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* Support manually disabling exceptionsHEADmasterGravatar Benjamin Barenblat2021-07-07
| | | | | Rename EIGEN_EXCEPTIONS to EIGEN_USE_EXCEPTIONS, and allow disabling exceptions with -DEIGEN_USE_EXCEPTIONS=0.
* Fix Tensor documentation page.Gravatar Antonio Sanchez2021-07-03
| | | | | | | | | | | | | | | | | | | | | | | | | The extra [TOC] tag is generating a huge floating duplicated table-of-contents, which obscures the majority of the page (see bottom of https://eigen.tuxfamily.org/dox/unsupported/eigen_tensors.html). Remove it. Also, headers do not support markup (see [doxygen bug](https://github.com/doxygen/doxygen/issues/7467)), so backticks like ``` ``` end up generating titles that looks like ``` Constructor <tt>Tensor<double,2></tt> ``` Removing backticks for now. To generate proper formatted headers, we must directly use html instead of markdown, i.e. ``` <h2>Constructor <code>Tensor&lt;double,2&gt;</code></h2> ``` which is ugly. Fixes #2254.
* Don't crash when attempting to shuffle an empty tensor.Gravatar Jonas Harsch2021-07-02
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* Fix compile issues for gcc 4.8.Gravatar Antonio Sanchez2021-07-01
| | | | | | - Move constructors can only be defaulted as NOEXCEPT if all members have NOEXCEPT move constructors. - gcc 4.8 has some funny parsing bug in `a < b->c`, thinking `b-` is a template parameter.
* Modify tensor argmin/argmax to always return first occurence.Gravatar Antonio Sanchez2021-06-29
| | | | | | | | | As written, depending on multithreading/gpu, the returned index from `argmin`/`argmax` is not currently stable. Here we modify the functors to always keep the first occurence (i.e. if the value is equal to the current min/max, then keep the one with the smallest index). This is otherwise causing unpredictable results in some TF tests.
* changed documentation to make example compileGravatar jenswehner2021-06-16
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* Fix placement of permanent GPU defines.Gravatar Antonio Sanchez2021-06-15
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* Fix more enum arithmetic.Gravatar Rasmus Munk Larsen2021-06-15
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* Add ability to permanently enable HIP/CUDA gpu* defines.Gravatar Antonio Sanchez2021-06-11
| | | | | | When using Eigen for gpu, these simplify portability. If `EIGEN_PERMANENTLY_ENABLE_GPU_HIP_CUDA_DEFINES` is set, then we do not undefine them.
* Allow custom TENSOR_CONTRACTION_DISPATCH macro.Gravatar Antonio Sanchez2021-06-11
| | | | | | Currently TF lite needs to hack around with the Tensor headers in order to customize the contraction dispatch method. Here we add simple `#ifndef` guards to allow them to provide their own dispatch prior to inclusion.
* Fix calls to device functions from host codeGravatar Nathan Luehr2021-05-11
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* Clean up gpu device properties.Gravatar Antonio Sanchez2021-05-07
| | | | | | | | Made a class and singleton to encapsulate initialization and retrieval of device properties. Related to !481, which already changed the API to address a static linkage issue.
* Simplify TensorRandom and remove time-dependence.Gravatar Antonio Sanchez2021-05-04
| | | | | | | | | | | | | | | | | | | | Time-dependence prevents tests from being repeatable. This has long been an issue with debugging the tensor tests. Removing this will allow future tests to be repeatable in the usual way. Also, the recently added macros in !476 are causing headaches across different platforms. For example, checking `_XOPEN_SOURCE` is leading to multiple ambiguous macro errors across Google, and `_DEFAULT_SOURCE`/`_SVID_SOURCE`/`_BSD_SOURCE` are sometimes defined with values, sometimes defined as empty, and sometimes not defined at all when they probably should be. This is leading to multiple build breakages. The simplest approach is to generate a seed via `Eigen::internal::random<uint64_t>()` if on CPU. For GPU, we use a hash based on the current thread ID (since `rand()` isn't supported on GPU). Fixes #1602.
