Changes between Version 8 and Version 9 of Fast_GEMM_implementation_On_Cypress

Timestamp:

Nov 2, 2010 12:50:19 AM (14 years ago)

Author:

nakasato

Comment:

--

Fast_GEMM_implementation_On_Cypress

@@ -8 +8 @@
 == abstract ==
 We present benchmark results of optimized dense matrix multiplication
-kernels for a Cypress GPU. We write general matrix multiply (GEMM) kernels
+kernels for Cypress GPU. 
+We write general matrix multiply (GEMM) kernels
 for single (SP), double (DP) and double-double (DDP) precision. 
-Our SGEMM and DGEMM kernels show 73% and 87% of 
+Our SGEMM and DGEMM kernels show ~ 2 Tflop/s and ~ 470 Glop/s, respectively.
+These results for SP and DP correspond to 73% and 87% of 
 the theoretical performance of the GPU, respectively.
-Currently, our SGEMM and DGEMM kernels are fastest 
-with one GPU chip to our knowledge.
+Currently, our SGEMM and DGEMM kernels are fastest with one GPU chip to our knowledge.
 Furthermore, the performance of our matrix multiply kernel in DDP is 31 Gflop/s.
 This performance in DDP is more than 200 times faster than the performance 
-in DDP on single core of a recent CPU (with mpack version 0.6.5).
-We describe our GEMM kernels with main focus on the SGEMM implementation
-since all GEMM kernels share common programming and optimization techniques.
-While a conventional wisdom of GPU programming recommends us 
-to heavily use shared memory on GPUs,  
-we show that texture cache is very effective on the Cypress architecture. 
+results in DDP on single core of a recent CPU (with mpack version 0.6.5).We describe our GEMM kernels with main focus on the SGEMM implementation
+since all GEMM kernels share common programming and optimization techniques.While a conventional wisdom of GPU programming recommends us 
+to heavily use shared memory on GPUs,  we show that texture cache is very effective on the Cypress architecture. 
+
 
 == Sample program for DGEMM ==