CUTLASS 2.0 (#62)
CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
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Andrew Kerr
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<div id="projectname">Cutlass
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<div id="projectname">CUTLASS
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<div id="projectbrief">CUDA Templates for Linear Algebra Subroutines and Solvers</div>
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<li><a href="functions_type.html"><span>Typedefs</span></a></li>
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<li><a href="functions_enum.html"><span>Enumerations</span></a></li>
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<li><a href="functions_eval.html"><span>Enumerator</span></a></li>
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<li><a href="functions_vars.html#index_a"><span>a</span></a></li>
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<li><a href="functions_vars_b.html#index_b"><span>b</span></a></li>
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<li><a href="functions_vars_c.html#index_c"><span>c</span></a></li>
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<li><a href="functions_vars_i.html#index_i"><span>i</span></a></li>
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<li><a href="functions_vars_k.html#index_k"><span>k</span></a></li>
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<li><a href="functions_vars_l.html#index_l"><span>l</span></a></li>
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<li class="current"><a href="functions_vars_m.html#index_m"><span>m</span></a></li>
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<li><a href="functions_vars_n.html#index_n"><span>n</span></a></li>
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<li><a href="functions_vars_o.html#index_o"><span>o</span></a></li>
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<li><a href="functions_vars_p.html#index_p"><span>p</span></a></li>
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<li><a href="functions_vars_r.html#index_r"><span>r</span></a></li>
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<li><a href="functions_vars_s.html#index_s"><span>s</span></a></li>
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<li><a href="functions_vars_t.html#index_t"><span>t</span></a></li>
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<li><a href="functions_vars_u.html#index_u"><span>u</span></a></li>
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<li><a href="functions_vars_v.html#index_v"><span>v</span></a></li>
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<h3><a id="index_m"></a>- m -</h3><ul>
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<h3><a class="anchor" id="index_m"></a>- m -</h3><ul>
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<li>main_loop
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: <a class="el" href="unioncutlass_1_1gemm_1_1GemmTraits_1_1SharedStorage.html#aa5dd7edc3cffa785eb1e5b62c18c74c4">cutlass::gemm::GemmTraits< GemmConfig_, GlobalLoadStreamA_, GlobalLoadStreamB_, SharedLoadStreamA_, SharedLoadStreamB_, Epilogue_, BlockSwizzle_, Index_, ClearAccumulators_ >::SharedStorage</a>
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: <a class="el" href="unioncutlass_1_1gemm_1_1kernel_1_1Gemm_1_1SharedStorage.html#a25ca6f379b42d97b73de07473e2fdf02">cutlass::gemm::kernel::Gemm< Mma_, Epilogue_, ThreadblockSwizzle_, SplitKSerial >::SharedStorage</a>
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, <a class="el" href="unioncutlass_1_1gemm_1_1kernel_1_1GemmBatched_1_1SharedStorage.html#ae9f9d72c08cbf031961d95dca828d573">cutlass::gemm::kernel::GemmBatched< Mma_, Epilogue_, ThreadblockSwizzle_ >::SharedStorage</a>
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, <a class="el" href="unioncutlass_1_1gemm_1_1kernel_1_1GemmSplitKParallel_1_1SharedStorage.html#a9a4c11bb49a8cadae9cb2c17c92bf6db">cutlass::gemm::kernel::GemmSplitKParallel< Mma_, Epilogue_, ThreadblockSwizzle_ >::SharedStorage</a>
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</li>
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<li>mask_
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: <a class="el" href="structcutlass_1_1gemm_1_1IgemmGlobalIteratorAb.html#aa6e76073303f5f5a95053a781ec8b762">cutlass::gemm::IgemmGlobalIteratorAb< TileTraits_, Index_ ></a>
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<li>math_instruction
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: <a class="el" href="structcutlass_1_1library_1_1TileDescription.html#aad96c46afa29cb6c02f8e0737eeaf963">cutlass::library::TileDescription</a>
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</li>
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<li>max
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: <a class="el" href="structcutlass_1_1Distribution.html#a824641fd3addfa360999614970adfac0">cutlass::Distribution</a>
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</li>
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<li>maximum_compute_capability