* Fix for issue with static global variables in TensorDeviceGpu.hGravatar Turing Eret2021-04-23
| | | | | | | | | | | | | | m_deviceProperties and m_devicePropInitialized are defined as global statics which will define multiple copies which can cause issues if initializeDeviceProp() is called in one translation unit and then m_deviceProperties is used in a different translation unit. Added inline functions getDeviceProperties() and getDevicePropInitialized() which defines those variables as static locals. As per the C++ standard 7.1.2/4, a static local declared in an inline function always refers to the same object, so this should be safer. Credit to Sun Chenggen for this fix. This fixes issue #1475.
* Check existence of BSD random before use.Gravatar Antonio Sanchez2021-04-22
| | | | | | | | | | | | | `TensorRandom` currently relies on BSD `random()`, which is not always available. The [linux manpage](https://man7.org/linux/man-pages/man3/srandom.3.html) gives the glibc condition: ``` _XOPEN_SOURCE >= 500 || /* Glibc since 2.19: */ _DEFAULT_SOURCE || /* Glibc <= 2.19: */ _SVID_SOURCE || _BSD_SOURCE ``` In particular, this was failing to compile for MinGW via msys2. If not available, we fall back to using `rand()`.
* Fix typo in TensorDimensions.hGravatar Rasmus Munk Larsen2021-04-12
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* This fixes an issue where the compiler was not choosing the GPU specific ↵Gravatar Rohit Santhanam2021-04-08
| | | | | | | | | | | | | | specialization of ScanLauncher. The issue was discovered when the GPU scan unit test was run and resulted in a segmentation fault. The segmantation fault occurred because the unit test allocated GPU memory and passed a pointer to that memory to the computation that it presumed would execute on the GPU. But because of the issue, the computation was scheduled to execute on the CPU so a situation was constructed where the CPU attempted to access a GPU memory location. The fix expands the GPU specific ScanLauncher specialization to handle cases where vectorization is enabled. Previously, the GPU specialization is chosen only if Vectorization is not used.
* Re-implement move assignments.Gravatar Antonio Sanchez2021-03-10
| | | | | | | | | | | | | | | The original swap approach leads to potential undefined behavior (reading uninitialized memory) and results in unnecessary copying of data for static storage. Here we pass down the move assignment to the underlying storage. Static storage does a one-way copy, dynamic storage does a swap. Modified the tests to no longer read from the moved-from matrix/tensor, since that can lead to UB. Added a test to ensure we do not access uninitialized memory in a move. Fixes: #2119
* Define EIGEN_CPLUSPLUS and replace most __cplusplus checks.Gravatar Antonio Sanchez2021-03-05
| | | | | | | | | | | | | | | The macro `__cplusplus` is not defined correctly in MSVC unless building with the the `/Zc:__cplusplus` flag. Instead, it defines `_MSVC_LANG` to the specified c++ standard version number. Here we introduce `EIGEN_CPLUSPLUS` which will contain the c++ version number both for MSVC and otherwise. This simplifies checks for supported features. Also replaced most instances of standard version checking via `__cplusplus` with the existing `EIGEN_COMP_CXXVER` macro for better clarity. Fixes: #2170
* Add log2 operation to TensorBaseGravatar Eugene Zhulenev2021-03-04
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* Inherit from `no_assignment_operator` to avoid implicit copy constructor ↵Gravatar Christoph Hertzberg2021-02-27
| | | | | | warnings (cherry picked from commit 9bbb7ea4b54b1f307863be4ed8d105c38cdefe50)
* Don't crash when attempting to slice an empty tensor.Gravatar Rasmus Munk Larsen2021-02-24
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* Include `<cstdint>` in one place, remove custom typedefsGravatar Antonio Sanchez2021-01-26
| | | | | | | | | | | | | | Originating from [this SO issue](https://stackoverflow.com/questions/65901014/how-to-solve-this-all-error-2-in-this-case), some win32 compilers define `__int32` as a `long`, but MinGW defines `std::int32_t` as an `int`, leading to a type conflict. To avoid this, we remove the custom `typedef` definitions for win32. The Tensor module requires C++11 anyways, so we are guaranteed to have included `<cstdint>` already in `Eigen/Core`. Also re-arranged the headers to only include `<cstdint>` in one place to avoid this type of error again.
