Commit Graph

317 Commits

Author SHA1 Message Date
Rasmus Munk Larsen
9312a5bf5c Implement a generic vectorized version of Smith's algorithms for complex division. 2021-07-01 23:31:12 +00:00
Rasmus Munk Larsen
52a5f98212 Get rid of code duplication for conj_helper. For packets where LhsType=RhsType a single generic implementation suffices. For scalars, the generic implementation of pconj automatically forwards to numext::conj, so much of the existing specialization can be avoided. For mixed types we still need specializations. 2021-06-24 15:47:48 -07:00
guoqiangqi
3d9051ea84 Changing the storage of the SSE complex packets to that of the wrapper. This should fix #2242 . 2021-05-10 23:53:16 +00:00
Christoph Hertzberg
722ca0b665 Revert addition of unused paddsub<Packet2cf>. This fixes #2242 2021-05-06 18:36:47 +02:00
Christoph Hertzberg
9357feedc7 Avoid using uninitialized inputs and if available, use slightly more efficient movsd instruction for pset1<Packet2cf>. 2021-04-13 01:36:59 +02:00
David Tellenbach
eb71e5db98 Fix another warning on missing commas 2021-03-17 03:07:04 +01:00
David Tellenbach
9fb7062440 Silence warning on comma at end of enumerator list 2021-03-17 01:46:52 +01:00
Antonio Sanchez
82d61af3a4 Fix rint SSE/NEON again, using optimization barrier.
This is a new version of !423, which failed for MSVC.

Defined `EIGEN_OPTIMIZATION_BARRIER(X)` that uses inline assembly to
prevent operations involving `X` from crossing that barrier. Should
work on most `GNUC` compatible compilers (MSVC doesn't seem to need
this). This is a modified version adapted from what was used in
`psincos_float` and tested on more platforms
(see #1674, https://godbolt.org/z/73ezTG).

Modified `rint` to use the barrier to prevent the add/subtract rounding
trick from being optimized away.

Also fixed an edge case for large inputs that get bumped up a power of two
and ends up rounding away more than just the fractional part.  If we are
over `2^digits` then just return the input.  This edge case was missed in
the test since the test was comparing approximate equality, which was still
satisfied.  Adding a strict equality option catches it.
2021-03-05 08:54:12 -08:00
Antonio Sánchez
9a663973b4 Revert "Fix rint for SSE/NEON."
This reverts commit e72dfeb8b9
2021-03-03 18:51:51 +00:00
Antonio Sanchez
e72dfeb8b9 Fix rint for SSE/NEON.
It seems *sometimes* with aggressive optimizations the combination
`psub(padd(a, b), b)` trick to force rounding is compiled away. Here
we replace with inline assembly to prevent this (I tried `volatile`,
but that leads to additional loads from memory).

Also fixed an edge case for large inputs `a` where adding `b` bumps
the value up a power of two and ends up rounding away more than
just the fractional part.  If we are over `2^digits` then just return
the input.  This edge case was missed in the test since the test was
comparing approximate equality, which was still satisfied.  Adding
a strict equality option catches it.
2021-03-03 09:41:46 -08:00
Antonio Sanchez
1e0c7d4f49 Add print for SSE/NEON, use NEON rounding intrinsics if available.
In SSE, by adding/subtracting 2^MantissaBits, we force rounding according to the
current rounding mode.

For NEON, we use the provided intrinsics for rint/floor/ceil if
available (armv8).

Related to #1969.
2021-02-27 22:42:07 +00:00
Antonio Sanchez
5529db7524 Fix SSE/NEON pfloor/pceil for saturated values.
The original will saturate if the input does not fit into an integer
type.  Here we fix this, returning the input if it doesn't have
enough precision to have a fractional part.

Also added `pceil` for NEON.

Fixes #1969.
2021-02-25 14:39:26 -08:00
Christoph Hertzberg
73922b0174 Fixes Bug #1925. Packets should be passed by const reference, even to inline functions. 2021-02-20 18:56:42 +01:00
Antonio Sanchez
7ff0b7a980 Updated pfrexp implementation.
The original implementation fails for 0, denormals, inf, and NaN.

See #2150
2021-02-17 02:23:24 +00:00
Antonio Sanchez
4cb563a01e Fix ldexp implementations.
The previous implementations produced garbage values if the exponent did
not fit within the exponent bits.  See #2131 for a complete discussion,
and !375 for other possible implementations.

Here we implement the 4-factor version. See `pldexp_impl` in
`GenericPacketMathFunctions.h` for a full description.

