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CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
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CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
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CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
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CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
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CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
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CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
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CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
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CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
<td id="projectlogo"><img alt="Logo" src="cutlass-logo-small.png"/></td>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
<td id="projectalign" style="padding-left: 0.5em;">
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
<div id="projectname">CUTLASS
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</div>
<div id="projectbrief">CUDA Templates for Linear Algebra Subroutines and Solvers</div>
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CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
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CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
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CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
<div id="navrow1" class="tabs">
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<li><a href="index.html"><span>Main&#160;Page</span></a></li>
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<li><a href="annotated.html"><span>Class&#160;List</span></a></li>
<li><a href="classes.html"><span>Class&#160;Index</span></a></li>
<li><a href="inherits.html"><span>Class&#160;Hierarchy</span></a></li>
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<li class="current"><a href="functions.html"><span>All</span></a></li>
<li><a href="functions_func.html"><span>Functions</span></a></li>
<li><a href="functions_vars.html"><span>Variables</span></a></li>
<li><a href="functions_type.html"><span>Typedefs</span></a></li>
<li><a href="functions_enum.html"><span>Enumerations</span></a></li>
<li><a href="functions_eval.html"><span>Enumerator</span></a></li>
</ul>
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<div id="navrow4" class="tabs3">
<ul class="tablist">
<li><a href="functions.html#index_a"><span>a</span></a></li>
<li><a href="functions_b.html#index_b"><span>b</span></a></li>
<li class="current"><a href="functions_c.html#index_c"><span>c</span></a></li>
<li><a href="functions_d.html#index_d"><span>d</span></a></li>
<li><a href="functions_e.html#index_e"><span>e</span></a></li>
<li><a href="functions_f.html#index_f"><span>f</span></a></li>
<li><a href="functions_g.html#index_g"><span>g</span></a></li>
<li><a href="functions_h.html#index_h"><span>h</span></a></li>
<li><a href="functions_i.html#index_i"><span>i</span></a></li>
<li><a href="functions_k.html#index_k"><span>k</span></a></li>
<li><a href="functions_l.html#index_l"><span>l</span></a></li>
<li><a href="functions_m.html#index_m"><span>m</span></a></li>
<li><a href="functions_n.html#index_n"><span>n</span></a></li>
<li><a href="functions_o.html#index_o"><span>o</span></a></li>
<li><a href="functions_p.html#index_p"><span>p</span></a></li>
<li><a href="functions_q.html#index_q"><span>q</span></a></li>
<li><a href="functions_r.html#index_r"><span>r</span></a></li>
<li><a href="functions_s.html#index_s"><span>s</span></a></li>
<li><a href="functions_t.html#index_t"><span>t</span></a></li>
<li><a href="functions_u.html#index_u"><span>u</span></a></li>
<li><a href="functions_v.html#index_v"><span>v</span></a></li>
<li><a href="functions_w.html#index_w"><span>w</span></a></li>
<li><a href="functions_y.html#index_y"><span>y</span></a></li>
<li><a href="functions_0x7e.html#index_0x7e"><span>~</span></a></li>
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CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</div><!-- top -->
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<div class="textblock">Here is a list of all class members with links to the classes they belong to:</div>
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
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<h3><a class="anchor" id="index_c"></a>- c -</h3><ul>
Updated doxygen
2018-09-20 05:02:08 +08:00
<li>C
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
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: <a class="el" href="structcutlass_1_1library_1_1GemmArguments.html#a55d2e1d991fa6cfee6c8a4f3b71cbdd9">cutlass::library::GemmArguments</a>
, <a class="el" href="structcutlass_1_1library_1_1GemmArrayArguments.html#a49928c947bec63af706d31f51fb10fd9">cutlass::library::GemmArrayArguments</a>
, <a class="el" href="structcutlass_1_1library_1_1GemmDescription.html#adb43e15d8d870f6a4fca038a401125e7">cutlass::library::GemmDescription</a>
Updated doxygen
2018-09-20 05:02:08 +08:00
</li>
<li>c()
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
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: <a class="el" href="structcutlass_1_1Tensor4DCoord.html#ab0f58e5f54b42534fca77a662c78c7ad">cutlass::Tensor4DCoord</a>
</li>
<li>can_implement()
: <a class="el" href="classcutlass_1_1gemm_1_1device_1_1Gemm.html#a40ad889da7ff420fd9f9000cd9f98e32">cutlass::gemm::device::Gemm&lt; ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_, LayoutC_, ElementAccumulator_, OperatorClass_, ArchTag_, ThreadblockShape_, WarpShape_, InstructionShape_, EpilogueOutputOp_, ThreadblockSwizzle_, Stages, AlignmentA, AlignmentB, SplitKSerial, Operator_, IsBetaZero &gt;</a>
, <a class="el" href="classcutlass_1_1gemm_1_1device_1_1Gemm_3_01ElementA___00_01LayoutA___00_01ElementB___00_01Layout4d0960ae6b1d1bf19e6239dbd002249c.html#a662bcbcb6164c803ab490c86e69b9ee1">cutlass::gemm::device::Gemm&lt; ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_, layout::ColumnMajor, ElementAccumulator_, OperatorClass_, ArchTag_, ThreadblockShape_, WarpShape_, InstructionShape_, EpilogueOutputOp_, ThreadblockSwizzle_, Stages, AlignmentA, AlignmentB, SplitKSerial, Operator_, IsBetaZero &gt;</a>