* fix paddings of TensorVolumePatchOpGravatar Maozhou, Ge2021-01-15
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* Add CUDA complex sqrt.Gravatar Antonio Sanchez2020-12-22
| | | | | | | | | | | | | | | This is to support scalar `sqrt` of complex numbers `std::complex<T>` on device, requested by Tensorflow folks. Technically `std::complex` is not supported by NVCC on device (though it is by clang), so the default `sqrt(std::complex<T>)` function only works on the host. Here we create an overload to add back the functionality. Also modified the CMake file to add `--relaxed-constexpr` (or equivalent) flag for NVCC to allow calling constexpr functions from device functions, and added support for specifying compute architecture for NVCC (was already available for clang).
* Replace call to FixedDimensions() with a singleton instance ofGravatar Turing Eret2020-12-16
| | | | FixedDimensions.
* TensorStorage with FixedDimensions now has zero instance memory overhead.Gravatar Turing Eret2020-12-14
| | | | | | | Removed m_dimension as instance member of TensorStorage with FixedDimensions and instead use the template parameter. This means that the sizeof a pure fixed-size storage is exactly equal to the data it is storing.
* Clean up the Tensor header and get rid of the EIGEN_SLEEP macro.Gravatar Rasmus Munk Larsen2020-12-02
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* Add bit_cast for half/bfloat to/from uint16_t, fix TensorRandomGravatar Antonio Sanchez2020-11-18
| | | | | | | | | | The existing `TensorRandom.h` implementation makes the assumption that `half` (`bfloat16`) has a `uint16_t` member `x` (`value`), which is not always true. This currently fails on arm64, where `x` has type `__fp16`. Added `bit_cast` specializations to allow casting to/from `uint16_t` for both `half` and `bfloat16`. Also added tests in `half_float`, `bfloat16_float`, and `cxx11_tensor_random` to catch these errors in the future.
* Fix rule-of-3 for the Tensor module.Gravatar Antonio Sanchez2020-11-18
| | | | | | | Adds copy constructors to Tensor ops, inherits assignment operators from `TensorBase`. Addresses #1863
* [SYCL clean up the code] : removing exrta #pragma unroll in SYCL which was ↵Gravatar mehdi-goli2020-10-28
| | | | causing issues in embeded systems
* Get rid of nested template specialization in TensorReductionGpu.h, which was ↵Gravatar Rasmus Munk Larsen2020-10-13
| | | | broken by c6953f799b01d36f4236b64f351cc1446e0abe17.
* Add packet generic ops `predux_fmin`, `predux_fmin_nan`, `predux_fmax`, and ↵Gravatar Rasmus Munk Larsen2020-10-13
| | | | `predux_fmax_nan` that implement reductions with `PropagateNaN`, and `PropagateNumbers` semantics. Add (slow) generic implementations for most reductions.
* Don't make assumptions about NaN-propagation for pmin/pmax - it various ↵Gravatar Rasmus Munk Larsen2020-10-07
| | | | | | across platforms. Change test to only test for NaN-propagation for pfmin/pfmax.
* Fix Eigen::ThreadPool::CurrentThreadId returning wrong thread id when ↵Gravatar Zhuyie2020-09-25
| | | | EIGEN_AVOID_THREAD_LOCAL and NDEBUG are defined
* Get rid of initialization logic for blueNorm by making the computed ↵Gravatar Rasmus Munk Larsen2020-09-18
| | | | | | constants static const or constexpr. Move macro definition EIGEN_CONSTEXPR to Core and make all methods in NumTraits constexpr when EIGEN_HASH_CONSTEXPR is 1.