The SSE `pcmp*` methods were moved down since `pcmp_le<Packet4i>`
requires `por`.

Left as a "TODO" is to delegate to a faster version if we know the
exponent does fit within the exponent bits.

Fixes #2131.
2021-02-10 22:45:41 +00:00
Rasmus Munk Larsen
cdd8fdc32e Vectorize pow(x, y). This closes https://gitlab.com/libeigen/eigen/-/issues/2085, which also contains a description of the algorithm.
I ran some testing (comparing to `std::pow(double(x), double(y)))` for `x` in the set of all (positive) floats in the interval `[std::sqrt(std::numeric_limits<float>::min()), std::sqrt(std::numeric_limits<float>::max())]`, and `y` in `{2, sqrt(2), -sqrt(2)}` I get the following error statistics:

```
max_rel_error = 8.34405e-07
rms_rel_error = 2.76654e-07
```

If I widen the range to all normal float I see lower accuracy for arguments where the result is subnormal, e.g. for `y = sqrt(2)`:

```
max_rel_error = 0.666667
rms = 6.8727e-05
count = 1335165689
argmax = 2.56049e-32, 2.10195e-45 != 1.4013e-45
```

which seems reasonable, since these results are subnormals with only couple of significant bits left.
2021-01-18 13:25:16 +00:00
Antonio Sanchez
bde6741641 Improved std::complex sqrt and rsqrt.
Replaces `std::sqrt` with `complex_sqrt` for all platforms (previously
`complex_sqrt` was only used for CUDA and MSVC), and implements
custom `complex_rsqrt`.

Also introduces `numext::rsqrt` to simplify implementation, and modified
`numext::hypot` to adhere to IEEE IEC 6059 for special cases.

The `complex_sqrt` and `complex_rsqrt` implementations were found to be
significantly faster than `std::sqrt<std::complex<T>>` and
`1/numext::sqrt<std::complex<T>>`.

Benchmark file attached.
```
GCC 10, Intel Xeon, x86_64:
---------------------------------------------------------------------------
Benchmark                                 Time             CPU   Iterations
---------------------------------------------------------------------------
BM_Sqrt<std::complex<float>>           9.21 ns         9.21 ns     73225448
BM_StdSqrt<std::complex<float>>        17.1 ns         17.1 ns     40966545
BM_Sqrt<std::complex<double>>          8.53 ns         8.53 ns     81111062
BM_StdSqrt<std::complex<double>>       21.5 ns         21.5 ns     32757248
BM_Rsqrt<std::complex<float>>          10.3 ns         10.3 ns     68047474
BM_DivSqrt<std::complex<float>>        16.3 ns         16.3 ns     42770127
BM_Rsqrt<std::complex<double>>         11.3 ns         11.3 ns     61322028
BM_DivSqrt<std::complex<double>>       16.5 ns         16.5 ns     42200711

Clang 11, Intel Xeon, x86_64:
---------------------------------------------------------------------------
Benchmark                                 Time             CPU   Iterations
---------------------------------------------------------------------------
BM_Sqrt<std::complex<float>>           7.46 ns         7.45 ns     90742042
BM_StdSqrt<std::complex<float>>        16.6 ns         16.6 ns     42369878
BM_Sqrt<std::complex<double>>          8.49 ns         8.49 ns     81629030
BM_StdSqrt<std::complex<double>>       21.8 ns         21.7 ns     31809588
BM_Rsqrt<std::complex<float>>          8.39 ns         8.39 ns     82933666
BM_DivSqrt<std::complex<float>>        14.4 ns         14.4 ns     48638676
BM_Rsqrt<std::complex<double>>         9.83 ns         9.82 ns     70068956
BM_DivSqrt<std::complex<double>>       15.7 ns         15.7 ns     44487798

Clang 9, Pixel 2, aarch64:
---------------------------------------------------------------------------
Benchmark                                 Time             CPU   Iterations
---------------------------------------------------------------------------
BM_Sqrt<std::complex<float>>           24.2 ns         24.1 ns     28616031
BM_StdSqrt<std::complex<float>>         104 ns          103 ns      6826926
BM_Sqrt<std::complex<double>>          31.8 ns         31.8 ns     22157591
BM_StdSqrt<std::complex<double>>        128 ns          128 ns      5437375
BM_Rsqrt<std::complex<float>>          31.9 ns         31.8 ns     22384383
BM_DivSqrt<std::complex<float>>        99.2 ns         98.9 ns      7250438
BM_Rsqrt<std::complex<double>>         46.0 ns         45.8 ns     15338689
BM_DivSqrt<std::complex<double>>        119 ns          119 ns      5898944
```
2021-01-17 08:50:57 -08:00
Guoqiang QI
38ae5353ab 1)provide a better generic paddsub op implementation
2)make paddsub op support the Packet2cf/Packet4f/Packet2f in NEON
3)make paddsub op support the Packet2cf/Packet4f in SSE
2021-01-13 22:54:03 +00:00
Rasmus Munk Larsen
05754100fe * Add iterative psqrt<double> for AVX and SSE when FMA is available. This provides a ~10% speedup.
* Write iterative sqrt explicitly in terms of pmadd. This gives up to 7% speedup for psqrt<float> with AVX & SSE with FMA.
* Remove iterative psqrt<double> for NEON, because the initial rsqrt apprimation is not accurate enough for convergence in 2 Newton-Raphson steps and with 3 steps, just calling the builtin sqrt insn is faster.

