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182 Commits

Author SHA1 Message Date
Rasmus Munk Larsen
c770746d70 Fix Half NaN definition and test. 
The `half_float` test was failing with `-mcpu=cortex-a55` (native `__fp16`) due
to a bad NaN bit-pattern comparison (in the case of casting a float to `__fp16`,
the signaling `NaN` is quieted). There was also an inconsistency between
`numeric_limits<half>::quiet_NaN()` and `NumTraits::quiet_NaN()`.  Here we
correct the inconsistency and compare NaNs according to the IEEE 754
definition.

Also modified the `bfloat16_float` test to match.

Tested with `cortex-a53` and `cortex-a55`.
 2020-11-24 20:53:07 +00:00
mehdi-goli
e24a1f57e3 [SYCL Function pointer Issue]: SYCL does not support function pointer inside the kernel, due to the portability issue of a function pointer and memory address space among host and accelerators. To fix the issue, function pointers have been replaced by function objects. 2020-11-12 01:50:28 +00:00
Deven Desai
011e0db31d Fix for ROCm/HIP breakage - 201013 
The following commit seems to have introduced regressions in ROCm/HIP support.

183a208212

It causes some unit-tests to fail with the following error

```
...
Eigen/src/Core/GenericPacketMath.h:322:3: error: no member named 'bit_and' in the global namespace; did you mean 'std::bit_and'?
...
Eigen/src/Core/GenericPacketMath.h:329:3: error: no member named 'bit_or' in the global namespace; did you mean 'std::bit_or'?
...
Eigen/src/Core/GenericPacketMath.h:336:3: error: no member named 'bit_xor' in the global namespace; did you mean 'std::bit_xor'?
...
```

The error occurs because, when compiling the device code in HIP/CUDA, the compiler will pick up the some of the std functions (whose calls are prefixed by EIGEN_USING_STD) from the global namespace (i.e. use ::bit_xor instead of std::bit_xor). For this to work, those functions must be declared in the global namespace in the HIP/CUDA header files. The `bit_and`, `bit_or` and `bit_xor` routines are not declared in the HIP header file that contain the decls for the std math functions ( `math_functions.h` ), and this is the cause of the error above.

It seems that the newer HIP compilers do support the calling of `std::` math routines within device code, and the ideal fix here would have been to change all calls to std math functions in EIGEN to use the `std::` namespace (instead of the global namespace ), when compiling  with HIP compiler. However it seems there was a recent commit to remove the EIGEN_USING_STD_MATH macro and collapse it uses into the EIGEN_USING_STD macro ( 4091f6b25c ).

Replacing all std math calls will essentially require re-surrecting the EIGEN_USING_STD_MATH macro, so not choosing that option.

Also HIP compilers only have support std math calls within device code, and not all std functions (specifically not for malloc/free which are prefixed via EIGEN_USING_STD). So modyfing EIGEN_USE_STD implementation to use std:: namspace for HIP will not work either.

Hence going for the ugly solution of special casing the three calls that breaking the HIP compile, to explicitly use the std:: namespace
 2020-10-15 12:17:35 +00:00
Rasmus Munk Larsen
208b3626d1 Revert generic implementation of predux, since it break compilation of predux_any with MSVC. 2020-10-14 21:41:28 +00:00
Rasmus Munk Larsen
c6953f799b Add packet generic ops predux_fmin, predux_fmin_nan, predux_fmax, and predux_fmax_nan that implement reductions with PropagateNaN, and PropagateNumbers semantics. Add (slow) generic implementations for most reductions. 2020-10-13 21:48:31 +00:00
Rasmus Munk Larsen
9a4d04c05f Make bitwise_helper a device function to unbreak GPU builds. 2020-10-10 01:45:20 +00:00
David Tellenbach
4091f6b25c Drop EIGEN_USING_STD_MATH in favour of EIGEN_USING_STD 2020-10-09 02:05:05 +02:00
Rasmus Munk Larsen
183a208212 Implement generic bitwise logical packet ops that work for all types. 2020-10-08 22:45:20 +00:00
Rasmus Munk Larsen
b431024404 Don't make assumptions about NaN-propagation for pmin/pmax - it various across platforms. 
Change test to only test for NaN-propagation for pfmin/pfmax.
 2020-10-07 19:05:18 +00:00
Rasmus Munk Larsen
3b445d9bf2 Add a generic packet ops corresponding to {std}::fmin and {std}::fmax. The non-sensical NaN-propagation rules for std::min std::max implemented by pmin and pmax in Eigen is a longstanding source og confusion and bug report. This change is a first step towards addressing it, as discussing in issue #564. 2020-10-01 16:54:31 +00:00
Pedro Caldeira
35d149e34c Add missing functions for Packet8bf in Altivec architecture. 
Including new tests for bfloat16 Packets.
Fix prsqrt on GenericPacketMath.
 2020-09-08 09:22:11 -05:00
Guoqiang QI
85428a3440 Add Neon psqrt<Packet2d> and pexp<Packet2d> 2020-09-08 09:04:03 +00:00
Alexander Neumann
5272106826 remove semi triggering -Wextra-semi-stmt 2020-09-07 11:42:30 +02:00
Sheng Yang
116c5235ac BF16 for scalar_cmp_with_cast_op 2020-07-01 18:33:42 +00:00
Antonio Sanchez
03ebdf6acb Added missing NEON pcasts, update packetmath tests. 
The NEON `pcast` operators are all implemented and tested for existing
packets. This requires adding a `pcast(a,b,c,d,e,f,g,h)` for casting
between `int64_t` and `int8_t` in `GenericPacketMath.h`.