* Fixing a CUDA / P100 regression introduced by PR 181Gravatar Deven Desai2020-08-20
| | | | | | PR 181 ( https://gitlab.com/libeigen/eigen/-/merge_requests/181 ) adds `__launch_bounds__(1024)` attribute to GPU kernels, that did not have that attribute explicitly specified. That PR seems to cause regressions on the CUDA platform. This PR/commit makes the changes in PR 181, to be applicable for HIP only
* Adding an explicit launch_bounds(1024) attribute for GPU kernels.Gravatar Deven Desai2020-08-05
| | | | | | | | | | Starting with ROCm 3.5, the HIP compiler will change from HCC to hip-clang. This compiler change introduce a change in the default value of the `__launch_bounds__` attribute associated with a GPU kernel. (default value means the value assumed by the compiler as the `__launch_bounds attribute__` value, when it is not explicitly specified by the user) Currently (i.e. for HIP with ROCm 3.3 and older), the default value is 1024. That changes to 256 with ROCm 3.5 (i.e. hip-clang compiler). As a consequence of this change, if a GPU kernel with a `__luanch_bounds__` attribute of 256 is launched at runtime with a threads_per_block value > 256, it leads to a runtime error. This is leading to a couple of Eigen unit test failures with ROCm 3.5. This commit adds an explicit `__launch_bounds(1024)__` attribute to every GPU kernel that currently does not have it explicitly specified (and hence will end up getting the default value of 256 with the change to hip-clang)
* Inherit alignment trait from argument in TensorBroadcasting to avoid ↵Gravatar Rasmus Munk Larsen2020-07-28
| | | | segfault when the argument is unaligned.
* Fix tensor casts for large packets and casts to/from std::complexGravatar Antonio Sanchez2020-06-30
| | | | | | | | | | | | | The original tensor casts were only defined for `SrcCoeffRatio`:`TgtCoeffRatio` 1:1, 1:2, 2:1, 4:1. Here we add the missing 1:N and 8:1. We also add casting `Eigen::half` to/from `std::complex<T>`, which was missing to make it consistent with `Eigen:bfloat16`, and generalize the overload to work for any complex type. Tests were added to `basicstuff`, `packetmath`, and `cxx11_tensor_casts` to test all cast configurations.
* Support BFloat16 in EigenGravatar Teng Lu2020-06-20
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* Run two independent chains, when reducing tensors.Gravatar Ilya Tokar2020-06-16
| | | | | | | | | | | | | | | | | | | | | | | | | | Running two chains exposes more instruction level parallelism, by allowing to execute both chains at the same time. Results are a bit noisy, but for medium length we almost hit theoretical upper bound of 2x. BM_fullReduction_16T/3 [using 16 threads] 17.3ns ±11% 17.4ns ± 9% ~ (p=0.178 n=18+19) BM_fullReduction_16T/4 [using 16 threads] 17.6ns ±17% 17.0ns ±18% ~ (p=0.835 n=20+19) BM_fullReduction_16T/7 [using 16 threads] 