The following benchmarks were compiled with clang "-O2 -fast-math -mfma" and with and without -mavx.

AVX+FMA (float)

name                      old cpu/op  new cpu/op  delta
BM_eigen_sqrt_float/1     1.08ns ± 0%  1.09ns ± 1%    ~
BM_eigen_sqrt_float/8     2.07ns ± 0%  2.08ns ± 1%    ~
BM_eigen_sqrt_float/64    12.4ns ± 0%  12.4ns ± 1%    ~
BM_eigen_sqrt_float/512   95.7ns ± 0%  95.5ns ± 0%    ~
BM_eigen_sqrt_float/4k     776ns ± 0%   763ns ± 0%  -1.67%
BM_eigen_sqrt_float/32k   6.57µs ± 1%  6.13µs ± 0%  -6.69%
BM_eigen_sqrt_float/256k  83.7µs ± 3%  83.3µs ± 2%    ~
BM_eigen_sqrt_float/1M     335µs ± 2%   332µs ± 2%    ~

SSE+FMA (float)
name                      old cpu/op  new cpu/op  delta
BM_eigen_sqrt_float/1     1.08ns ± 0%  1.09ns ± 0%    ~
BM_eigen_sqrt_float/8     2.07ns ± 0%  2.06ns ± 0%    ~
BM_eigen_sqrt_float/64    12.4ns ± 0%  12.4ns ± 1%    ~
BM_eigen_sqrt_float/512   95.7ns ± 0%  96.3ns ± 4%    ~
BM_eigen_sqrt_float/4k     774ns ± 0%   763ns ± 0%  -1.50%
BM_eigen_sqrt_float/32k   6.58µs ± 2%  6.11µs ± 0%  -7.06%
BM_eigen_sqrt_float/256k  82.7µs ± 1%  82.6µs ± 1%    ~
BM_eigen_sqrt_float/1M     330µs ± 1%   329µs ± 2%    ~

SSE+FMA (double)
BM_eigen_sqrt_double/1      1.63ns ± 0%  1.63ns ± 0%     ~
BM_eigen_sqrt_double/8      6.51ns ± 0%  6.08ns ± 0%   -6.68%
BM_eigen_sqrt_double/64     52.1ns ± 0%  46.5ns ± 1%  -10.65%
BM_eigen_sqrt_double/512     417ns ± 0%   374ns ± 1%  -10.29%
BM_eigen_sqrt_double/4k     3.33µs ± 0%  2.97µs ± 1%  -11.00%
BM_eigen_sqrt_double/32k    26.7µs ± 0%  23.7µs ± 0%  -11.07%
BM_eigen_sqrt_double/256k    213µs ± 0%   206µs ± 1%   -3.31%
BM_eigen_sqrt_double/1M      862µs ± 0%   870µs ± 2%   +0.96%

