Auto-tuning Dense Matrix Multiplication for GPGPU with Cache

Abstract

In this paper we discuss about our experiences in improving the performance of GEMM (both single and double precision) on Fermi architecture using CUDA, and how the new features of Fermi such as cache affect performance. It is found that the addition of cache in GPU on one hand helps the processers take advantage of data locality occurred in runtime but on the other hand renders the dependency of performance on algorithmic parameters less predictable. Auto tuning then becomes a useful technique to address this issue. Our auto-tuned SGEMM and DGEMM reach 563 GFlops and 253 GFlops respectively on Tesla C2050. The design and implementation entirely use CUDA and C and have not benefited from tuning at the level of binary code.