18.9ns ±12% 18.2ns ±10% ~ (p=0.756 n=20+18) BM_fullReduction_16T/8 [using 16 threads] 19.8ns ±13% 19.4ns ±21% ~ (p=0.512 n=20+20) BM_fullReduction_16T/10 [using 16 threads] 23.5ns ±15% 20.8ns ±24% -11.37% (p=0.000 n=20+19) BM_fullReduction_16T/15 [using 16 threads] 35.8ns ±21% 26.9ns ±17% -24.76% (p=0.000 n=20+19) BM_fullReduction_16T/16 [using 16 threads] 38.7ns ±22% 27.7ns ±18% -28.40% (p=0.000 n=20+19) BM_fullReduction_16T/31 [using 16 threads] 146ns ±17% 74ns ±11% -49.05% (p=0.000 n=20+18) BM_fullReduction_16T/32 [using 16 threads] 154ns ±19% 84ns ±30% -45.79% (p=0.000 n=20+19) BM_fullReduction_16T/64 [using 16 threads] 603ns ± 8% 308ns ±12% -48.94% (p=0.000 n=17+17) BM_fullReduction_16T/128 [using 16 threads] 2.44µs ±13% 1.22µs ± 1% -50.29% (p=0.000 n=17+17) BM_fullReduction_16T/256 [using 16 threads] 9.84µs ±14% 5.13µs ±30% -47.82% (p=0.000 n=19+19) BM_fullReduction_16T/512 [using 16 threads] 78.0µs ± 9% 56.1µs ±17% -28.02% (p=0.000 n=18+20) BM_fullReduction_16T/1k [using 16 threads] 325µs ± 5% 263µs ± 4% -19.00% (p=0.000 n=20+16) BM_fullReduction_16T/2k [using 16 threads] 1.09ms ± 3% 0.99ms ± 1% -9.04% (p=0.000 n=20+20) BM_fullReduction_16T/4k [using 16 threads] 7.66ms ± 3% 7.57ms ± 3% -1.24% (p=0.017 n=20+20) BM_fullReduction_16T/10k [using 16 threads] 65.3ms ± 4% 65.0ms ± 3% ~ (p=0.718 n=20+20)
* Eigen moved the `scanLauncehr` function inside the internal namespace.Gravatar mehdi-goli2020-05-11
| | | | | | | This commit applies the following changes: - Moving the `scamLauncher` specialization inside internal namespace to fix compiler crash on TensorScan for SYCL backend. - Replacing `SYCL/sycl.hpp` to `CL/sycl.hpp` in order to follow SYCL 1.2.1 standard. - minor fixes: commenting out an unused variable to avoid compiler warnings.
* Add parallelization of TensorScanOp for types without packet ops.Gravatar Rasmus Munk Larsen2020-05-06
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Clean up the code a bit and do a few micro-optimizations to improve performance for small tensors. Benchmark numbers for Tensor<uint32_t>: name old time/op new time/op delta BM_cumSumRowReduction_1T/8 [using 1 threads] 76.5ns ± 0% 61.3ns ± 4% -19.80% (p=0.008 n=5+5) BM_cumSumRowReduction_1T/64 [using 1 threads] 2.47µs ± 1% 2.40µs ± 1% -2.77% (p=0.008 n=5+5) BM_cumSumRowReduction_1T/256 [using 1 threads] 39.8µs ± 0% 39.6µs ± 0% -0.60% (p=0.008 n=5+5) BM_cumSumRowReduction_1T/4k [using 1 threads] 13.9ms ± 0% 13.4ms ± 1% -4.19% (p=0.008 n=5+5) BM_cumSumRowReduction_2T/8 [using 2 threads] 76.8ns ± 0% 59.1ns ± 0% -23.09% (p=0.016 n=5+4) BM_cumSumRowReduction_2T/64 [using 2 threads] 2.47µs ± 1% 2.41µs ± 1% -2.53% (p=0.008 n=5+5) BM_cumSumRowReduction_2T/256 [using 2 threads] 39.8µs ± 0% 34.7µs ± 6% -12.74% (p=0.008 n=5+5) BM_cumSumRowReduction_2T/4k [using 2 threads] 13.8ms ± 1% 7.2ms ± 6% -47.74% (p=0.008 n=5+5) BM_cumSumRowReduction_8T/8 [using 8 threads] 76.4ns ± 0% 61.8ns ± 3% -19.02% (p=0.008 n=5+5) BM_cumSumRowReduction_8T/64 [using 8 threads] 2.47µs ± 1% 2.40µs ± 1% -2.84% (p=0.008 n=5+5) BM_cumSumRowReduction_8T/256 [using 8 threads] 39.8µs ± 0% 28.3µs ±11% -28.75% (p=0.008 n=5+5) BM_cumSumRowReduction_8T/4k [using 8 threads] 13.8ms ± 0% 2.7ms ± 5% -80.39% (p=0.008 n=5+5) BM_cumSumColReduction_1T/8 [using 1 threads] 59.1ns ± 0% 80.3ns ± 0% +35.94% (p=0.029 n=4+4) BM_cumSumColReduction_1T/64 [using 1 threads] 3.06µs ± 0% 3.08µs ± 1% ~ (p=0.114 n=4+4) BM_cumSumColReduction_1T/256 [using 1 threads] 175µs ± 0% 176µs ± 0% ~ (p=0.190 n=4+5) BM_cumSumColReduction_1T/4k [using 1 threads] 824ms ± 1% 844ms ± 1% +2.37% (p=0.008 n=5+5) BM_cumSumColReduction_2T/8 [using 2 threads] 59.0ns ± 0% 90.7ns ± 0% +53.74% (p=0.029 n=4+4) BM_cumSumColReduction_2T/64 [using 2 threads] 3.06µs ± 0% 3.10µs ± 0% +1.08% (p=0.016 n=4+5) BM_cumSumColReduction_2T/256 [using 2 threads] 176µs ± 0% 189µs ±18% ~ (p=0.151 n=5+5) BM_cumSumColReduction_2T/4k [using 2 threads] 836ms ± 2% 611ms ±14% -26.92% (p=0.008 n=5+5) BM_cumSumColReduction_8T/8 [using 8 threads] 59.3ns ± 2% 90.6ns ± 0% +52.79% (p=0.008 n=5+5) BM_cumSumColReduction_8T/64 [using 8 threads] 3.07µs ± 0% 3.10µs ± 0% +0.99% (p=0.016 n=5+4) BM_cumSumColReduction_8T/256 [using 8 threads] 176µs ± 0% 80µs ±19% -54.51% (p=0.008 n=5+5) BM_cumSumColReduction_8T/4k [using 8 threads] 827ms ± 2% 180ms ±14% -78.24% (p=0.008 n=5+5)
* Fix accidental copy of loop variable.Gravatar Rasmus Munk Larsen2020-05-05
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* Vectorize and parallelize TensorScanOp.Gravatar Rasmus Munk Larsen2020-05-05
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | TensorScanOp is used in TensorFlow for a number of operations, such as cumulative logexp reduction and cumulative sum and product reductions. The benchmarks numbers below are for cumulative row- and column reductions of NxN matrices. name old time/op new time/op delta BM_cumSumRowReduction_1T/4 [using 1 threads ] 25.1ns ± 1% 35.2ns ± 1% +40.45% BM_cumSumRowReduction_1T/8 [using 1 threads ] 73.4ns ± 0% 82.7ns ± 3% +12.74% BM_cumSumRowReduction_1T/32 [using 1 threads ] 988ns ± 0% 832ns ± 0% -15.77% BM_cumSumRowReduction_1T/64 [using 1 threads ] 4.07µs ± 2% 3.47µs ± 0% -14.70% BM_cumSumRowReduction_1T/128 [using 1 threads ] 18.0µs ± 0% 16.8µs ± 0% -6.58% BM_cumSumRowReduction_1T/512 [using 1 threads ] 287µs ± 0% 281µs ± 0% -2.22% BM_cumSumRowReduction_1T/2k [using 1 threads ] 4.78ms ± 1% 4.78ms ± 2% ~ BM_cumSumRowReduction_1T/10k [using 1 threads ] 117ms ± 1% 117ms ± 1% ~ BM_cumSumRowReduction_8T/4 [using 8 threads ] 25.0ns ± 0% 35.2ns ± 0% +40.82% BM_cumSumRowReduction_8T/8 [using 8 threads ] 77.2ns ±16% 81.3ns ± 0% ~ BM_cumSumRowReduction_8T/32 [using 8 threads ] 988ns ± 0% 833ns ± 0% -15.67% BM_cumSumRowReduction_8T/64 [using 8 