AVX+FMA (double)
name                        old cpu/op  new cpu/op  delta
BM_eigen_sqrt_double/1      1.63ns ± 0%  1.63ns ± 0%     ~
BM_eigen_sqrt_double/8      6.51ns ± 0%  6.06ns ± 0%   -6.95%
BM_eigen_sqrt_double/64     52.1ns ± 0%  46.5ns ± 1%  -10.80%
BM_eigen_sqrt_double/512     417ns ± 0%   373ns ± 1%  -10.59%
BM_eigen_sqrt_double/4k     3.33µs ± 0%  2.97µs ± 1%  -10.79%
BM_eigen_sqrt_double/32k    26.7µs ± 0%  23.8µs ± 0%  -10.94%
BM_eigen_sqrt_double/256k    214µs ± 0%   208µs ± 2%   -2.76%
BM_eigen_sqrt_double/1M      866µs ± 3%   923µs ± 7%     ~
2020-12-16 18:16:11 +00:00
Antonio Sanchez
e82722a4a7 Fix MSVC SSE casts.
MSVC doesn't like __m128(__m128i) c-style casts, so packets need to be
converted using intrinsic methods.
2020-12-11 08:52:59 -08:00
Rasmus Munk Larsen
125cc9a5df Implement vectorized complex square root.
Closes #1905

Measured speedup for sqrt of `complex<float>` on Skylake:

SSE:
```
name                      old time/op             new time/op  delta
BM_eigen_sqrt_ctype/1     49.4ns ± 0%             54.3ns ± 0%  +10.01%
BM_eigen_sqrt_ctype/8      332ns ± 0%               50ns ± 1%  -84.97%
BM_eigen_sqrt_ctype/64    2.81µs ± 1%             0.38µs ± 0%  -86.49%
BM_eigen_sqrt_ctype/512   23.8µs ± 0%              3.0µs ± 0%  -87.32%
BM_eigen_sqrt_ctype/4k     202µs ± 0%               24µs ± 2%  -88.03%
BM_eigen_sqrt_ctype/32k   1.63ms ± 0%             0.19ms ± 0%  -88.18%
BM_eigen_sqrt_ctype/256k  13.0ms ± 0%              1.5ms ± 1%  -88.20%
BM_eigen_sqrt_ctype/1M    52.1ms ± 0%              6.2ms ± 0%  -88.18%
```

AVX2:
```
name                      old cpu/op  new cpu/op  delta
BM_eigen_sqrt_ctype/1     53.6ns ± 0%  55.6ns ± 0%   +3.71%
BM_eigen_sqrt_ctype/8      334ns ± 0%    27ns ± 0%  -91.86%
BM_eigen_sqrt_ctype/64    2.79µs ± 0%  0.22µs ± 2%  -92.28%
BM_eigen_sqrt_ctype/512   23.8µs ± 1%   1.7µs ± 1%  -92.81%
BM_eigen_sqrt_ctype/4k     201µs ± 0%    14µs ± 1%  -93.24%
BM_eigen_sqrt_ctype/32k   1.62ms ± 0%  0.11ms ± 1%  -93.29%
BM_eigen_sqrt_ctype/256k  13.0ms ± 0%   0.9ms ± 1%  -93.31%
BM_eigen_sqrt_ctype/1M    52.0ms ± 0%   3.5ms ± 1%  -93.31%
```

AVX512:
```
name                      old cpu/op  new cpu/op  delta
BM_eigen_sqrt_ctype/1     53.7ns ± 0%  56.2ns ± 1%   +4.75%
BM_eigen_sqrt_ctype/8      334ns ± 0%    18ns ± 2%  -94.63%
BM_eigen_sqrt_ctype/64    2.79µs ± 0%  0.12µs ± 1%  -95.54%
BM_eigen_sqrt_ctype/512   23.9µs ± 1%   1.0µs ± 1%  -95.89%
BM_eigen_sqrt_ctype/4k     202µs ± 0%     8µs ± 1%  -96.13%
BM_eigen_sqrt_ctype/32k   1.63ms ± 0%  0.06ms ± 1%  -96.15%
BM_eigen_sqrt_ctype/256k  13.0ms ± 0%   0.5ms ± 4%  -96.11%
BM_eigen_sqrt_ctype/1M    52.1ms ± 0%   2.0ms ± 1%  -96.13%
```
2020-12-08 18:13:35 -08:00
Rasmus Munk Larsen
f9fac1d5b0 Add log2() to Eigen. 2020-12-04 21:45:09 +00:00
Rasmus Munk Larsen
f23dc5b971 Revert "Add log2() operator to Eigen"
This reverts commit 4d91519a9b.
2020-12-03 14:32:45 -08:00
Rasmus Munk Larsen
4d91519a9b Add log2() operator to Eigen 2020-12-03 22:31:44 +00:00
Rasmus Munk Larsen
79818216ed Revert "Fix Half NaN definition and test."
This reverts commit c770746d70.
2020-11-24 12:57:28 -08:00
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
Antonio Sanchez
22f67b5958 Fix boolean float conversion and product warnings.
This fixes some gcc warnings such as:
```
Eigen/src/Core/GenericPacketMath.h:655:63: warning: implicit conversion turns floating-point number into bool: 'typename __gnu_cxx::__enable_if<__is_integer<bool>::__value, double>::__type' (aka 'double') to 'bool' [-Wimplicit-conversion-floating-point-to-bool]
    Packet psqrt(const Packet& a) { EIGEN_USING_STD(sqrt); return sqrt(a); }
```

Details:

- Added `scalar_sqrt_op<bool>` (`-Wimplicit-conversion-floating-point-to-bool`).

