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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
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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="projectname">CUTLASS
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
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<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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CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
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<div class="textblock">Here is a list of all namespaces with brief descriptions:</div><div class="directory">
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 class="levels">[detail level <span onclick="javascript:toggleLevel(1);">1</span><span onclick="javascript:toggleLevel(2);">2</span><span onclick="javascript:toggleLevel(3);">3</span><span onclick="javascript:toggleLevel(4);">4</span><span onclick="javascript:toggleLevel(5);">5</span>]</div><table class="directory">
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
<tr id="row_0_" class="even"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_0_" class="arrow" onclick="toggleFolder('0_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass.html" target="_self">cutlass</a></td><td class="desc"></td></tr>
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
<tr id="row_0_0_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1arch.html" target="_self">arch</a></td><td class="desc"></td></tr>
<tr id="row_0_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1debug.html" target="_self">debug</a></td><td class="desc"></td></tr>
<tr id="row_0_2_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1detail.html" target="_self">detail</a></td><td class="desc"></td></tr>
<tr id="row_0_3_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1device__memory.html" target="_self">device_memory</a></td><td class="desc"></td></tr>
<tr id="row_0_4_"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_0_4_" class="arrow" onclick="toggleFolder('0_4_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1epilogue.html" target="_self">epilogue</a></td><td class="desc"></td></tr>
<tr id="row_0_4_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1epilogue_1_1thread.html" target="_self">thread</a></td><td class="desc"></td></tr>
<tr id="row_0_4_1_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_4_1_" class="arrow" onclick="toggleFolder('0_4_1_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1epilogue_1_1threadblock.html" target="_self">threadblock</a></td><td class="desc"></td></tr>
<tr id="row_0_4_1_0_" class="even"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1epilogue_1_1threadblock_1_1detail.html" target="_self">detail</a></td><td class="desc"></td></tr>
<tr id="row_0_4_2_"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1epilogue_1_1warp.html" target="_self">warp</a></td><td class="desc"></td></tr>
<tr id="row_0_5_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_0_5_" class="arrow" onclick="toggleFolder('0_5_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1gemm.html" target="_self">gemm</a></td><td class="desc"></td></tr>
<tr id="row_0_5_0_"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1gemm_1_1device.html" target="_self">device</a></td><td class="desc"></td></tr>
<tr id="row_0_5_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_5_1_" class="arrow" onclick="toggleFolder('0_5_1_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1gemm_1_1kernel.html" target="_self">kernel</a></td><td class="desc"></td></tr>
<tr id="row_0_5_1_0_"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1gemm_1_1kernel_1_1detail.html" target="_self">detail</a></td><td class="desc"></td></tr>
<tr id="row_0_5_2_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_5_2_" class="arrow" onclick="toggleFolder('0_5_2_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1gemm_1_1thread.html" target="_self">thread</a></td><td class="desc"></td></tr>
<tr id="row_0_5_2_0_"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1gemm_1_1thread_1_1detail.html" target="_self">detail</a></td><td class="desc"></td></tr>
<tr id="row_0_5_3_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_5_3_" class="arrow" onclick="toggleFolder('0_5_3_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1gemm_1_1threadblock.html" target="_self">threadblock</a></td><td class="desc"></td></tr>
<tr id="row_0_5_3_0_"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1gemm_1_1threadblock_1_1detail.html" target="_self">detail</a></td><td class="desc"></td></tr>
<tr id="row_0_5_4_" class="even"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1gemm_1_1warp.html" target="_self">warp</a></td><td class="desc"></td></tr>
<tr id="row_0_6_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1layout.html" target="_self">layout</a></td><td class="desc"></td></tr>
<tr id="row_0_7_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1library.html" target="_self">library</a></td><td class="desc"></td></tr>
<tr id="row_0_8_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1platform.html" target="_self">platform</a></td><td class="desc"></td></tr>
<tr id="row_0_9_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_0_9_" class="arrow" onclick="toggleFolder('0_9_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reduction.html" target="_self">reduction</a></td><td class="desc"></td></tr>
<tr id="row_0_9_0_"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reduction_1_1kernel.html" target="_self">kernel</a></td><td class="desc"></td></tr>
<tr id="row_0_9_1_" class="even"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reduction_1_1thread.html" target="_self">thread</a></td><td class="desc"></td></tr>
<tr id="row_0_10_"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_0_10_" class="arrow" onclick="toggleFolder('0_10_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reference.html" target="_self">reference</a></td><td class="desc"></td></tr>
<tr id="row_0_10_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reference_1_1detail.html" target="_self">detail</a></td><td class="desc"></td></tr>
<tr id="row_0_10_1_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_10_1_" class="arrow" onclick="toggleFolder('0_10_1_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reference_1_1device.html" target="_self">device</a></td><td class="desc"></td></tr>
<tr id="row_0_10_1_0_" class="even"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reference_1_1device_1_1detail.html" target="_self">detail</a></td><td class="desc"></td></tr>
<tr id="row_0_10_1_1_"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_1_" class="arrow" onclick="toggleFolder('0_10_1_1_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reference_1_1device_1_1kernel.html" target="_self">kernel</a></td><td class="desc"></td></tr>
<tr id="row_0_10_1_1_0_" class="even"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reference_1_1device_1_1kernel_1_1detail.html" target="_self">detail</a></td><td class="desc">Defines several helpers </td></tr>
<tr id="row_0_10_1_2_"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reference_1_1device_1_1thread.html" target="_self">thread</a></td><td class="desc"></td></tr>
<tr id="row_0_10_2_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_10_2_" class="arrow" onclick="toggleFolder('0_10_2_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reference_1_1host.html" target="_self">host</a></td><td class="desc"></td></tr>
<tr id="row_0_10_2_0_"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reference_1_1host_1_1detail.html" target="_self">detail</a></td><td class="desc">Defines several helpers </td></tr>
<tr id="row_0_11_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1thread.html" target="_self">thread</a></td><td class="desc"></td></tr>
<tr id="row_0_12_"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_0_12_" class="arrow" onclick="toggleFolder('0_12_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1transform.html" target="_self">transform</a></td><td class="desc"></td></tr>
<tr id="row_0_12_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1transform_1_1thread.html" target="_self">thread</a></td><td class="desc"></td></tr>
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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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