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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
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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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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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<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
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CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
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<li class="navelem"><a class="el" href="namespacecutlass.html">cutlass</a></li><li class="navelem"><a class="el" href="classcutlass_1_1TensorRef.html">TensorRef</a></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
<div class="title">cutlass::TensorRef&lt; Element_, Layout_ &gt; Member List</div> </div>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</div><!--header-->
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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
<p>This is the complete list of members for <a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a>, including all inherited members.</p>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
<table 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
<tr class="even"><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a4bed879c428963070de8ffbdc5d6e4f9">add_coord_offset</a>(TensorCoord const &amp;coord)</td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a6bbcd0e512915565cabfeccdb1b6417d">add_pointer_offset</a>(LongIndex offset_)</td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a8758907a1c9b1fcd00e7ece626d03b76">at</a>(TensorCoord const &amp;coord) const </td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a5c5c66a59e9759f11f832fb71f4234c2">const_ref</a>() const </td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#ad3c5c9466713f62a5191e720827f34da">ConstTensorRef</a> typedef</td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#ac7db3ca62ab1dfe0d3ea08bcadbc9352">data</a>() const </td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a965e8b3b7f92dc51d4d3821ea6a25012">data</a>(LongIndex idx) const </td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a6c1be7001a3fb05f8ac2d1fe7ea94c68">Element</a> typedef</td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#ac968ea9cb34fa99d29c64608c53bd4d4">good</a>() const </td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a11ec4b07a2132e647ca2ebe5112ce5ec">Index</a> typedef</td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a87c5c1c23b67b7182a177ef8d9437edd">kRank</a></td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
<tr><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#ae0e372b28e665820e6a2d17fc9f68d2b">Layout</a> typedef</td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a7771fcd932e36c51c15572305f5b5520">layout</a>()</td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#aca7d373c988016ee946f85ea958b21a7">layout</a>() const </td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#adeada5e33b231f125a4aaeaf963bd3a3">LongIndex</a> typedef</td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a66a9bab939e2d57c130bb76a5527f482">non_const_ref</a>() const </td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a68c6f7efc142acf71dd3908b00494b38">NonConstTensorRef</a> typedef</td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a4166ac2a0754574ac21d5d57d74f34e5">offset</a>(TensorCoord const &amp;coord) const </td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#ad4e1a9d4912a18547cd5391d63e8e7ac">operator+</a>(TensorCoord const &amp;b) const </td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#ae82b8ba3bbf7ebb28fe063932db3dc6b">operator+=</a>(TensorCoord const &amp;b)</td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#aa275fd660406665884f25c6f693a8892">operator-</a>(TensorCoord const &amp;b) const </td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#ac5f24cda8149ddfa8f19467ff2629f49">operator-=</a>(TensorCoord const &amp;b)</td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a10e36681cae687d9a45ec01bad2626b7">operator[]</a>(TensorCoord const &amp;coord) const </td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a0aba29ebf8715d217817a49356f95d3f">Reference</a> typedef</td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a9c2149162016bc19c7735b824d57eb9e">reset</a>(Element *ptr=nullptr)</td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a1c539841446205c482fe18cc0a99d913">reset</a>(Element *ptr, Layout const &amp;layout)</td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a7fb24829405f63b9fa9bbcb110141d9e">Stride</a> typedef</td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a191e88bc0fb310be655d700e937ab97c">stride</a>() const </td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a5fbf003d2e2321f816be60b0cbd0cfb7">stride</a>()</td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a300283c640d4e6aadc9c695befa26fec">stride</a>(int dim) const </td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a425adc5418cc80c9929579046d3111ef">stride</a>(int dim)</td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#ace218cdb46555a46bd71dbdfc2c317c1">TensorCoord</a> typedef</td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a26c607b811a8f828f5ec5b00f3b874ea">TensorRef</a>(Element *ptr=nullptr, Layout const &amp;layout=Layout())</td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html#a2b79f0d7c0c70774199ba4226bae0fe0">TensorRef</a>(NonConstTensorRef const &amp;ref)</td><td class="entry"><a class="el" href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; Element_, Layout_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
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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