, <a class="el" href="classcutlass_1_1gemm_1_1device_1_1GemmBatched.html#acb4d53fbea4366349574091d68594558">cutlass::gemm::device::GemmBatched&lt; ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_, LayoutC_, ElementAccumulator_, OperatorClass_, ArchTag_, ThreadblockShape_, WarpShape_, InstructionShape_, EpilogueOutputOp_, ThreadblockSwizzle_, Stages, AlignmentA, AlignmentB, Operator_ &gt;</a>
, <a class="el" href="classcutlass_1_1gemm_1_1device_1_1GemmBatched_3_01ElementA___00_01LayoutA___00_01ElementB___00_0c9bb6f4463ab6085e6008b5d5ad6abfd.html#abbd82c0f989a9d07e5e222db96386701">cutlass::gemm::device::GemmBatched&lt; ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_, layout::ColumnMajor, ElementAccumulator_, OperatorClass_, ArchTag_, ThreadblockShape_, WarpShape_, InstructionShape_, EpilogueOutputOp_, ThreadblockSwizzle_, Stages, AlignmentA, AlignmentB, Operator_ &gt;</a>
, <a class="el" href="classcutlass_1_1gemm_1_1device_1_1GemmComplex.html#aa94353c3aa13b38f0e859070edf61c6e">cutlass::gemm::device::GemmComplex&lt; ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_, LayoutC_, ElementAccumulator_, OperatorClass_, ArchTag_, ThreadblockShape_, WarpShape_, InstructionShape_, EpilogueOutputOp_, ThreadblockSwizzle_, Stages, TransformA, TransformB, SplitKSerial &gt;</a>
, <a class="el" href="classcutlass_1_1gemm_1_1device_1_1GemmComplex_3_01ElementA___00_01LayoutA___00_01ElementB___00_07c56401b4df75709ae636675d9980a9a.html#adb94d2e6dd70b46bea6b5b433e14fea9">cutlass::gemm::device::GemmComplex&lt; ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_, layout::ColumnMajor, ElementAccumulator_, OperatorClass_, ArchTag_, ThreadblockShape_, WarpShape_, InstructionShape_, EpilogueOutputOp_, ThreadblockSwizzle_, Stages, TransformA, TransformB, SplitKSerial &gt;</a>
, <a class="el" href="classcutlass_1_1gemm_1_1device_1_1GemmSplitKParallel.html#a114b122602b425909f9be0df461353a4">cutlass::gemm::device::GemmSplitKParallel&lt; ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_, LayoutC_, ElementAccumulator_, OperatorClass_, ArchTag_, ThreadblockShape_, WarpShape_, InstructionShape_, EpilogueOutputOp_, ConvertScaledOp_, ReductionOp_, ThreadblockSwizzle_, Stages, kAlignmentA, kAlignmentB, Operator_ &gt;</a>
, <a class="el" href="classcutlass_1_1gemm_1_1device_1_1GemmSplitKParallel_3_01ElementA___00_01LayoutA___00_01ElementBbe7c1f7154ad5b5bf9d4d28301e2b457.html#a465591fbfde2a9aa6330d9adcbf82bd6">cutlass::gemm::device::GemmSplitKParallel&lt; ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_, layout::ColumnMajor, ElementAccumulator_, OperatorClass_, ArchTag_, ThreadblockShape_, WarpShape_, InstructionShape_, EpilogueOutputOp_, ConvertScaledOp_, ReductionOp_, ThreadblockSwizzle_, Stages, kAlignmentA, kAlignmentB, Operator_ &gt;</a>
, <a class="el" href="structcutlass_1_1gemm_1_1kernel_1_1Gemm.html#afa50b807bd445330e9f3a55d664008c9">cutlass::gemm::kernel::Gemm&lt; Mma_, Epilogue_, ThreadblockSwizzle_, SplitKSerial &gt;</a>
, <a class="el" href="classcutlass_1_1library_1_1Operation.html#a36bef483ad4a6c1d5bd426e134f16538">cutlass::library::Operation</a>
</li>
<li>capacity
: <a class="el" href="structcutlass_1_1device__memory_1_1allocation.html#a81d1c8ae7ffc695ae1e6a190ebfe8bb6">cutlass::device_memory::allocation&lt; T &gt;</a>
, <a class="el" href="classcutlass_1_1HostTensor.html#aa6ea111e8fcba15c07f0cf679e1eec7f">cutlass::HostTensor&lt; Element_, Layout_ &gt;</a>
, <a class="el" href="classcutlass_1_1IdentityTensorLayout.html#a3dc530520b5eb35bc57c75de7954b59f">cutlass::IdentityTensorLayout&lt; Rank &gt;</a>
, <a class="el" href="classcutlass_1_1layout_1_1ColumnMajor.html#ae0ede39a3011be136c9033327935170d">cutlass::layout::ColumnMajor</a>
, <a class="el" href="structcutlass_1_1layout_1_1ColumnMajorBlockLinear.html#afa68813c9946c24aa27eae56848a52e6">cutlass::layout::ColumnMajorBlockLinear&lt; BlockRows, BlockColumns &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1ColumnMajorInterleaved.html#a8233dbf6ef04491c79780bf27893f3e1">cutlass::layout::ColumnMajorInterleaved&lt; Interleave &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1ColumnMajorTensorOpMultiplicandCongruous.html#a789b0744a97926b5447be3861f184122">cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous&lt; ElementSize, Crosswise &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1ColumnMajorTensorOpMultiplicandCrosswise.html#afeba15c02217a7e06c3cb96c7bef2bd0">cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise&lt; ElementSize, Crosswise &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1ColumnMajorVoltaTensorOpMultiplicandBCongruous.html#a9ac9d347fb65719c8a05295816120fec">cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandBCongruous&lt; ElementSize &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1ColumnMajorVoltaTensorOpMultiplicandCongruous.html#ae616638f7e2dc315865b3693f71f52cd">cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous&lt; ElementSize &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1ColumnMajorVoltaTensorOpMultiplicandCrosswise.html#a1c709df04e3d4693707dc30b6c1f08f5">cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCrosswise&lt; ElementSize, KBlock &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1ContiguousMatrix.html#af7266ef6f5ba58567103e8792cb0484b">cutlass::layout::ContiguousMatrix</a>
, <a class="el" href="structcutlass_1_1layout_1_1GeneralMatrix.html#a435e6e78b25296cd7ccaada3dcd4e16b">cutlass::layout::GeneralMatrix</a>
, <a class="el" href="classcutlass_1_1layout_1_1PackedVectorLayout.html#a20b384f56d697894a75c8f4693155320">cutlass::layout::PackedVectorLayout</a>
, <a class="el" href="classcutlass_1_1layout_1_1PitchLinear.html#a3fb2016836e011e8c77a4b3fbb3e51d5">cutlass::layout::PitchLinear</a>
, <a class="el" href="classcutlass_1_1layout_1_1RowMajor.html#a89225f007689acef0e17f6990e32c56e">cutlass::layout::RowMajor</a>