threads ] 4.08µs ± 2% 3.47µs ± 0% -14.95% BM_cumSumRowReduction_8T/128 [using 8 threads ] 18.0µs ± 0% 17.3µs ±10% ~ BM_cumSumRowReduction_8T/512 [using 8 threads ] 287µs ± 0% 58µs ± 6% -79.92% BM_cumSumRowReduction_8T/2k [using 8 threads ] 4.79ms ± 1% 0.64ms ± 1% -86.58% BM_cumSumRowReduction_8T/10k [using 8 threads ] 117ms ± 1% 18ms ± 6% -84.50% BM_cumSumColReduction_1T/4 [using 1 threads ] 23.9ns ± 0% 33.4ns ± 1% +39.68% BM_cumSumColReduction_1T/8 [using 1 threads ] 71.6ns ± 1% 49.1ns ± 3% -31.40% BM_cumSumColReduction_1T/32 [using 1 threads ] 973ns ± 0% 165ns ± 2% -83.10% BM_cumSumColReduction_1T/64 [using 1 threads ] 4.06µs ± 1% 0.57µs ± 1% -85.94% BM_cumSumColReduction_1T/128 [using 1 threads ] 33.4µs ± 1% 4.1µs ± 1% -87.67% BM_cumSumColReduction_1T/512 [using 1 threads ] 1.72ms ± 4% 0.21ms ± 5% -87.91% BM_cumSumColReduction_1T/2k [using 1 threads ] 119ms ±53% 11ms ±35% -90.42% BM_cumSumColReduction_1T/10k [using 1 threads ] 1.59s ±67% 0.35s ±49% -77.96% BM_cumSumColReduction_8T/4 [using 8 threads ] 23.8ns ± 0% 33.3ns ± 0% +40.06% BM_cumSumColReduction_8T/8 [using 8 threads ] 71.6ns ± 1% 49.2ns ± 5% -31.33% BM_cumSumColReduction_8T/32 [using 8 threads ] 1.01µs ±12% 0.17µs ± 3% -82.93% BM_cumSumColReduction_8T/64 [using 8 threads ] 4.15µs ± 4% 0.58µs ± 1% -86.09% BM_cumSumColReduction_8T/128 [using 8 threads ] 33.5µs ± 0% 4.1µs ± 4% -87.65% BM_cumSumColReduction_8T/512 [using 8 threads ] 1.71ms ± 3% 0.06ms ±16% -96.21% BM_cumSumColReduction_8T/2k [using 8 threads ] 97.1ms ±14% 3.0ms ±23% -96.88% BM_cumSumColReduction_8T/10k [using 8 threads ] 1.97s ± 8% 0.06s ± 2% -96.74%
* Extend support for Packet16b:Gravatar Rasmus Munk Larsen2020-04-28
| | | | | | | | | | | | | | | | | * Add ptranspose<*,4> to support matmul and add unit test for Matrix<bool> * Matrix<bool> * work around a bug in slicing of Tensor<bool>. * Add tensor tests This speeds up matmul for boolean matrices by about 10x name old time/op new time/op delta BM_MatMul<bool>/8 267ns ± 0% 479ns ± 0% +79.25% (p=0.008 n=5+5) BM_MatMul<bool>/32 6.42µs ± 0% 0.87µs ± 0% -86.50% (p=0.008 n=5+5) BM_MatMul<bool>/64 43.3µs ± 0% 5.9µs ± 0% -86.42% (p=0.008 n=5+5) BM_MatMul<bool>/128 315µs ± 0% 44µs ± 0% -85.98% (p=0.008 n=5+5) BM_MatMul<bool>/256 2.41ms ± 0% 0.34ms ± 0% -85.68% (p=0.008 n=5+5) BM_MatMul<bool>/512 18.8ms ± 0% 2.7ms ± 0% -85.53% (p=0.008 n=5+5) BM_MatMul<bool>/1k 149ms ± 0% 22ms ± 0% -85.40% (p=0.008 n=5+5)
* Add async evaluation support to TensorSlicingOp.Gravatar Eugene Zhulenev2020-04-22
| | | Device::memcpy is not async-safe and might lead to deadlocks. Always evaluate slice expression in async mode.
* Fix a bug in TensorIndexList.hGravatar Changming Sun2020-04-13
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* Resolve C4346 when building eigen on windowsGravatar jangsoopark2020-04-08
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