From 6cb88d53eb8d5cfd18189f57cbe252c287a1b5d5 Mon Sep 17 00:00:00 2001
From: Duane Merrill <duane.merrill@gmail.com>
Date: Tue, 5 Dec 2017 22:58:12 -0500
Subject: [PATCH] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 11fe5a1c..d346ca84 100644
--- a/README.md
+++ b/README.md
@@ -25,7 +25,7 @@ in CUDA C++"](https://devblogs.nvidia.com/parallelforall/cutlass-linear-algebra-
 
 # Performance
 
-![ALT](/media/cutlass-performance-plot.png "Relative performance of CUTLASS and cuBLAS for large matrices")
+<p align="center"><img src=/media/cutlass-performance-plot.png></p>
 
 CUTLASS primitives are very efficient.  When used to construct device-wide GEMM kernels, 
 they exhibit performance comparable to cuBLAS for scalar GEMM