, <a class="el" href="structcutlass_1_1layout_1_1RowMajorBlockLinear.html#a4e39977f24228d8a2776ca1652336c3b">cutlass::layout::RowMajorBlockLinear&lt; BlockRows, BlockColumns &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1RowMajorInterleaved.html#a37d81b341052fb816b7a5373efd76c52">cutlass::layout::RowMajorInterleaved&lt; Interleave &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1RowMajorTensorOpMultiplicandCongruous.html#aa5e728fbc807f398363a43dafd0118f5">cutlass::layout::RowMajorTensorOpMultiplicandCongruous&lt; ElementSize, Crosswise &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1RowMajorTensorOpMultiplicandCrosswise.html#a04e4211bb2e725d434371b2cf1de696e">cutlass::layout::RowMajorTensorOpMultiplicandCrosswise&lt; ElementSize, Crosswise &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1RowMajorVoltaTensorOpMultiplicandBCongruous.html#a2a556452484f1d6958ec57d0a5b68dea">cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous&lt; ElementSize &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1RowMajorVoltaTensorOpMultiplicandCongruous.html#a38fc7f3ffab51a56a76d297ecdc7edf7">cutlass::layout::RowMajorVoltaTensorOpMultiplicandCongruous&lt; ElementSize &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1RowMajorVoltaTensorOpMultiplicandCrosswise.html#a0a6c5b20d5c84ace7d2440c03b42f29e">cutlass::layout::RowMajorVoltaTensorOpMultiplicandCrosswise&lt; ElementSize, KBlock &gt;</a>
, <a class="el" href="classcutlass_1_1layout_1_1TensorCxRSKx.html#a27c2569d09991401630d7842c0c1ba67">cutlass::layout::TensorCxRSKx&lt; Interleave &gt;</a>
, <a class="el" href="classcutlass_1_1layout_1_1TensorNCHW.html#a20dd56dacab1558db7253fb737704c51">cutlass::layout::TensorNCHW</a>
, <a class="el" href="classcutlass_1_1layout_1_1TensorNCxHWx.html#a915e1193d4f9c1feb973ae1331687bf9">cutlass::layout::TensorNCxHWx&lt; Interleave &gt;</a>
, <a class="el" href="classcutlass_1_1layout_1_1TensorNHWC.html#a3bb3250d891e752789fa02d5c0cc0ede">cutlass::layout::TensorNHWC</a>
, <a class="el" href="structcutlass_1_1layout_1_1TensorOpMultiplicand.html#a9ef7a95265d1602eb5d050eb89ad8b6b">cutlass::layout::TensorOpMultiplicand&lt; ElementSize, Crosswise &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1TensorOpMultiplicandColumnMajorInterleaved.html#a68ec1121f75551e44ef28edc17763179">cutlass::layout::TensorOpMultiplicandColumnMajorInterleaved&lt; ElementSize, InterleavedK &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1TensorOpMultiplicandCongruous.html#a016ea88480ec49f60b311b00e06dba54">cutlass::layout::TensorOpMultiplicandCongruous&lt; ElementSize, Crosswise &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1TensorOpMultiplicandCongruous_3_0132_00_01Crosswise_01_4.html#a8580ab6b5c31b815c9ff4d146cbec442">cutlass::layout::TensorOpMultiplicandCongruous&lt; 32, Crosswise &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1TensorOpMultiplicandCrosswise.html#abf4bd0dac231dfa83fc109420371fe8e">cutlass::layout::TensorOpMultiplicandCrosswise&lt; ElementSize, Crosswise &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1TensorOpMultiplicandRowMajorInterleaved.html#a0df7088ef67f17f69fd72fb5d238fc3e">cutlass::layout::TensorOpMultiplicandRowMajorInterleaved&lt; ElementSize, InterleavedK &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1VoltaTensorOpMultiplicandBCongruous.html#aaafaf46f0d3bb10de607e999e28ca87a">cutlass::layout::VoltaTensorOpMultiplicandBCongruous&lt; ElementSize &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1VoltaTensorOpMultiplicandCongruous.html#af9149c2f7914f62232dcb3bd8f46384d">cutlass::layout::VoltaTensorOpMultiplicandCongruous&lt; ElementSize &gt;</a>
, <a class="el" href="structcutlass_1_1layout_1_1VoltaTensorOpMultiplicandCrosswise.html#a0c2e65352446d60ea1a917ff840245e8">cutlass::layout::VoltaTensorOpMultiplicandCrosswise&lt; ElementSize, KBlock &gt;</a>
, <a class="el" href="classcutlass_1_1TensorView.html#ad7c287afe582ff3681a8cd6e53d6a4f5">cutlass::TensorView&lt; Element_, Layout_ &gt;</a>
, <a class="el" href="classcutlass_1_1thread_1_1Matrix.html#a6c737c01701bb00f0d47ffdf847fcb6d">cutlass::thread::Matrix&lt; Element, Rows, Columns, Layout &gt;</a>
Updated doxygen
2018-09-20 05:02:08 +08:00
</li>
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
<li>cbegin()
: <a class="el" href="classcutlass_1_1Array_3_01T_00_01N_00_01false_01_4.html#a86a56cc907c8566068034ef8294cf7c2">cutlass::Array&lt; T, N, false &gt;</a>
, <a class="el" href="classcutlass_1_1Array_3_01T_00_01N_00_01true_01_4.html#a815d434e9da9715a115896b3f6e64608">cutlass::Array&lt; T, N, true &gt;</a>
</li>
<li>cend()
: <a class="el" href="classcutlass_1_1Array_3_01T_00_01N_00_01false_01_4.html#ae6106b72ee9035389afb313801561b16">cutlass::Array&lt; T, N, false &gt;</a>
, <a class="el" href="classcutlass_1_1Array_3_01T_00_01N_00_01true_01_4.html#a27e663ee5e22d4af436588a500a6cc0c">cutlass::Array&lt; T, N, true &gt;</a>
Updated doxygen
2018-09-20 05:02:08 +08:00
</li>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
<li>check()
: <a class="el" href="structcutlass_1_1platform_1_1is__base__of__helper.html#a5bf08859497e304ca353699ad6ac332b">cutlass::platform::is_base_of_helper&lt; BaseT, DerivedT &gt;</a>
</li>
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
<li>check_cmd_line_flag()
: <a class="el" href="structcutlass_1_1CommandLine.html#a5a20785501f9ed3d4a57241b08399552">cutlass::CommandLine</a>
</li>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
<li>clamp()
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
: <a class="el" href="structcutlass_1_1Coord.html#a40e145063833155c800b38f82cee7461">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</li>
<li>clear()
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
: <a class="el" href="classcutlass_1_1Array_3_01T_00_01N_00_01false_01_4.html#a5b84c4dc5257f31108a0598915f03f94">cutlass::Array&lt; T, N, false &gt;</a>
, <a class="el" href="classcutlass_1_1Array_3_01T_00_01N_00_01true_01_4.html#ae67b1d98a446384fc75a1c92474e719d">cutlass::Array&lt; T, N, true &gt;</a>
, <a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1InterleavedPredicatedTileIterator_1_1Mask.html#aed4717037e76148efbb7bb68d6c4e509">cutlass::epilogue::threadblock::InterleavedPredicatedTileIterator&lt; ThreadMap_, Element_, InterleavedK &gt;::Mask</a>
