Changes between Version 35 and Version 36 of MatrixMultiply

Timestamp:

Jul 7, 2010 8:22:32 PM (14 years ago)

Author:

nakasato

Comment:

--

MatrixMultiply

@@ -1 +1 @@
 = Matrix Multiply on GPU =
-We have implemented single/double precision matrix multiply programs for RV770/Cypress. In our implementation, we use two input streams for computing C=AB. One is transposed input matrix A (i.e. column major) and other is input matrix B in normal format (i.e. row major). Output matrix C is also row major. We adopted 8x8 block for single precision and 4x4 for double precision. Here is benchmark result for each case. Note only kernel execution time is measured.
+We have implemented single/double precision matrix multiply programs for RV770/Cypress. In our implementation, we use two input streams for computing C=AB. One is transposed input matrix A (i.e. column major) and other is input matrix B in normal format (i.e. row major). Output matrix C is also row major. We adopted 8x8 block for single precision and 4x4 for double precision. Here is benchmark result for each case. __Note we only measure kernel execution time.__
 
 Update: we put double-double (DD) precision performance. In this case, we used 2x2 block. On Cypress architecture GPU, we take advantage of FMA_64 instruction. For MAD peak in DD, we assume one DD operation takes 20 DP operations(ops) without FMA and 15 ops with FMA. Precisely, DD add and DD mul without FMA takes ~ 20 ops while DD mul with FMA only takes ~ 8 ops. Even without FMA_64 instruction, we can use MULADD instruction to reduce op count in DD mul. On RV770, we have 13% better performance as indicated with the row with MAD.