- Added `scalar_square_op<bool>` and `scalar_cube_op<bool>`
specializations (`-Wint-in-bool-context`)

- Deprecated above specialized ops for bool.

- Modified `cxx11_tensor_block_eval` to specialize generator for
booleans (`-Wint-in-bool-context`) and to use `abs` instead of `square` to
avoid deprecated bool ops.
2020-11-24 20:20:36 +00:00
Guoqiang QI
394f564055 Unify Inverse_SSE.h and Inverse_NEON.h into a single generic implementation using PacketMath. 2020-11-17 12:27:01 +00:00
Antonio Sanchez
d9f0d9eb76 Fix missing pfirst<Packet16b> for MSVC.
It was only defined under one `#ifdef` case.  This fixes the `packetmath_14`
test for MSVC.
2020-10-16 16:22:00 -07:00
Rasmus Munk Larsen
21edea5edd Fix the specialization of pfrexp for AVX to be faster when AVX2/AVX512DQ is not available, and avoid undefined behavior in C++. Also mask off the sign bit when extracting the exponent. 2020-10-15 18:39:58 -07:00
Rasmus Munk Larsen
af6f43d7ff Add specializations for pmin/pmax with prescribed NaN propagation semantics for SSE/AVX/AVX512. 2020-10-14 23:11:24 +00:00
Rasmus Munk Larsen
9078f47cd6 Fix build breakage with MSVC 2019, which does not support MMX intrinsics for 64 bit builds, see:
https://stackoverflow.com/questions/60933486/mmx-intrinsics-like-mm-cvtpd-pi32-not-found-with-msvc-2019-for-64bit-targets-c

Instead use the equivalent SSE2 intrinsics.
2020-10-01 12:37:55 -07:00
Rasmus Munk Larsen
44b9d4e412 Specialize pldexp_double and pfdexp_double and get rid of Packet2l definition for SSE. SSE does not support conversion between 64 bit integers and double and the existing implementation of casting between Packet2d and Packer2l results in undefined behavior when casting NaN to int. Since pldexp and pfdexp only manipulate exponent fields that fit in 32 bit, this change provides specializations that use existing instructions _mm_cvtpd_pi32 and _mm_cvtsi32_pd instead. 2020-09-30 13:33:44 -07:00
Rasmus Munk Larsen
74ff5719b3 Fix compilation of 64 bit constant arguments to pset1frombits in TypeCasting.h on platforms where uint64_t != unsigned long. 2020-09-28 22:47:11 +00:00
Christoph Hertzberg
6b0c0b587e Provide a more efficient Packet2l->Packet2d cast method 2020-09-28 22:14:02 +00:00
Guoqiang QI
821702e771 Fix the #issue1997 and #issue1991 bug triggered by unsupport a[index](type a: __i28d) ops with MSVC compiler 2020-09-21 15:49:00 +00:00
Rasmus Munk Larsen
c4b99f78c7 Fix breakage in pcast<Packet2l, Packet2d> due to _mm_cvtsi128_si64 not being available on 32 bit x86.
If SSE 4.1 is available use the faster _mm_extract_epi64 intrinsic.
2020-09-18 18:13:20 -07:00
guoqiangqi
9aad16b443 Fix undefined reference to pset1frombits bug on different platforms 2020-09-19 00:53:21 +00:00
Guoqiang QI
3012e755e9 Add plog ops support packet2d for NEON 2020-09-15 17:10:35 +00:00
Guoqiang QI
7c5d48f313 Unified sse pldexp_double api 2020-09-12 10:56:55 +00:00
Sheng Yang
116c5235ac BF16 for scalar_cmp_with_cast_op 2020-07-01 18:33:42 +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
fbe7916c55 Fix compilation error with Clang on Android: _mm_extract_epi64 fails to compile. 2020-04-29 00:58:41 +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
e80ec24357 Remove unused packet op "preduxp". 2020-04-23 18:17:14 +00:00
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
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