, <a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1PredicatedTileIterator_1_1Mask.html#a0fcbcea35583d096e4154209237ba217">cutlass::epilogue::threadblock::PredicatedTileIterator&lt; ThreadMap_, Element_ &gt;::Mask</a>
, <a class="el" href="structcutlass_1_1PredicateVector.html#a51d9239e76ec040819333022fcecdb55">cutlass::PredicateVector&lt; kPredicates_, kPredicatesPerByte_, kPredicateStart_ &gt;</a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</li>
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
<li>clear_mask()
: <a class="el" href="classcutlass_1_1epilogue_1_1threadblock_1_1InterleavedPredicatedTileIterator.html#ab478d83e7b15b9eca8f3f281072cba38">cutlass::epilogue::threadblock::InterleavedPredicatedTileIterator&lt; ThreadMap_, Element_, InterleavedK &gt;</a>
, <a class="el" href="classcutlass_1_1epilogue_1_1threadblock_1_1PredicatedTileIterator.html#a17a6ccbe829782c27e49f47922fce84a">cutlass::epilogue::threadblock::PredicatedTileIterator&lt; ThreadMap_, Element_ &gt;</a>
, <a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator2dThreadTile_3_01Shape__da632779aba661c0f4cfaaa78126b771.html#a887454d8c430984ceffd4859903b4898">cutlass::transform::threadblock::PredicatedTileAccessIterator2dThreadTile&lt; Shape_, Element_, layout::ColumnMajor, AdvanceRank, ThreadMap_, AccessType_ &gt;</a>
, <a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator2dThreadTile_3_01Shape__1790abaa54a01f277d75766d5882fec8.html#a5ad8047c30a84a55fa1c21c911d148d8">cutlass::transform::threadblock::PredicatedTileAccessIterator2dThreadTile&lt; Shape_, Element_, layout::PitchLinear, AdvanceRank, ThreadMap_, AccessType_ &gt;</a>
, <a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator2dThreadTile_3_01Shape__7327fa15996bcb8502cdfcc192350fe1.html#abc85cc801d0e933dfb026a9c266674e2">cutlass::transform::threadblock::PredicatedTileAccessIterator2dThreadTile&lt; Shape_, Element_, layout::RowMajor, AdvanceRank, ThreadMap_, AccessType_ &gt;</a>
, <a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator_3_01Shape___00_01Elemen89c687c583745a73cb485041911a4c4e.html#ae78b23bb3d11b1ad5fe615a4bcb2d15d">cutlass::transform::threadblock::PredicatedTileAccessIterator&lt; Shape_, Element_, layout::ColumnMajor, AdvanceRank, ThreadMap_, AccessType_ &gt;</a>
, <a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator_3_01Shape___00_01Elemenab63a1e105bf37f6371516cb9e2c5a7a.html#a2fd2b595bf69e2f4d1c9af852acdd018">cutlass::transform::threadblock::PredicatedTileAccessIterator&lt; Shape_, Element_, layout::ColumnMajorInterleaved&lt; InterleavedK &gt;, AdvanceRank, ThreadMap_, AccessType_ &gt;</a>
, <a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator_3_01Shape___00_01Elemen784a0e9da3f55064c47e5613791f51f7.html#ac0b90ae65b6bd987bfb0a0fca4912533">cutlass::transform::threadblock::PredicatedTileAccessIterator&lt; Shape_, Element_, layout::PitchLinear, AdvanceRank, ThreadMap_, AccessType_ &gt;</a>
, <a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator_3_01Shape___00_01Elemen9838736ad62fae54213fbaf722a989ab.html#ad52c785f2d0e7bfc86633571f5e4a926">cutlass::transform::threadblock::PredicatedTileAccessIterator&lt; Shape_, Element_, layout::RowMajor, AdvanceRank, ThreadMap_, AccessType_ &gt;</a>
, <a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator_3_01Shape___00_01Elemen809793e785fb4211888c6b4e5dcfcb39.html#acd9df23ddc440195ba7a648db3c55f3e">cutlass::transform::threadblock::PredicatedTileAccessIterator&lt; Shape_, Element_, layout::RowMajorInterleaved&lt; InterleavedK &gt;, AdvanceRank, ThreadMap_, AccessType_ &gt;</a>
, <a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator2dThreadTile_3_01Shape___00_0165b39a630d10785a3558406f9adb99b9.html#a16eb418826fc3bead16d7f15dccae29f">cutlass::transform::threadblock::PredicatedTileIterator2dThreadTile&lt; Shape_, Element_, layout::ColumnMajor, AdvanceRank, ThreadMap_, Transpose_ &gt;</a>
, <a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator2dThreadTile_3_01Shape___00_017a517f3c73efd795ab05059cc9b111e1.html#a24d17e4be377b870202dd8524047af8d">cutlass::transform::threadblock::PredicatedTileIterator2dThreadTile&lt; Shape_, Element_, layout::PitchLinear, AdvanceRank, ThreadMap_, Transpose_ &gt;</a>
, <a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator2dThreadTile_3_01Shape___00_013671177d6219bfeb0e1b4dc4c1b5bf11.html#a2778dc7d89b47e3fe9b1d3f9e67c9601">cutlass::transform::threadblock::PredicatedTileIterator2dThreadTile&lt; Shape_, Element_, layout::RowMajor, AdvanceRank, ThreadMap_, Transpose_ &gt;</a>
, <a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator_3_01Shape___00_01Element___0068b3e874b5d93d11f0fa902c7f1d11d9.html#ac31418e71cb8ed71b7ebf51ab7028713">cutlass::transform::threadblock::PredicatedTileIterator&lt; Shape_, Element_, layout::ColumnMajor, AdvanceRank, ThreadMap_, AccessSize &gt;</a>
, <a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator_3_01Shape___00_01Element___00f6b3a9dfab5e7c72d5233f7e5e6e3b9b.html#a73c78020399019ab244400b48f43d7cc">cutlass::transform::threadblock::PredicatedTileIterator&lt; Shape_, Element_, layout::ColumnMajorInterleaved&lt; InterleavedK &gt;, AdvanceRank, ThreadMap_, AccessSize &gt;</a>
, <a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator_3_01Shape___00_01Element___00e7c2c404e7aedfe60ad56bb5571306a1.html#afd3e189a510aef0ddceb6f0ceb519d88">cutlass::transform::threadblock::PredicatedTileIterator&lt; Shape_, Element_, layout::PitchLinear, AdvanceRank, ThreadMap_, AccessSize &gt;</a>
, <a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator_3_01Shape___00_01Element___0041ea81994f8af0d4d071fdb9e66b5ff0.html#a38b1509afcb20c5474ea5998f85c6507">cutlass::transform::threadblock::PredicatedTileIterator&lt; Shape_, Element_, layout::RowMajor, AdvanceRank, ThreadMap_, AccessSize &gt;</a>
, <a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator_3_01Shape___00_01Element___00d670f969180a8d182dffb356ebcc957e.html#a40cdf0a9b56d2571c87f50d1abcfae73">cutlass::transform::threadblock::PredicatedTileIterator&lt; Shape_, Element_, layout::RowMajorInterleaved&lt; InterleavedK &gt;, AdvanceRank, ThreadMap_, AccessSize &gt;</a>
Updated doxygen
2018-09-20 05:02:08 +08:00
</li>
<li>column()
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
