correctly.

Full benchmarks measured on Intel(R) Xeon(R) Gold 6154 here: https://bitbucket.org/snippets/rmlarsen/MXBkpM
 2019-08-12 13:53:28 -07:00
Rasmus Munk Larsen
85928e5f47 Guard against repeated definition of EIGEN_MPL2_ONLY 2019-08-07 14:19:00 -07:00
Mehdi Goli
16a56b2ddd [SYCL] This PR adds the minimum modifications to Eigen core required to run Eigen unsupported modules on devices supporting SYCL. 
* Adding SYCL memory model
* Enabling/Disabling SYCL  backend in Core
*  Supporting Vectorization
 2019-06-27 12:25:09 +01:00
Rasmus Munk Larsen
988f24b730 Various fixes for packet ops. 
1. Fix buggy pcmp_eq and unit test for half types.
2. Add unit test for pselect and add specializations for SSE 4.1, AVX512, and half types.
3. Get rid of FIXME: Implement faster pnegate for half by XOR'ing with a sign bit mask.
 2019-06-20 11:47:49 -07:00
Rasmus Larsen
c1b0aea653 Merged in Artem-B/eigen (pull request PR-654) 
Minor build improvements

Approved-by: Rasmus Larsen <rmlarsen@google.com>
 2019-05-31 22:27:04 +00:00
Rasmus Munk Larsen
b08527b0c1 Clean up CUDA/NVCC version macros and their use in Eigen, and a few other CUDA build failures. 2019-05-31 15:26:06 -07:00
tra
b4c49bf00e Minor build improvements 
* Allow specifying multiple GPU architectures. E.g.:
  cmake -DEIGEN_CUDA_COMPUTE_ARCH="60;70"
* Pass CUDA SDK path to clang. Without it it will default to /usr/local/cuda
which may not be the right location, if cmake was invoked with
-DCUDA_TOOLKIT_ROOT_DIR=/some/other/CUDA/path
 2019-05-31 14:08:34 -07:00
Christoph Hertzberg
4ccd1ece92 bug #1707: Fix deprecation warnings, or disable warnings when testing deprecated functions 2019-05-10 14:57:05 +02:00
Eugene Zhulenev
e9f0eb8a5e Add masked_store_available to unpacket_traits 2019-05-02 14:52:58 -07:00
Eugene Zhulenev
b4010f02f9 Add masked pstoreu to AVX and AVX512 PacketMath 2019-05-02 13:14:18 -07:00
Anuj Rawat
8c7a6feb8e Adding lowlevel APIs for optimized RHS packet load in TensorFlow 
SpatialConvolution

Low-level APIs are added in order to optimized packet load in gemm_pack_rhs
in TensorFlow SpatialConvolution. The optimization is for scenario when a
packet is split across 2 adjacent columns. In this case we read it as two
'partial' packets and then merge these into 1. Currently this only works for
Packet16f (AVX512) and Packet8f (AVX2). We plan to add this for other
packet types (such as Packet8d) also.

This optimization shows significant speedup in SpatialConvolution with
certain parameters. Some examples are below.

Benchmark parameters are specified as:
Batch size, Input dim, Depth, Num of filters, Filter dim

Speedup numbers are specified for number of threads 1, 2, 4, 8, 16.

AVX512:

Parameters                  | Speedup (Num of threads: 1, 2, 4, 8, 16)
----------------------------|------------------------------------------
128,   24x24,  3, 64,   5x5 |2.18X, 2.13X, 1.73X, 1.64X, 1.66X
128,   24x24,  1, 64,   8x8 |2.00X, 1.98X, 1.93X, 1.91X, 1.91X
 32,   24x24,  3, 64,   5x5 |2.26X, 2.14X, 2.17X, 2.22X, 2.33X
128,   24x24,  3, 64,   3x3 |1.51X, 1.45X, 1.45X, 1.67X, 1.57X
 32,   14x14, 24, 64,   5x5 |1.21X, 1.19X, 1.16X, 1.70X, 1.17X
128, 128x128,  3, 96, 11x11 |2.17X, 2.18X, 2.19X, 2.20X, 2.18X

AVX2:

Parameters                  | Speedup (Num of threads: 1, 2, 4, 8, 16)
----------------------------|------------------------------------------
128,   24x24,  3, 64,   5x5 | 1.66X, 1.65X, 1.61X, 1.56X, 1.49X
 32,   24x24,  3, 64,   5x5 | 1.71X, 1.63X, 1.77X, 1.58X, 1.68X
128,   24x24,  1, 64,   5x5 | 1.44X, 1.40X, 1.38X, 1.37X, 1.33X
128,   24x24,  3, 64,   3x3 | 1.68X, 1.63X, 1.58X, 1.56X, 1.62X
128, 128x128,  3, 96, 11x11 | 1.36X, 1.36X, 1.37X, 1.37X, 1.37X

In the higher level benchmark cifar10, we observe a runtime improvement
of around 6% for AVX512 on Intel Skylake server (8 cores).

On lower level PackRhs micro-benchmarks specified in TensorFlow
tensorflow/core/kernels/eigen_spatial_convolutions_test.cc, we observe
the following runtime numbers:

AVX512:

Parameters                                                     | Runtime without patch (ns) | Runtime with patch (ns) | Speedup
---------------------------------------------------------------|----------------------------|-------------------------|---------
BM_RHS_NAME(PackRhs, 128, 24, 24, 3, 64, 5, 5, 1, 1, 256, 56)  |  41350                     | 15073                   | 2.74X
BM_RHS_NAME(PackRhs, 32, 64, 64, 32, 64, 5, 5, 1, 1, 256, 56)  |   7277                     |  7341                   | 0.99X
BM_RHS_NAME(PackRhs, 32, 64, 64, 32, 64, 5, 5, 2, 2, 256, 56)  |   8675                     |  8681                   | 1.00X
BM_RHS_NAME(PackRhs, 32, 64, 64, 30, 64, 5, 5, 1, 1, 256, 56)  |  24155                     | 16079                   | 1.50X
BM_RHS_NAME(PackRhs, 32, 64, 64, 30, 64, 5, 5, 2, 2, 256, 56)  |  25052                     | 17152                   | 1.46X
BM_RHS_NAME(PackRhs, 32, 256, 256, 4, 16, 8, 8, 1, 1, 256, 56) |  18269                     | 18345                   | 1.00X
BM_RHS_NAME(PackRhs, 32, 256, 256, 4, 16, 8, 8, 2, 4, 256, 56) |  19468                     | 19872                   | 0.98X
BM_RHS_NAME(PackRhs, 32, 64, 64, 4, 16, 3, 3, 1, 1, 36, 432)   | 156060                     | 42432                   | 3.68X
BM_RHS_NAME(PackRhs, 32, 64, 64, 4, 16, 3, 3, 2, 2, 36, 432)   | 132701                     | 36944                   | 3.59X

AVX2:

Parameters                                                     | Runtime without patch (ns) | Runtime with patch (ns) | Speedup
---------------------------------------------------------------|----------------------------|-------------------------|---------
BM_RHS_NAME(PackRhs, 128, 24, 24, 3, 64, 5, 5, 1, 1, 256, 56)  | 26233                      | 12393                   | 2.12X
BM_RHS_NAME(PackRhs, 32, 64, 64, 32, 64, 5, 5, 1, 1, 256, 56)  |  6091                      |  6062                   | 1.00X
BM_RHS_NAME(PackRhs, 32, 64, 64, 32, 64, 5, 5, 2, 2, 256, 56)  |  7427                      |  7408                   | 1.00X
BM_RHS_NAME(PackRhs, 32, 64, 64, 30, 64, 5, 5, 1, 1, 256, 56)  | 23453                      | 20826                   | 1.13X
BM_RHS_NAME(PackRhs, 32, 64, 64, 30, 64, 5, 5, 2, 2, 256, 56)  | 23167                      | 22091                   | 1.09X
BM_RHS_NAME(PackRhs, 32, 256, 256, 4, 16, 8, 8, 1, 1, 256, 56) | 23422                      | 23682                   | 0.99X
BM_RHS_NAME(PackRhs, 32, 256, 256, 4, 16, 8, 8, 2, 4, 256, 56) | 23165                      | 23663                   | 0.98X
BM_RHS_NAME(PackRhs, 32, 64, 64, 4, 16, 3, 3, 1, 1, 36, 432)   | 72689                      | 44969                   | 1.62X
BM_RHS_NAME(PackRhs, 32, 64, 64, 4, 16, 3, 3, 2, 2, 36, 432)   | 61732                      | 39779                   | 1.55X

