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
+102
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<title>Cutlass: Class Members - Variables</title>
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<title>CUTLASS: Class Members - Variables</title>
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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_f.html#index_f"><span>f</span></a></li>
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<li><a href="functions_vars_g.html#index_g"><span>g</span></a></li>
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<li><a href="functions_vars_h.html#index_h"><span>h</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><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_d"></a>- d -</h3><ul>
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<h3><a class="anchor" id="index_d"></a>- d -</h3><ul>
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<li>D
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: <a class="el" href="structcutlass_1_1gemm_1_1GemmDesc.html#adf95451422c529587d55aac0fecf0d9f">cutlass::gemm::GemmDesc< AType_, BType_, CType_, DType_, SType_, Index_ ></a>
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: <a class="el" href="structcutlass_1_1library_1_1GemmArguments.html#a2f4c0652e6632aebe6d9159c425ecc3f">cutlass::library::GemmArguments</a>
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, <a class="el" href="structcutlass_1_1library_1_1GemmArrayArguments.html#ae6ccc3b91e9a77ad170d276f70fe2c30">cutlass::library::GemmArrayArguments</a>
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</li>
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<li>d_a
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: <a class="el" href="structcutlass_1_1reduction_1_1BatchedReductionTraits_1_1Params.html#af1b12ba220602692e84616b420b00f1c">cutlass::reduction::BatchedReductionTraits< ScalarA_, ScalarC_, ScalarD_, ScalarAlphaBeta_, ScalarAccum_, ReductionSize_, OutputTile_, SubTile_, ThreadShape_, Index_, BlockSwizzle_, maxInReg_, maxOutReg_, Functor_ >::Params</a>
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@@ -83,6 +143,26 @@ $(function() {
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<li>d_d
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: <a class="el" href="structcutlass_1_1reduction_1_1BatchedReductionTraits_1_1Params.html#abf9744373a72f3819a616b5a5b3bff22">cutlass::reduction::BatchedReductionTraits< ScalarA_, ScalarC_, ScalarD_, ScalarAlphaBeta_, ScalarAccum_, ReductionSize_, OutputTile_, SubTile_, ThreadShape_, Index_, BlockSwizzle_, maxInReg_, maxOutReg_, Functor_ >::Params</a>
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</li>
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<li>delta
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: <a class="el" href="structcutlass_1_1Distribution.html#a77613df810c3f8f68b595599802cedb4">cutlass::Distribution</a>
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</li>
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<li>destination
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: <a class="el" href="structcutlass_1_1reduction_1_1kernel_1_1ReduceSplitK_1_1Params.html#a08089218798599f5f47184f8c94723cb">cutlass::reduction::kernel::ReduceSplitK< Shape_, OutputOp_, ReductionOp_, PartitionsPerStage >::Params</a>
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</li>
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<li>destination_ref
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: <a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DirectEpilogueTensorOp_1_1Params.html#a89d406e705b7817243e3aa9d4253bb14">cutlass::epilogue::threadblock::DirectEpilogueTensorOp< Shape_, Operator_, PartitionsK, Element_, OutputOp_, ConvertOp_ >::Params</a>
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</li>
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<li>diag
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: <a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1TensorFillDiagonalFunc_1_1Params.html#abcbca40684cd478a53c0cc80c8e418e1">cutlass::reference::device::detail::TensorFillDiagonalFunc< Element, Layout >::Params</a>
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, <a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1TensorUpdateDiagonalFunc_1_1Params.html#adc562519d503d235a49b11a8f2fc2bf6">cutlass::reference::device::detail::TensorUpdateDiagonalFunc< Element, Layout >::Params</a>
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, <a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1TensorFillDiagonalFunc.html#a027d9ae77e068454e8df798018276c18">cutlass::reference::host::detail::TensorFillDiagonalFunc< Element, Layout ></a>
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</li>
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<li>digits
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: <a class="el" href="structstd_1_1numeric__limits_3_01cutlass_1_1half__t_01_4.html#a92152311525685a53c6a0db4cb74f193">std::numeric_limits< cutlass::half_t ></a>
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</li>
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<li>dst
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: <a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1TensorCopyIf.html#a0d94963e36e238233ddb550845b37004">cutlass::reference::host::detail::TensorCopyIf< DstElement, DstLayout, SrcElement, SrcLayout, F ></a>
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</li>
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<li>dynamic_smem
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: <a class="el" href="structcutlass_1_1KernelLaunchConfiguration.html#a4a6ac693d4284c84301279219623e2bc">cutlass::KernelLaunchConfiguration</a>
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</li>
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@@ -90,9 +170,9 @@ $(function() {
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</a> 1.8.14
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