: <a class="el" href="structcutlass_1_1MatrixCoord.html#afbdcc5ca5b91f11f29046667b0bfde7b">cutlass::MatrixCoord</a>
Updated doxygen
2018-09-20 05:02:08 +08:00
</li>
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
<li>ColumnMajor()
: <a class="el" href="classcutlass_1_1layout_1_1ColumnMajor.html#adc69d6cd2cf938d1ab41c4f2ba0589a4">cutlass::layout::ColumnMajor</a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</li>
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
<li>ColumnMajorBlockLinear()
: <a class="el" href="structcutlass_1_1layout_1_1ColumnMajorBlockLinear.html#a740b33237fd49dee4692946811e203ba">cutlass::layout::ColumnMajorBlockLinear&lt; BlockRows, BlockColumns &gt;</a>
</li>
<li>ColumnMajorInterleaved()
: <a class="el" href="structcutlass_1_1layout_1_1ColumnMajorInterleaved.html#acd4d70f17b74ebe3e55ea0027bac5899">cutlass::layout::ColumnMajorInterleaved&lt; Interleave &gt;</a>
</li>
<li>ColumnMajorTensorOpMultiplicandCongruous()
: <a class="el" href="structcutlass_1_1layout_1_1ColumnMajorTensorOpMultiplicandCongruous.html#a910ff464fc55a153bf3e54cb7b816ccb">cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous&lt; ElementSize, Crosswise &gt;</a>
</li>
<li>ColumnMajorTensorOpMultiplicandCrosswise()
: <a class="el" href="structcutlass_1_1layout_1_1ColumnMajorTensorOpMultiplicandCrosswise.html#ae9010e219ceb098de21d2673a9112b50">cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise&lt; ElementSize, Crosswise &gt;</a>
</li>
<li>ColumnMajorVoltaTensorOpMultiplicandBCongruous()
: <a class="el" href="structcutlass_1_1layout_1_1ColumnMajorVoltaTensorOpMultiplicandBCongruous.html#ab42f59c4fb58814ce2b5617b12e3faf0">cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandBCongruous&lt; ElementSize &gt;</a>
</li>
<li>ColumnMajorVoltaTensorOpMultiplicandCongruous()
: <a class="el" href="structcutlass_1_1layout_1_1ColumnMajorVoltaTensorOpMultiplicandCongruous.html#a9019c397159688cd2bafe55967ee7f36">cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous&lt; ElementSize &gt;</a>
</li>
<li>ColumnMajorVoltaTensorOpMultiplicandCrosswise()
: <a class="el" href="structcutlass_1_1layout_1_1ColumnMajorVoltaTensorOpMultiplicandCrosswise.html#af2456911e4cfca021d50ec16ca1d7505">cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCrosswise&lt; ElementSize, KBlock &gt;</a>
</li>
<li>CommandLine()
: <a class="el" href="structcutlass_1_1CommandLine.html#a7156975dc884e8b58b91c710495fc79d">cutlass::CommandLine</a>
Updated doxygen
2018-09-20 05:02:08 +08:00
</li>
<li>complex()
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
: <a class="el" href="classcutlass_1_1complex.html#a9050cd0f48c7a7b718ea9223299b3a82">cutlass::complex&lt; T &gt;</a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</li>
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
<li>ComputeFragment
: <a class="el" href="classcutlass_1_1epilogue_1_1thread_1_1Convert.html#a1f6d13cc82035c3427f0a9367f35f18b">cutlass::epilogue::thread::Convert&lt; ElementOutput_, Count, ElementAccumulator_, Round &gt;</a>
, <a class="el" href="classcutlass_1_1epilogue_1_1thread_1_1LinearCombination.html#ad024cd3ca657233883219177fc2f50af">cutlass::epilogue::thread::LinearCombination&lt; ElementOutput_, Count, ElementAccumulator_, ElementCompute_, Round &gt;</a>
, <a class="el" href="classcutlass_1_1epilogue_1_1thread_1_1LinearCombinationClamp.html#a212e93f8d2c9793d77ec4b679b6c81e6">cutlass::epilogue::thread::LinearCombinationClamp&lt; ElementOutput_, Count, ElementAccumulator_, ElementCompute_, Round &gt;</a>
, <a class="el" href="classcutlass_1_1epilogue_1_1thread_1_1LinearCombinationRelu.html#a54909b2fb614bf006572ceb79a264d05">cutlass::epilogue::thread::LinearCombinationRelu&lt; ElementOutput_, Count, ElementAccumulator_, ElementCompute_, Round &gt;</a>
, <a class="el" href="classcutlass_1_1epilogue_1_1thread_1_1LinearCombinationRelu_3_01ElementOutput___00_01Count_00_01int_00_01float_00_01Round_01_4.html#a2690ec31dbbcb0b647a4c1846d171ef6">cutlass::epilogue::thread::LinearCombinationRelu&lt; ElementOutput_, Count, int, float, Round &gt;</a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</li>
<li>const_begin()
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
: <a class="el" href="structcutlass_1_1PredicateVector.html#a505db4b5fba1671ee8362c18e2ccce1b">cutlass::PredicateVector&lt; kPredicates_, kPredicatesPerByte_, kPredicateStart_ &gt;</a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</li>
<li>const_end()
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
: <a class="el" href="structcutlass_1_1PredicateVector.html#a8017a90ffe8a8039fff56b6956739045">cutlass::PredicateVector&lt; kPredicates_, kPredicatesPerByte_, kPredicateStart_ &gt;</a>
</li>
<li>const_iterator()
: <a class="el" href="classcutlass_1_1Array_3_01T_00_01N_00_01false_01_4_1_1const__iterator.html#a2baacc6de7180213621a2d6b2328ca7d">cutlass::Array&lt; T, N, false &gt;::const_iterator</a>
, <a class="el" href="classcutlass_1_1Array_3_01T_00_01N_00_01true_01_4_1_1const__iterator.html#a40f18ab5962efa95ac4ae4f5140c5d7b">cutlass::Array&lt; T, N, true &gt;::const_iterator</a>
</li>
<li>const_pointer
: <a class="el" href="structcutlass_1_1AlignedBuffer.html#a9ea058b3d86ad689240836e2d89686c4">cutlass::AlignedBuffer&lt; T, N, Align &gt;</a>
, <a class="el" href="classcutlass_1_1Array_3_01T_00_01N_00_01false_01_4.html#a8a90423fc5483b3ee1d31f377321e9e0">cutlass::Array&lt; T, N, false &gt;</a>
, <a class="el" href="classcutlass_1_1Array_3_01T_00_01N_00_01true_01_4.html#a319dba33ebc8556e58f699f32c6a391b">cutlass::Array&lt; T, N, true &gt;</a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</li>
<li>const_ref()
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
: <a class="el" href="classcutlass_1_1TensorRef.html#a5c5c66a59e9759f11f832fb71f4234c2">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a>
, <a class="el" href="classcutlass_1_1TensorView.html#aef27ab5348a53539286057a0da8720fc">cutlass::TensorView&lt; Element_, Layout_ &gt;</a>
, <a class="el" href="classcutlass_1_1thread_1_1Matrix.html#a850e9e43797c386ffbdec398d1c1b559">cutlass::thread::Matrix&lt; Element, Rows, Columns, Layout &gt;</a>
</li>
<li>const_reference