All benchmarks on Intel Skylake server with 8 cores.
 2019-04-20 06:46:43 +00:00
Gael Guennebaud
48898a988a fix unit test in c++03: c++03 does not allow passing local or anonymous enum as template param 2019-03-18 11:38:36 +01:00
Gael Guennebaud
cf7e2e277f bug #1692: enable enum as sizes of Matrix and Array 2019-03-17 21:59:30 +01:00
David Tellenbach
b013176e52 Remove undefined std::complex<int> 2019-03-14 11:40:28 +01:00
David Tellenbach
97f9a46cb9 PR 593: Add variadtic ctor for DiagonalMatrix with unit tests 2019-03-14 10:18:24 +01:00
Gael Guennebaud
45ab514fe2 revert debug stuff 2019-03-14 10:08:12 +01:00
Gael Guennebaud
d7d2f0680e bug #1684: partially workaround clang's 6/7 bug #40815 2019-03-13 10:40:01 +01:00
Gael Guennebaud
b0d406d91c Enable construction of Ref<VectorType> from a runtime vector. 2019-03-03 15:25:25 +01:00
Gael Guennebaud
32502f3c45 bug #1684: add simplified regression test for respective clang's bug (this also reveal the same bug in Apples's clang) 2019-02-22 10:29:06 +01:00
Gael Guennebaud
2a39659d79 Add fully generic Vector<Type,Size> and RowVector<Type,Size> type aliases. 2019-02-20 15:23:23 +01:00
Gael Guennebaud
44b54fa4a3 Protect c++11 type alias with Eigen's macro, and add respective unit test. 2019-02-20 14:43:05 +01:00
Gael Guennebaud
4e8047cdcf Fix compilation with gcc and remove TR1 stuff. 2019-02-20 13:59:34 +01:00
Gael Guennebaud
edd413c184 bug #1409: make EIGEN_MAKE_ALIGNED_OPERATOR_NEW* macros empty in c++17 mode: 
- this helps clang 5 and 6 to support alignas in STL's containers.
 - this makes the public API of our (and users) classes cleaner
 2019-02-20 13:52:11 +01:00
Gael Guennebaud
3b5deeb546 bug #899: make sparseqr unit test more stable by 1) trying with larger threshold and 2) relax rank computation for rank-deficient problems. 2019-02-19 22:57:51 +01:00
Gael Guennebaud
292d61970a Fix C++17 compilation 2019-02-19 21:59:41 +01:00
Gael Guennebaud
2cfc025bda fix unit compilation in c++17: std::ptr_fun has been removed. 2019-02-19 14:05:22 +01:00
Gael Guennebaud
7d10c78738 bug #1046: add unit tests for correct propagation of alignment through std::alignment_of 2019-02-19 10:31:56 +01:00
Gael Guennebaud
e23bf40dc2 Add unit test for LinSpaced and complex numbers. 2019-02-18 22:03:47 +01:00
Gael Guennebaud
31b6e080a9 Fix regression: .conjugate() was popped out but not re-introduced. 2019-02-18 14:45:55 +01:00
Gael Guennebaud
c69d0d08d0 Set cost of conjugate to 0 (in practice it boils down to a no-op). 
This is also important to make sure that A.conjugate() * B.conjugate() does not evaluate
its arguments into temporaries (e.g., if A and B are fixed and small, or * fall back to lazyProduct)
 2019-02-18 14:43:07 +01:00
Gael Guennebaud
512b74aaa1 GEMM: catch all scalar-multiple variants when falling-back to a coeff-based product. 
Before only s*A*B was caught which was both inconsistent with GEMM, sub-optimal,
and could even lead to compilation-errors (https://stackoverflow.com/questions/54738495).
 2019-02-18 11:47:54 +01:00
Gael Guennebaud
dada863d23 Enable unit tests of PartialPivLU on fixed size matrices, and increase tested matrix size (blocking was not tested!) 2019-02-11 17:56:20 +01:00
Gael Guennebaud
8a06c699d0 bug #1669: fix PartialPivLU/inverse with zero-sized matrices. 2019-01-29 10:27:13 +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
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
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
