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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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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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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
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<li class="navelem"><a class="el" href="namespacecutlass.html">cutlass</a></li><li class="navelem"><a class="el" href="structcutlass_1_1Coord.html">Coord</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::Coord&lt; Rank_, Index_, LongIndex_ &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="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &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="structcutlass_1_1Coord.html#a8a65128c86b236cd2bea875b85a34bc1">at</a>()</td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a358dde78a1c2105a9aeb4adee8bb3d2d">at</a>(int dim)</td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#ac7379275d7431ead927af7966b6fa0ec">at</a>() const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a06046c22b877abfb277d3f0fe4f8578a">at</a>(int dim) const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a40e145063833155c800b38f82cee7461">clamp</a>(Coord const &amp;max, Coord const &amp;min=Coord())</td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a5281db2419b5567db4265dead7ac02cc">Coord</a>(Index value=Index(0))</td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">explicit</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#ab7094975a4b7471315ca083ae575030a">Coord</a>(Index const (&amp;_idx)[kRank])</td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a42aefbb547e39b8cc7267c58a610c147">Coord</a>(Coord&lt; kRank, Index, LongIndex &gt; const &amp;coord)</td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a057a417a4d4a6e2f69e0b55a6f7ee902">dot</a>(Coord const &amp;b, LongIndex sum=LongIndex(0)) const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a7a89e5661ef391dd9f4fe81f0c982b75">Index</a> typedef</td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a2b07d7291d175920274c5e3346e5b68b">kRank</a></td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
<tr><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#ab61db7c2bfacaf0b7ce465e70d48c44f">LongIndex</a> typedef</td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#abe58b7c8f153a6029c2adc173f340fe0">max_dim_index</a>() const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#ae89e8a9fa3f07308f8938052ef1aa1fb">min_dim_index</a>() const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a88096d051dd05111cf265a011a89f7f6">operator bool</a>() const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">explicit</span></td></tr>
<tr><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#aa733c6fae0da553053530cba2dddcaa0">operator!</a>() const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a8183b9203a213d4b6381ad7dc120deea">operator!=</a>(Coord const &amp;b) const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#ac6b5fd8d0e5cb856d363fbff9a5b89dd">operator*</a>(Coord const &amp;b) const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a00e618bc944d355badf67c0edd791412">operator*=</a>(Coord const &amp;b)</td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#aec4c529a728118c0df6a3f527daba746">operator+</a>(Coord const &amp;b) const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#acb799faf60a17b708d0802f9e23c812f">operator+=</a>(Coord const &amp;b)</td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a2e1a706629eae28128230a0fa34b84a0">operator-</a>(Coord const &amp;b) const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a15ac170c861b34d418432aeb62ea86e0">operator-=</a>(Coord const &amp;b)</td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a6ae9f189d1a7a5ce7bb5e4416559c79f">operator/</a>(Coord const &amp;b) const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#af515e669363986dbbd60951ea6b69e14">operator/=</a>(Coord const &amp;b)</td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a47ad37153eb8d291266a51b39ead5948">operator&lt;</a>(Coord const &amp;b) const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a0e6405e081936a4fb23f15160e94ad08">operator&lt;=</a>(Coord const &amp;b) const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a8424ccd74e7e0ff1cf358ef571779cba">operator==</a>(Coord const &amp;b) const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a08507ff13f518a93a7d16ea0018f8a53">operator&gt;</a>(Coord const &amp;b) const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#ae37243e9f51b2b92b5fd09de69392657">operator&gt;=</a>(Coord const &amp;b) const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a6eee93e5fdbe147f751ec108b28275a1">operator[]</a>(int dim)</td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#accf5689f0d6a6f91965bff0cfd9ec296">operator[]</a>(int dim) const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#ad5a2fb5b6b57e0726624c2b6e7c6545c">product</a>() const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a329f97d4a09ef34e8470fe55800871f8">slice</a>(int start=0, Index identity=0) const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &gt;</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
<tr class="even"><td class="entry"><a class="el" href="structcutlass_1_1Coord.html#a49bb1a68198bd4c520d15efe3e84f757">sum</a>() const </td><td class="entry"><a class="el" href="structcutlass_1_1Coord.html">cutlass::Coord&lt; Rank_, Index_, LongIndex_ &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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