: <a class="el" href="structcutlass_1_1AlignedBuffer.html#a878e461a9368a2e9639464caf78ac718">cutlass::AlignedBuffer&lt; T, N, Align &gt;</a>
, <a class="el" href="classcutlass_1_1Array_3_01T_00_01N_00_01false_01_4_1_1const__reference.html#ac9e3b9e2f5797efbc47e3415aa204079">cutlass::Array&lt; T, N, false &gt;::const_reference</a>
, <a class="el" href="classcutlass_1_1Array_3_01T_00_01N_00_01true_01_4.html#ad64094119b89bb538cd1c1ea979c7954">cutlass::Array&lt; T, N, true &gt;</a>
</li>
<li>const_reverse_iterator()
: <a class="el" href="classcutlass_1_1Array_3_01T_00_01N_00_01false_01_4_1_1const__reverse__iterator.html#aae7705a26ea52ebd18d5f5809d816ee2">cutlass::Array&lt; T, N, false &gt;::const_reverse_iterator</a>
, <a class="el" href="classcutlass_1_1Array_3_01T_00_01N_00_01true_01_4_1_1const__reverse__iterator.html#aa839335a821fad9841eb31560d7520a2">cutlass::Array&lt; T, N, true &gt;::const_reverse_iterator</a>
</li>
<li>const_view()
: <a class="el" href="classcutlass_1_1TensorView.html#ab7794e21b87340f8b73aeb4dca2cb80c">cutlass::TensorView&lt; Element_, Layout_ &gt;</a>
, <a class="el" href="classcutlass_1_1thread_1_1Matrix.html#a65a4c5ff99fd0a7dbee722c9e04fac35">cutlass::thread::Matrix&lt; Element, Rows, Columns, Layout &gt;</a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</li>
<li>ConstIterator()
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
: <a class="el" href="classcutlass_1_1PredicateVector_1_1ConstIterator.html#abb6749fd0f66f9442fa18fabbb3588e4">cutlass::PredicateVector&lt; kPredicates_, kPredicatesPerByte_, kPredicateStart_ &gt;::ConstIterator</a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</li>
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
<li>ConstReference
: <a class="el" href="classcutlass_1_1HostTensor.html#a4c6d967596e266ceab3d36f6c8d05152">cutlass::HostTensor&lt; Element_, Layout_ &gt;</a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</li>
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
<li>ConstSubbyteReference()
: <a class="el" href="classcutlass_1_1ConstSubbyteReference.html#aa00016fe6dafa323e9875be4287fbfe5">cutlass::ConstSubbyteReference&lt; Element_, Storage_ &gt;</a>
Updated doxygen
2018-09-20 05:02:08 +08:00
</li>
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
<li>ConstTensorRef
: <a class="el" href="classcutlass_1_1epilogue_1_1threadblock_1_1Epilogue.html#a92587dbbf9e08f1db3fab5443ae870e8">cutlass::epilogue::threadblock::Epilogue&lt; Shape_, WarpMmaOperator_, PartitionsK, OutputTileIterator_, AccumulatorFragmentIterator_, WarpTileIterator_, SharedLoadIterator_, OutputOp_, Padding_ &gt;</a>
, <a class="el" href="classcutlass_1_1epilogue_1_1threadblock_1_1InterleavedEpilogue.html#ac88c09516fa86c8b66690a26a73c4a99">cutlass::epilogue::threadblock::InterleavedEpilogue&lt; Shape_, WarpMmaOperator_, PartitionsK, OutputTileIterator_, AccumulatorFragmentIterator_, OutputOp_, InterleavedK, IsBetaZero &gt;</a>
, <a class="el" href="classcutlass_1_1epilogue_1_1threadblock_1_1InterleavedPredicatedTileIterator.html#a5531982973996f04fb344d11e4e9d015">cutlass::epilogue::threadblock::InterleavedPredicatedTileIterator&lt; ThreadMap_, Element_, InterleavedK &gt;</a>
, <a class="el" href="classcutlass_1_1epilogue_1_1threadblock_1_1PredicatedTileIterator.html#a53cca23a482d1e55ca3e21011a54ae79">cutlass::epilogue::threadblock::PredicatedTileIterator&lt; ThreadMap_, Element_ &gt;</a>
, <a class="el" href="classcutlass_1_1epilogue_1_1threadblock_1_1SharedLoadIterator.html#a5208835793dcd89f36ea65a8fcadf7e7">cutlass::epilogue::threadblock::SharedLoadIterator&lt; ThreadMap_, Element_, MaxAlignment &gt;</a>
, <a class="el" href="classcutlass_1_1HostTensor.html#ae76a55dd7f4d827f042f9c9018567271">cutlass::HostTensor&lt; Element_, Layout_ &gt;</a>
, <a class="el" href="classcutlass_1_1TensorRef.html#ad3c5c9466713f62a5191e720827f34da">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a>
, <a class="el" href="classcutlass_1_1TensorView.html#a48c934ddac84fa964fb9b1364ec44164">cutlass::TensorView&lt; Element_, Layout_ &gt;</a>
, <a class="el" href="classcutlass_1_1thread_1_1Matrix.html#a5cae9543ffd1f2e722943b53fa5486b9">cutlass::thread::Matrix&lt; Element, Rows, Columns, Layout &gt;</a>
Updated doxygen
2018-09-20 05:02:08 +08:00
</li>
<li>ConstTensorView
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
: <a class="el" href="classcutlass_1_1HostTensor.html#ac4445444c8f092dd1be47f1530affbaf">cutlass::HostTensor&lt; Element_, Layout_ &gt;</a>
, <a class="el" href="classcutlass_1_1TensorView.html#a71def6d54ed28dfe3b17fde3e6461578">cutlass::TensorView&lt; Element_, Layout_ &gt;</a>
, <a class="el" href="classcutlass_1_1thread_1_1Matrix.html#a24979d95b579648b9871db63ba9f7c6b">cutlass::thread::Matrix&lt; Element, Rows, Columns, Layout &gt;</a>
</li>
<li>contains
: <a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1TensorContainsFunc.html#a36f817e5b6e993ac3c9aaf78186a1ffb">cutlass::reference::host::detail::TensorContainsFunc&lt; Element, Layout &gt;</a>
, <a class="el" href="classcutlass_1_1TensorView.html#a6a6a1f99d06abd8fb3f5a8e4e0fea25e">cutlass::TensorView&lt; Element_, Layout_ &gt;</a>
Updated doxygen
2018-09-20 05:02:08 +08:00
</li>
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
<li>contiguous()
: <a class="el" href="structcutlass_1_1layout_1_1PitchLinearCoord.html#a3d3c19009c7cf8991cb33f1ff8c8d494">cutlass::layout::PitchLinearCoord</a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</li>
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
<li>ContiguousMatrix()
: <a class="el" href="structcutlass_1_1layout_1_1ContiguousMatrix.html#a4515c6a78dadf99bf491137e6bb52451">cutlass::layout::ContiguousMatrix</a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</li>
<li>Convert()
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
: <a class="el" href="classcutlass_1_1epilogue_1_1thread_1_1Convert.html#ac7ec0a00e5863dc06f4d99477fd5c056">cutlass::epilogue::thread::Convert&lt; ElementOutput_, Count, ElementAccumulator_, Round &gt;</a>
</li>
<li>convert