6908ce2a15 More thoroughly check variadic template ctor of fixed-size vectors 2019-01-24 10:24:28 +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
Gael Guennebaud
d18f49cbb3 Fix compilation of unit tests with gcc and c++17 2019-01-18 11:12:42 +01:00
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
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
Gael Guennebaud
b57c9787b1 Cleanup useless const_cast and add missing broadcast assignment tests 2019-01-17 16:55:42 +01:00
Patrick Peltzer
bba2f05064 Boosttest only available for Boost version >= 1.53.0 2019-01-17 11:54:37 +01:00
Patrick Peltzer
15e53d5d93 PR 567: makes all dense solvers inherit SoverBase (LU,Cholesky,QR,SVD). 
This changeset also includes:
 * add HouseholderSequence::conjugateIf
 * define int as the StorageIndex type for all dense solvers
 * dedicated unit tests, including assertion checking
 * _check_solve_assertion(): this method can be implemented in derived solver classes to implement custom checks
 * CompleteOrthogonalDecompositions: add applyZOnTheLeftInPlace, fix scalar type in applyZAdjointOnTheLeftInPlace(), add missing assertions
 * Cholesky: add missing assertions
 * FullPivHouseholderQR: Corrected Scalar type in _solve_impl()
 * BDCSVD: Unambiguous return type for ternary operator
 * SVDBase: Corrected Scalar type in _solve_impl()
 2019-01-17 01:17:39 +01:00
Gael Guennebaud
7f32109c11 Add conjugateIf<bool> members to DesneBase, TriangularView, SelfadjointView, and make PartialPivLU use it. 2019-01-17 11:33:43 +01:00
Gael Guennebaud
c8e40edac9 Remove Eigen2ToEigen3 migration page (obsolete since 3.3) 2019-01-16 16:27:00 +01:00
Gael Guennebaud
aeffdf909e bug #1617: add unit tests for empty triangular solve. 2019-01-16 15:24:59 +01:00
Gael Guennebaud
502f717980 bug #1646: disable aliasing detection for empty and 1x1 expression 2019-01-16 14:33:45 +01:00
Gael Guennebaud
2b70b2f570 Make Transform::rotation() an alias to Transform::linear() in the case of an Isometry 2019-01-15 22:50:42 +01:00
Gael Guennebaud
6ec6bf0b0d Enable visitor on empty matrices (the visitor is left unchanged), and protect min/maxCoeff(Index*,Index*) on empty matrices by an assertion (+ doc & unit tests) 2019-01-15 15:21:14 +01:00
Gael Guennebaud
027e44ed24 bug #1592: makes partial min/max reductions trigger an assertion on inputs with a zero reduction length (+doc and tests) 2019-01-15 15:13:24 +01:00
Gael Guennebaud
f8bc5cb39e Fix detection of vector-at-time: use Rows/Cols instead of MaxRow/MaxCols. 
This fix VectorXd(n).middleCol(0,0).outerSize() which was equal to 1.
 2019-01-15 15:09:49 +01:00
Gael Guennebaud
32d7232aec fix always true warning with gcc 4.7 2019-01-15 11:18:48 +01:00
Gael Guennebaud
e7d4d4f192 cleanup 2019-01-15 10:51:03 +01:00
Rasmus Larsen
5a59452aae Merged eigen/eigen into default 2019-01-14 10:23:23 -08:00
Gael Guennebaud
61b6eb05fe AVX512 (r)sqrt(double) was mistakenly disabled with clang and others 2019-01-14 17:28:47 +01:00
Greg Coombe
9d988a1e1a Initialize isometric transforms like affine transforms. 
The isometric transform, like the affine transform, has an implicit last
row of [0, 0, 0, 1]. This was not being properly initialized, as verified
by a new test function.
 2019-01-11 23:14:35 -08:00
Gael Guennebaud
f566724023 Fix StorageIndex FIXME in dense LU solvers 2019-01-13 17:54:30 +01:00
Rasmus Munk Larsen
28ba1b2c32 Add support for inverse hyperbolic functions. 