Removed incorrect `HasHalfPacket`  definition for NEON's
`Packet2l`/`Packet2ul`.

Adjustments were also made to the `packetmath` tests. These include
- minor bug fixes for cast tests (i.e. 4:1 casts, only casting for
  packets that are vectorizable)
- added 8:1 cast tests
- random number generation
  - original had uninteresting 0 to 0 casts for many casts between
    floating-point and integers, and exhibited signed overflow
    undefined behavior

Tested:
```
$ aarch64-linux-gnu-g++ -static -I./ '-DEIGEN_TEST_PART_ALL=1' test/packetmath.cpp -o packetmath
$ adb push packetmath /data/local/tmp/
$ adb shell "/data/local/tmp/packetmath"
```
 2020-06-21 09:32:31 -07:00
Antonio Sanchez
a7d2552af8 Remove HasCast and fix packetmath cast tests. 
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.
 2020-06-11 17:26:56 +00:00
Rasmus Munk Larsen
c2ab36f47a Fix broken packetmath test for logistic on Arm. 2020-06-04 16:24:47 -07:00
Rasmus Munk Larsen
537e2b322f Fix typo in previous update to generic predux_any. 2020-06-04 22:25:05 +00:00
Rasmus Munk Larsen
fdc1cbdce3 Avoid implicit float equality comparison in generic predux_any, but use numext::not_equal_strict to avoid breaking builds that compile with -Werror=float-equal. 2020-06-04 22:15:56 +00:00
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
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
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
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
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
393dbd8ee9 Fix bug in 52d54278be 2020-03-27 16:42:18 +00:00
Joel Holdsworth
52d54278be Additional NEON packet-math operations 2020-03-26 20:18:19 +00:00
Joel Holdsworth
d5c665742b Add absolute_difference coefficient-wise binary Array function 2020-03-19 17:45:20 +00:00
Everton Constantino
6ff5a14091 Reenabling packetmath unsigned tests, adding dummy pabs for relevant unsigned 
types.
 2020-03-19 17:31:49 +00:00
Joel Holdsworth
232f904082 Add shift_left<N> and shift_right<N> coefficient-wise unary Array functions 2020-03-19 17:24:06 +00:00
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
Deven Desai
312c8e77ff Fix for the HIP build+test errors. 
Recent changes have introduced the following build error when compiling with HIPCC

---------

unsupported/test/../../Eigen/src/Core/GenericPacketMath.h:254:58: error:  'ldexp':  no overloaded function has restriction specifiers that are compatible with the ambient context 'pldexp'

---------

The fix for the error is to pick the math function(s) from the global namespace (where they are declared as device functions in the HIP header files) when compiling with HIPCC.
 2019-12-02 17:41:32 +00:00
Rasmus Munk Larsen
5a3ebda36b Fix warning due to missing cast for exponent arguments for std::frexp and std::lexp. 2019-11-26 16:18:29 -08:00
Gael Guennebaud
d1def335dc fix one more possible conflicts with real/imag 2019-10-08 16:19:10 +02:00
Gael Guennebaud
87427d2eaa PR 719: fix real/imag namespace conflict 2019-10-08 09:15:17 +02:00
Rasmus Munk Larsen
13ef08e5ac Move implementation of vectorized error function erf() to SpecialFunctionsImpl.h. 2019-09-27 13:56:04 -07:00
Rasmus Munk Larsen
6de5ed08d8 Add generic PacketMath implementation of the Error Function (erf). 2019-09-19 12:48:30 -07:00
Deven Desai
e02d429637 Fix for the HIP build+test errors. 
The errors were introduced by this commit : 6e215cf109


The fix is switching to using ::<math_func> instead std::<math_func> when compiling for GPU
 2019-09-18 18:44:20 +00:00
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
Deven Desai
cdb377d0cb Fix for the HIP build+test errors introduced by the ndtri support. 
The fixes needed are
 * adding EIGEN_DEVICE_FUNC attribute to a couple of funcs (else HIPCC will error out when non-device funcs are called from global/device funcs)
 * switching to using ::<math_func> instead std::<math_func> (only for HIPCC) in cases where the std::<math_func> is not recognized as a device func by HIPCC
 * removing an errant "j" from a testcase (don't know how that made it in to begin with!)
 2019-09-06 16:03:49 +00:00
Gael Guennebaud
5702a57926 Fix possible warning regarding strict equality comparisons 2019-09-04 22:57:04 +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
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
ccddeaad90 fix warning 2019-01-14 16:51:16 +01:00
Rasmus Munk Larsen
89c4001d6f Fix warnings in ptrue for complex and half types. 2019-01-11 14:10:57 -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
f6ba6071c5 Fix typo. 2019-01-09 16:34:23 -08:00