: <a class="el" href="structcutlass_1_1gemm_1_1device_1_1GemmSplitKParallel_1_1Arguments.html#aabbedd03888a6090f049f53f53bf4e45">cutlass::gemm::device::GemmSplitKParallel&lt; ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_, LayoutC_, ElementAccumulator_, OperatorClass_, ArchTag_, ThreadblockShape_, WarpShape_, InstructionShape_, EpilogueOutputOp_, ConvertScaledOp_, ReductionOp_, ThreadblockSwizzle_, Stages, kAlignmentA, kAlignmentB, Operator_ &gt;::Arguments</a>
, <a class="el" href="structcutlass_1_1gemm_1_1device_1_1GemmSplitKParallel_3_01ElementA___00_01LayoutA___00_01Elementafcb1aeaf2035a7ac769d7acc233423b.html#a48ced96adaf371f03c1c9a50db9f50f2">cutlass::gemm::device::GemmSplitKParallel&lt; ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_, layout::ColumnMajor, ElementAccumulator_, OperatorClass_, ArchTag_, ThreadblockShape_, WarpShape_, InstructionShape_, EpilogueOutputOp_, ConvertScaledOp_, ReductionOp_, ThreadblockSwizzle_, Stages, kAlignmentA, kAlignmentB, Operator_ &gt;::Arguments</a>
, <a class="el" href="structcutlass_1_1half__t.html#a5ef78c3a7ccd316fc4fe52b7c230f87b">cutlass::half_t</a>
, <a class="el" href="structcutlass_1_1NumericArrayConverter.html#a157bacb3ab7d03032c83ff75d0a0d090">cutlass::NumericArrayConverter&lt; T, S, N, Round &gt;</a>
, <a class="el" href="structcutlass_1_1NumericArrayConverter_3_01float_00_01half__t_00_012_00_01Round_01_4.html#a16ac842664840d5db0ba823303c9ec4e">cutlass::NumericArrayConverter&lt; float, half_t, 2, Round &gt;</a>
, <a class="el" href="structcutlass_1_1NumericArrayConverter_3_01float_00_01half__t_00_01N_00_01Round_01_4.html#a4602966344dcf217c4dbd97deb358c6f">cutlass::NumericArrayConverter&lt; float, half_t, N, Round &gt;</a>
, <a class="el" href="structcutlass_1_1NumericArrayConverter_3_01half__t_00_01float_00_012_00_01FloatRoundStyle_1_1round__to__nearest_01_4.html#a3cc4d59f083555f24288e15490eeb41d">cutlass::NumericArrayConverter&lt; half_t, float, 2, FloatRoundStyle::round_to_nearest &gt;</a>
, <a class="el" href="structcutlass_1_1NumericArrayConverter_3_01half__t_00_01float_00_01N_00_01Round_01_4.html#af8e268c03414c218485ed80e158725e8">cutlass::NumericArrayConverter&lt; half_t, float, N, Round &gt;</a>
, <a class="el" href="structcutlass_1_1NumericConverter.html#a4d1a347bd8c92f3dc5b6e919005d34d2">cutlass::NumericConverter&lt; T, S, Round &gt;</a>
, <a class="el" href="structcutlass_1_1NumericConverter_3_01float_00_01half__t_00_01Round_01_4.html#af7d7dec76e968b489efa25be32a4cf04">cutlass::NumericConverter&lt; float, half_t, Round &gt;</a>
, <a class="el" href="structcutlass_1_1NumericConverter_3_01half__t_00_01float_00_01FloatRoundStyle_1_1round__to__nearest_01_4.html#aaf16c1dd3bb1fc0566c819146dfd5ab8">cutlass::NumericConverter&lt; half_t, float, FloatRoundStyle::round_to_nearest &gt;</a>
, <a class="el" href="structcutlass_1_1NumericConverter_3_01half__t_00_01float_00_01FloatRoundStyle_1_1round__toward__zero_01_4.html#a43ab30e5283f39b1defe46b13da9ac1b">cutlass::NumericConverter&lt; half_t, float, FloatRoundStyle::round_toward_zero &gt;</a>
, <a class="el" href="structcutlass_1_1NumericConverter_3_01int8__t_00_01float_00_01Round_01_4.html#ab90d4ee00677c3129962501a148cdaf7">cutlass::NumericConverter&lt; int8_t, float, Round &gt;</a>
, <a class="el" href="structcutlass_1_1NumericConverter_3_01T_00_01T_00_01Round_01_4.html#aa61325e20130b528104b990fc8ec3bb8">cutlass::NumericConverter&lt; T, T, Round &gt;</a>
, <a class="el" href="structcutlass_1_1NumericConverterClamp.html#aefadbac80ed0f5be1abc6f6704631fe2">cutlass::NumericConverterClamp&lt; T, S &gt;</a>
, <a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1TensorCopyIf.html#ad9e5902883076ca684487d276a79c47e">cutlass::reference::host::detail::TensorCopyIf&lt; DstElement, DstLayout, SrcElement, SrcLayout, F &gt;</a>
</li>
<li>convert_op
: <a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DirectEpilogueTensorOp_1_1Params.html#a97f1044e7b7cec1ddd1f120b8b7539f2">cutlass::epilogue::threadblock::DirectEpilogueTensorOp&lt; Shape_, Operator_, PartitionsK, Element_, OutputOp_, ConvertOp_ &gt;::Params</a>
</li>
<li>ConvertOp
: <a class="el" href="classcutlass_1_1epilogue_1_1threadblock_1_1DirectEpilogueTensorOp.html#a71707c91f25720e027e9e3b9f7a8a113">cutlass::epilogue::threadblock::DirectEpilogueTensorOp&lt; Shape_, Operator_, PartitionsK, Element_, OutputOp_, ConvertOp_ &gt;</a>
</li>
<li>ConvertScaledOp
: <a class="el" href="classcutlass_1_1gemm_1_1device_1_1GemmSplitKParallel.html#affb7a5c96c9e8b04eb94a464e5fdc48b">cutlass::gemm::device::GemmSplitKParallel&lt; ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_, LayoutC_, ElementAccumulator_, OperatorClass_, ArchTag_, ThreadblockShape_, WarpShape_, InstructionShape_, EpilogueOutputOp_, ConvertScaledOp_, ReductionOp_, ThreadblockSwizzle_, Stages, kAlignmentA, kAlignmentB, Operator_ &gt;</a>
, <a class="el" href="classcutlass_1_1gemm_1_1device_1_1GemmSplitKParallel_3_01ElementA___00_01LayoutA___00_01ElementBbe7c1f7154ad5b5bf9d4d28301e2b457.html#aa69d9364cc5247ea353608d5c0600fe7">cutlass::gemm::device::GemmSplitKParallel&lt; ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_, layout::ColumnMajor, ElementAccumulator_, OperatorClass_, ArchTag_, ThreadblockShape_, WarpShape_, InstructionShape_, EpilogueOutputOp_, ConvertScaledOp_, ReductionOp_, ThreadblockSwizzle_, Stages, kAlignmentA, kAlignmentB, Operator_ &gt;</a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</li>
<li>Coord()
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
: <a class="el" href="structcutlass_1_1Coord.html#ab7094975a4b7471315ca083ae575030a">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a>
</li>
<li>copy_in_device_to_device()
: <a class="el" href="classcutlass_1_1HostTensor.html#a5686ea068c8f3e820ccff015e95bc474">cutlass::HostTensor&lt; Element_, Layout_ &gt;</a>
</li>
<li>copy_in_device_to_host()
: <a class="el" href="classcutlass_1_1HostTensor.html#a2b4858efb0356a6fc01bb9f55f0ad3b2">cutlass::HostTensor&lt; Element_, Layout_ &gt;</a>
</li>
<li>copy_in_host_to_device()
: <a class="el" href="classcutlass_1_1HostTensor.html#ad385330c69ecd7bd0b6c3660815253fa">cutlass::HostTensor&lt; Element_, Layout_ &gt;</a>
</li>
<li>copy_in_host_to_host()
: <a class="el" href="classcutlass_1_1HostTensor.html#ae11229ea69460ca174c5e6f9815eb97f">cutlass::HostTensor&lt; Element_, Layout_ &gt;</a>
</li>
<li>copy_out_device_to_device()
