【摘要】 讨论在Fermi结构GPU使用CUDA对GEMM(单精度和双精度)算法进行优化,以及Fermi体系结构的新特性(如缓存)对性能的影响。GPU缓存一方面可以提高处理器在运行时数据访问的局部性,另一方面使得代码性能对与性能相关算法参数的依赖变得不可预测。自动优化技术可以用来解决这一问题。自动优化的SGEMM和DGEMM代码在Tesla C2050 GPU上达到了563GFlops和253GFlops的性能。代码使用CUDA和C语言进行实现,未进行二进制代码级别的优化。更多还原

【Abstract】 Automatic optimization of GEMM algorithm on Fermi GPU and the impact of Fermi GPU’s architectural features on performance are discussed in this paper. The cache on GPU can not only improve the data access locality of processors,but also make the code performance relying on relative algorithm parameters unpredictable. Auto-tuning can be used to solve this problem. Auto-tuned SGEMM and DGEMM codes achieve 563 GFlops and 253 GFlops respectively on C2050 GPU. The codes are implemented by using CUDA and C language,but the optimization on the binary level is not involved.更多还原