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: <a class="el" href="structcutlass_1_1library_1_1TileDescription.html#a37f5925a2f04995091c56c034f4f2572">cutlass::library::TileDescription</a>
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</li>
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<li>maxInReg
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: <a class="el" href="structcutlass_1_1reduction_1_1BatchedReductionTraits.html#af11a3284195a24e580d2f379f179f05a">cutlass::reduction::BatchedReductionTraits< ScalarA_, ScalarC_, ScalarD_, ScalarAlphaBeta_, ScalarAccum_, ReductionSize_, OutputTile_, SubTile_, ThreadShape_, Index_, BlockSwizzle_, maxInReg_, maxOutReg_, Functor_ ></a>
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@@ -83,16 +150,35 @@ $(function() {
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<li>maxOutReg
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: <a class="el" href="structcutlass_1_1reduction_1_1BatchedReductionTraits.html#ac28e31791c5888bbe7b04abe6376a422">cutlass::reduction::BatchedReductionTraits< ScalarA_, ScalarC_, ScalarD_, ScalarAlphaBeta_, ScalarAccum_, ReductionSize_, OutputTile_, SubTile_, ThreadShape_, Index_, BlockSwizzle_, maxInReg_, maxOutReg_, Functor_ ></a>
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</li>
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<li>multiplicand_bounds
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: <a class="el" href="structcutlass_1_1gemm_1_1GlobalLoadStream.html#a386f2f4d2e239075f0fe1489ed7788c1">cutlass::gemm::GlobalLoadStream< Operand, LoadIterator_, StoreIterator_, Transformer_ ></a>
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<li>mean
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: <a class="el" href="structcutlass_1_1Distribution.html#a776df53c7ad1b7de983c9f9d17d7438c">cutlass::Distribution</a>
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, <a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1RandomGaussianFunc_1_1Params.html#a07d12eba25e8e2e9da03ce735b1b8113">cutlass::reference::device::detail::RandomGaussianFunc< Element >::Params</a>
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, <a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1RandomGaussianFunc.html#a5da7c7b953aa178bb356ba8578245d89">cutlass::reference::host::detail::RandomGaussianFunc< Element ></a>
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, <a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1RandomGaussianFunc_3_01complex_3_01Element_01_4_01_4.html#ad8c60b0630a2867fd80a0d09a3cf63cd">cutlass::reference::host::detail::RandomGaussianFunc< complex< Element > ></a>
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</li>
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<li>min
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: <a class="el" href="structcutlass_1_1Distribution.html#a846430e3a21ed25c779fc6e714bc1bcc">cutlass::Distribution</a>
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, <a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1RandomUniformFunc_1_1Params.html#a5f49e5175fa2fc3c3c7fb495fe6958db">cutlass::reference::device::detail::RandomUniformFunc< Element >::Params</a>
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, <a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1RandomUniformFunc.html#ab3de08baeb9246f5472a1c8ec19c68fa">cutlass::reference::host::detail::RandomUniformFunc< Element ></a>
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, <a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1RandomUniformFunc_3_01complex_3_01Element_01_4_01_4.html#a005299ae9f0e4533f6847a7f6ff9e6ad">cutlass::reference::host::detail::RandomUniformFunc< complex< Element > ></a>
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</li>
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<li>minimum_compute_capability
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: <a class="el" href="structcutlass_1_1library_1_1TileDescription.html#a25557ce7220e284c095ef7f691b16fa0">cutlass::library::TileDescription</a>
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</li>
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<li>mma
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: <a class="el" href="classcutlass_1_1gemm_1_1warp_1_1MmaTensorOp.html#a2eddba2b1644d2df5ade4de535b95ed7">cutlass::gemm::warp::MmaTensorOp< Shape_, ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_, LayoutC_, Policy_, PartitionsK_, AccumulatorsInRowMajor, PartitionsN_, Enable ></a>
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, <a class="el" href="classcutlass_1_1gemm_1_1warp_1_1MmaVoltaTensorOp.html#aa4837cb1fd3eac3713e99d0625f14842">cutlass::gemm::warp::MmaVoltaTensorOp< Shape_, ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_, LayoutC_, Policy_, Enable ></a>
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</li>
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<li>msg
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: <a class="el" href="classcutlass_1_1cuda__exception.html#af748a69a87ad9863985f6a77260ba77a">cutlass::cuda_exception</a>
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