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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 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
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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="structcutlass_1_1PredicateVector.html">PredicateVector</a></li><li class="navelem"><a class="el" href="structcutlass_1_1PredicateVector_1_1TrivialIterator.html">TrivialIterator</a></li> </ul>
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<div class="title">cutlass::PredicateVector&lt; kPredicates_, kPredicatesPerByte_, kPredicateStart_ &gt;::TrivialIterator Struct Reference</div> </div>
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<tr class="memitem:a6cb3664b5cba4280b7055a65ddad7850"><td class="memItemLeft" align="right" valign="top"><a class="el" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structcutlass_1_1PredicateVector_1_1TrivialIterator.html#a6cb3664b5cba4280b7055a65ddad7850">TrivialIterator</a> ()</td></tr>
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<tr class="memitem:ada8cd3ac6db568bb9bf268ba2c3a3e14"><td class="memItemLeft" align="right" valign="top"><a class="el" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structcutlass_1_1PredicateVector_1_1TrivialIterator.html#ada8cd3ac6db568bb9bf268ba2c3a3e14">TrivialIterator</a> (<a class="el" href="classcutlass_1_1PredicateVector_1_1Iterator.html">Iterator</a> const &amp;it)</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 class="memdesc:ada8cd3ac6db568bb9bf268ba2c3a3e14"><td class="mdescLeft">&#160;</td><td class="mdescRight">Copy constructor. <a href="#ada8cd3ac6db568bb9bf268ba2c3a3e14">More...</a><br /></td></tr>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
<tr class="separator:ada8cd3ac6db568bb9bf268ba2c3a3e14"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3adf0440f9a0143a61b43d39c3f03721"><td class="memItemLeft" align="right" valign="top"><a class="el" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structcutlass_1_1PredicateVector_1_1TrivialIterator.html#a3adf0440f9a0143a61b43d39c3f03721">TrivialIterator</a> (<a class="el" href="structcutlass_1_1PredicateVector.html">PredicateVector</a> const &amp;_vec)</td></tr>
<tr class="memdesc:a3adf0440f9a0143a61b43d39c3f03721"><td class="mdescLeft">&#160;</td><td class="mdescRight">Constructs an iterator from a <a class="el" href="structcutlass_1_1PredicateVector.html" title="Statically sized array of bits implementing. ">PredicateVector</a>. <a href="#a3adf0440f9a0143a61b43d39c3f03721">More...</a><br /></td></tr>
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<tr class="memitem:ad24e9b451064e99fb19955f772c30e6a"><td class="memItemLeft" align="right" valign="top"><a class="el" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a> <a class="el" href="structcutlass_1_1PredicateVector_1_1TrivialIterator.html">TrivialIterator</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structcutlass_1_1PredicateVector_1_1TrivialIterator.html#ad24e9b451064e99fb19955f772c30e6a">operator++</a> ()</td></tr>
<tr class="memdesc:ad24e9b451064e99fb19955f772c30e6a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Pre-increment. <a href="#ad24e9b451064e99fb19955f772c30e6a">More...</a><br /></td></tr>
<tr class="separator:ad24e9b451064e99fb19955f772c30e6a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa35b9165920b83b9a5a888df83925051"><td class="memItemLeft" align="right" valign="top"><a class="el" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a> <a class="el" href="structcutlass_1_1PredicateVector_1_1TrivialIterator.html">TrivialIterator</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structcutlass_1_1PredicateVector_1_1TrivialIterator.html#aa35b9165920b83b9a5a888df83925051">operator++</a> (int)</td></tr>
<tr class="memdesc:aa35b9165920b83b9a5a888df83925051"><td class="mdescLeft">&#160;</td><td class="mdescRight">Post-increment. <a href="#aa35b9165920b83b9a5a888df83925051">More...</a><br /></td></tr>
<tr class="separator:aa35b9165920b83b9a5a888df83925051"><td class="memSeparator" colspan="2">&#160;</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 class="memitem:aa6774c80e75fdd469bf1e42d98f8959a"><td class="memItemLeft" align="right" valign="top"><a class="el" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a> bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structcutlass_1_1PredicateVector_1_1TrivialIterator.html#aa6774c80e75fdd469bf1e42d98f8959a">operator*</a> () const </td></tr>
<tr class="memdesc:aa6774c80e75fdd469bf1e42d98f8959a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Dereferences iterator. <a href="#aa6774c80e75fdd469bf1e42d98f8959a">More...</a><br /></td></tr>
<tr class="separator:aa6774c80e75fdd469bf1e42d98f8959a"><td class="memSeparator" colspan="2">&#160;</td></tr>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
</table>
