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CUTLASS 2.0 (#62)

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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.
This commit is contained in:
Andrew Kerr
2019-11-19 16:55:34 -08:00
committed by GitHub
parent b5cab177a9
commit fb335f6a5f
5434 changed files with 599799 additions and 250176 deletions
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@ -26,8 +28,9 @@
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
<td id="projectlogo"><img alt="Logo" src="cutlass-logo-small.png"/></td>
<td id="projectalign" style="padding-left: 0.5em;">
<div id="projectname">Cutlass
<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_func_c.html#index_c"><span>c</span></a></li>
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@ -70,7 +131,16 @@ $(function() {
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<h3><a id="index_0x7e"></a>- ~ -</h3><ul>
<h3><a class="anchor" id="index_0x7e"></a>- ~ -</h3><ul>
<li>~allocation()
: <a class="el" href="structcutlass_1_1device__memory_1_1allocation.html#af205dd59859566d6fab5ac3eea8de7bf">cutlass::device_memory::allocation&lt; T &gt;</a>
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<li>~HostTensor()
: <a class="el" href="classcutlass_1_1HostTensor.html#a068d76dabce39c48b617ee7fe8d7edb8">cutlass::HostTensor&lt; Element_, Layout_ &gt;</a>
</li>
<li>~Operation()
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