From 5bd3f09312121a25e5205f51ade82787b9fda4c6 Mon Sep 17 00:00:00 2001 From: Duane Merrill Date: Tue, 5 Dec 2017 22:53:11 -0500 Subject: [PATCH] Update README.md --- README.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 6787f648..4e5f38cc 100644 --- a/README.md +++ b/README.md @@ -20,15 +20,17 @@ point (FP64) types. Furthermore, CUTLASS demonstrates CUDA's WMMA API for targe the programmable, high-throughput _Tensor Cores_ provided by NVIDIA's Volta architecture and beyond. +For more exposition, see our Parallel Forall blog post ["CUTLASS: Fast Linear Algebra +in CUDA C++"](https://devblogs.nvidia.com/parallelforall/cutlass-linear-algebra-cuda). + +# Performance + ![ALT](/media/cutlass-performance-plot.png "Relative performance of CUTLASS and cuBLAS for large matrices") CUTLASS is very efficient, with performance comparable to cuBLAS for scalar GEMM computations. The above figure shows CUTLASS performance relative to cuBLAS -compiled with CUDA 9.0 running on an NVIDIA Tesla V100 GPU for large matrix -dimensions (M=10240, N=K=4096). - -For more exposition, see our Parallel Forall blog post ["CUTLASS: Fast Linear Algebra -in CUDA C++"](https://devblogs.nvidia.com/parallelforall/cutlass-linear-algebra-cuda). +for large matrix dimensions (M=10240, N=K=4096) running on an NVIDIA Tesla V100 GPU +when compiled with CUDA 9.0. # Project Structure