<h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
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="anchor" id="a6cb3664b5cba4280b7055a65ddad7850"></a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
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template&lt;int kPredicates_, int kPredicatesPerByte_ = 4, int kPredicateStart_ = 0&gt; </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
<div class="memitem">
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template&lt;int kPredicates_, int kPredicatesPerByte_ = 4, int kPredicateStart_ = 0&gt; </div>
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<td class="memname"><a class="el" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a> <a class="el" href="structcutlass_1_1PredicateVector.html">cutlass::PredicateVector</a>&lt; kPredicates_, kPredicatesPerByte_, kPredicateStart_ &gt;::TrivialIterator::TrivialIterator </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classcutlass_1_1PredicateVector_1_1Iterator.html">Iterator</a> const &amp;&#160;</td>
<td class="paramname"><em>it</em></td><td>)</td>
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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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template&lt;int kPredicates_, int kPredicatesPerByte_ = 4, int kPredicateStart_ = 0&gt; </div>
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<td class="memname"><a class="el" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a> <a class="el" href="structcutlass_1_1PredicateVector.html">cutlass::PredicateVector</a>&lt; kPredicates_, kPredicatesPerByte_, kPredicateStart_ &gt;::TrivialIterator::TrivialIterator </td>
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<td class="paramtype"><a class="el" href="structcutlass_1_1PredicateVector.html">PredicateVector</a> const &amp;&#160;</td>
<td class="paramname"><em>_vec</em></td><td>)</td>
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<h2 class="groupheader">Member Function Documentation</h2>
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="anchor" id="aa6774c80e75fdd469bf1e42d98f8959a"></a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
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template&lt;int kPredicates_, int kPredicatesPerByte_ = 4, int kPredicateStart_ = 0&gt; </div>
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<td class="memname"><a class="el" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a> bool <a class="el" href="structcutlass_1_1PredicateVector.html">cutlass::PredicateVector</a>&lt; kPredicates_, kPredicatesPerByte_, kPredicateStart_ &gt;::TrivialIterator::operator* </td>
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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
<a class="anchor" id="ad24e9b451064e99fb19955f772c30e6a"></a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
<div class="memitem">
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<div class="memtemplate">
template&lt;int kPredicates_, int kPredicatesPerByte_ = 4, int kPredicateStart_ = 0&gt; </div>
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<td class="memname"><a class="el" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a> <a class="el" href="structcutlass_1_1PredicateVector_1_1TrivialIterator.html">TrivialIterator</a>&amp; <a class="el" href="structcutlass_1_1PredicateVector.html">cutlass::PredicateVector</a>&lt; kPredicates_, kPredicatesPerByte_, kPredicateStart_ &gt;::TrivialIterator::operator++ </td>
<td>(</td>
<td class="paramname"></td><td>)</td>
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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
<a class="anchor" id="aa35b9165920b83b9a5a888df83925051"></a>
CUTLASS v1.0 release
2018-05-17 02:44:56 +08:00
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;int kPredicates_, int kPredicatesPerByte_ = 4, int kPredicateStart_ = 0&gt; </div>
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<tr>
<td class="mlabels-left">
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<td class="memname"><a class="el" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a> <a class="el" href="structcutlass_1_1PredicateVector_1_1TrivialIterator.html">TrivialIterator</a> <a class="el" href="structcutlass_1_1PredicateVector.html">cutlass::PredicateVector</a>&lt; kPredicates_, kPredicatesPerByte_, kPredicateStart_ &gt;::TrivialIterator::operator++ </td>
<td>(</td>
<td class="paramtype">int&#160;</td>
<td class="paramname"></td><td>)</td>
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<hr/>The documentation for this struct was generated from the following file:<ul>
<li><a class="el" href="predicate__vector_8h_source.html">predicate_vector.h</a></li>
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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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