Fix cost of division.
 2019-01-11 17:45:37 -08:00
Rasmus Munk Larsen
fcfced13ed Rename pones -> ptrue. Use _CMP_TRUE_UQ where appropriate. 2019-01-09 17:20:33 -08:00
Rasmus Munk Larsen
8f04442526 Collapsed revision 
* Collapsed revision
* Add packet up "pones". Write pnot(a) as pxor(pones(a), a).
* Collapsed revision
* Simplify a bit.
* Undo useless diffs.
* Fix typo.
 2019-01-09 16:34:23 -08:00
Rasmus Munk Larsen
cb955df9a6 Add packet up "pones". Write pnot(a) as pxor(pones(a), a). 2019-01-09 16:17:08 -08:00
Rasmus Larsen
cb3c059fa4 Merged eigen/eigen into default 2019-01-09 15:04:17 -08:00
Gael Guennebaud
e6b217b8dd bug #1652: implements a much more accurate version of vectorized sin/cos. This new version achieve same speed for SSE/AVX, and is slightly faster with FMA. Guarantees are as follows: 
- no FMA: 1ULP up to 3pi, 2ULP up to sin(25966) and cos(18838), fallback to std::sin/cos for larger inputs
  - FMA: 1ULP up to sin(117435.992) and cos(71476.0625), fallback to std::sin/cos for larger inputs
 2019-01-09 15:25:17 +01:00
Rasmus Munk Larsen
055f0b73db Add support for pcmp_eq and pnot, including for complex types. 2019-01-07 16:53:36 -08:00
Gael Guennebaud
697fba3bb0 Fix unit test 2018-12-27 11:20:47 +01:00
Gael Guennebaud
0f6f75bd8a Implement a faster fix for sin/cos of large entries that also correctly handle INF input. 2018-12-23 17:26:21 +01:00
Gael Guennebaud
38d704def8 Make sure that psin/pcos return number in [-1,1] for large inputs (though sin/cos on large entries is quite useless because it's inaccurate) 2018-12-23 16:13:24 +01:00
Gael Guennebaud
5713fb7feb Fix plog(+INF): it returned ~87 instead of +INF 2018-12-23 15:40:52 +01:00
Gael Guennebaud
cfc70dc13f Add regression test for bug #1174 2018-12-12 18:03:31 +01:00
Gael Guennebaud
2de8da70fd bug #1557: fix RealSchur and EigenSolver for matrices with only zeros on the diagonal. 2018-12-12 17:30:08 +01:00
Gael Guennebaud
72c0bbe2bd Simplify handling of tests that must fail to compile. 
Each test is now a normal ctest target, and build properties (compiler+flags) are preserved (instead of starting a new build-dir from scratch).
 2018-12-12 15:48:36 +01:00
Gael Guennebaud
81c27325ae bug #1641: fix testing of pandnot and fix pandnot for complex on SSE/AVX/AVX512 2018-12-08 14:27:48 +01:00
Gael Guennebaud
cd25b538ab Fix noise in sparse_basic_3 (numerical cancellation) 2018-12-08 00:13:37 +01:00
Gael Guennebaud
efaf03bf96 Fix noise in lu unit test 2018-12-08 00:05:03 +01:00
Gael Guennebaud
aab749b1c3 fix test regarding AVX512 vectorization of complexes. 2018-12-06 16:55:00 +01:00
Gael Guennebaud
c53eececb0 Implement AVX512 vectorization of std::complex<float/double> 2018-12-06 15:58:06 +01:00
Christoph Hertzberg
919414b9fe bug #785: Make Cholesky decomposition work for empty matrices 2018-12-03 16:18:15 +01:00
Gael Guennebaud
69ace742be Several improvements regarding packet-bitwise operations: 
- add unit tests
- optimize their AVX512f implementation
- add missing implementations (half, Packet4f, ...)
 2018-11-30 15:56:08 +01:00
Gael Guennebaud
48fe78c375 bug #1630: fix linspaced when requesting smaller packet size than default one. 2018-11-28 13:15:06 +01:00
Gael Guennebaud
382279eb7f Extend unit test to recursively check half-packet types and non packet types 2018-11-26 14:10:07 +01:00
Gael Guennebaud
e3b22a6bd0 merge 2018-11-23 16:06:21 +01:00
Gael Guennebaud
572d62697d check two ctors 2018-11-23 15:37:09 +01:00