: <a class="el" href="classcutlass_1_1HostTensor.html#ab7179440d39b0445113b30b7a460a1ec">cutlass::HostTensor&lt; Element_, Layout_ &gt;</a>
</li>
<li>copy_out_device_to_host()
: <a class="el" href="classcutlass_1_1HostTensor.html#ab3051d2842b3aa3815e2ea5f53abfc2a">cutlass::HostTensor&lt; Element_, Layout_ &gt;</a>
</li>
<li>copy_out_host_to_device()
: <a class="el" href="classcutlass_1_1HostTensor.html#abeefdb8bccb2d8d751fdb22fa7e8ef0c">cutlass::HostTensor&lt; Element_, Layout_ &gt;</a>
</li>
<li>copy_out_host_to_host()
: <a class="el" href="classcutlass_1_1HostTensor.html#a40da221db96cfda76ba5623856c66bf1">cutlass::HostTensor&lt; Element_, Layout_ &gt;</a>
</li>
<li>Core
: <a class="el" href="structcutlass_1_1gemm_1_1kernel_1_1DefaultGemv.html#a4a5d78877329cd922780fc31a6448ef0">cutlass::gemm::kernel::DefaultGemv&lt; ThreadBlockShape_, ThreadShape_, ElementA_, LayoutA_, ElementB_, LayoutB_, ElementCD_, LayoutCD_, ElementAccumulator_ &gt;</a>
</li>
<li>Count
: <a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DefaultThreadMapVoltaTensorOp_3_01ThreadblockShape__52116c60c62f0fd520071558e42b814f.html#a26f942339b9c6844886b8d5967e07914">cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp&lt; ThreadblockShape_, WarpShape_, PartitionsK, ElementOutput_, ElementsPerAccess, float &gt;::Detail</a>
, <a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DefaultThreadMapVoltaTensorOp_3_01ThreadblockShape__4433cc988100e98097a748d2670fb0fc.html#a037e7c6716020fa2297eee14ba9704b0">cutlass::epilogue::threadblock::DefaultThreadMapVoltaTensorOp&lt; ThreadblockShape_, WarpShape_, PartitionsK, ElementOutput_, ElementsPerAccess, half_t &gt;::Detail</a>
, <a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1OutputTileOptimalThreadMap.html#a94a7c6cfdb44e3c8d15b1f948dbebaaf">cutlass::epilogue::threadblock::OutputTileOptimalThreadMap&lt; Shape_, Count_, Threads, ElementsPerAccess, ElementSize &gt;</a>
, <a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1OutputTileThreadMap.html#a0bd9c4b005f277eb40b9d2bdccdcb9e0">cutlass::epilogue::threadblock::OutputTileThreadMap&lt; ThreadMap_, Shape_, Iterations_, Delta_, Count_ &gt;</a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</li>
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
<li>crbegin()
: <a class="el" href="classcutlass_1_1Array_3_01T_00_01N_00_01false_01_4.html#a01b9f76c6052dc2467095b91c1ebe34e">cutlass::Array&lt; T, N, false &gt;</a>
, <a class="el" href="classcutlass_1_1Array_3_01T_00_01N_00_01true_01_4.html#ab1813941489bef9563cc0bc3f647b2ca">cutlass::Array&lt; T, N, true &gt;</a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</li>
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
<li>crend()
: <a class="el" href="classcutlass_1_1Array_3_01T_00_01N_00_01false_01_4.html#abbc436f18649c1578ef95eb501872094">cutlass::Array&lt; T, N, false &gt;</a>
, <a class="el" href="classcutlass_1_1Array_3_01T_00_01N_00_01true_01_4.html#a76e1b5d728b155f9d967a43c0cc3b0dd">cutlass::Array&lt; T, N, true &gt;</a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</li>
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
<li>cublas_type
: <a class="el" href="structcutlass_1_1TypeTraits_3_01complex_3_01double_01_4_01_4.html#a474db90f9990e15f86a822e6a226eeb7">cutlass::TypeTraits&lt; complex&lt; double &gt; &gt;</a>
, <a class="el" href="structcutlass_1_1TypeTraits_3_01complex_3_01float_01_4_01_4.html#a6885f2871ac12091946d8f9a833efc0e">cutlass::TypeTraits&lt; complex&lt; float &gt; &gt;</a>
, <a class="el" href="structcutlass_1_1TypeTraits_3_01complex_3_01half_01_4_01_4.html#afe1a23ad5e158fc64fac88bd6095602e">cutlass::TypeTraits&lt; complex&lt; half &gt; &gt;</a>
, <a class="el" href="structcutlass_1_1TypeTraits_3_01complex_3_01half__t_01_4_01_4.html#a5ca73eeea32d33e33e8a98890a78593d">cutlass::TypeTraits&lt; complex&lt; half_t &gt; &gt;</a>
, <a class="el" href="structcutlass_1_1TypeTraits_3_01double_01_4.html#ae0e23f7459fa1586160ae47e151428ae">cutlass::TypeTraits&lt; double &gt;</a>
, <a class="el" href="structcutlass_1_1TypeTraits_3_01float_01_4.html#aa835af229fbe3c00ccc6ea164bb1eb62">cutlass::TypeTraits&lt; float &gt;</a>
, <a class="el" href="structcutlass_1_1TypeTraits_3_01half__t_01_4.html#a0491882d302a1038f1bb3c3d09374bb4">cutlass::TypeTraits&lt; half_t &gt;</a>
, <a class="el" href="structcutlass_1_1TypeTraits_3_01int_01_4.html#abe5b201de5b1ef7a4e23f5ab6ed06f4a">cutlass::TypeTraits&lt; int &gt;</a>
, <a class="el" href="structcutlass_1_1TypeTraits_3_01int64__t_01_4.html#a24cf2f6d484f30a1b329c3f8c1fb573d">cutlass::TypeTraits&lt; int64_t &gt;</a>
, <a class="el" href="structcutlass_1_1TypeTraits_3_01int8__t_01_4.html#ac801fb97ec8a1a8cce0dbab46a614eff">cutlass::TypeTraits&lt; int8_t &gt;</a>
, <a class="el" href="structcutlass_1_1TypeTraits_3_01uint64__t_01_4.html#a9ef28cd1f430f25cdda594f060f4e718">cutlass::TypeTraits&lt; uint64_t &gt;</a>
, <a class="el" href="structcutlass_1_1TypeTraits_3_01uint8__t_01_4.html#ae5edc866e5de8527b6ddf06c3844684b">cutlass::TypeTraits&lt; uint8_t &gt;</a>
, <a class="el" href="structcutlass_1_1TypeTraits_3_01unsigned_01_4.html#aeafbc657f1a9020e36bbe523a33990b5">cutlass::TypeTraits&lt; unsigned &gt;</a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</li>
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
<li>cuda_exception()
: <a class="el" href="classcutlass_1_1cuda__exception.html#acbfb99c9979ce7d24fe774459c66cfa5">cutlass::cuda_exception</a>
Updated doxygen
2018-09-20 05:02:08 +08:00
</li>
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
<li>cudaError()
: <a class="el" href="classcutlass_1_1cuda__exception.html#a5e2363c04ed0a43e244b274cb21aebf1">cutlass::cuda_exception</a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</li>
</ul>
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CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
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CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
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CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
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CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
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