数据的快速及时供应对访存密集型程序的性能有着直接的影响.提出一种多倍数据供应MDS(Multiple Data Supply)的编译优化方法,在不增加处理器设计复杂度的前提下,利用现有处理器的高带宽,一次对内存进行多个数据的读写,减少访存次数,提高应用程序效率.在编译优化阶段,利用自动向量化技术,生成向量形式的树结构,增加一条新的扩展路径来处理从向量化的树结构到底层结构的扩展.针对向量化后树结构的多样性问题,设计新的优化遍以及RAC(Register Assignment Chain)替换算法进行专门处理.在龙芯3A处理器平台上,对SPEC-CPU2000的测试,CINT程序平均性能提升11.6%,CFP程序平均性能提升14.4%.
Developing parallel applications on heterogeneous processors is facing the challenges of 'memory wall',due to limited capacity of local storage,limited bandwidth and long latency for memory access. Aiming at this problem,a parallelization approach was proposed with six memory optimization schemes for CG,four schemes of them aiming at all kinds of sparse matrix-vector multiplication (SPMV) operation. Conducted on IBM QS20,the parallelization approach can reach up to 21 and 133 times speedups with size A and B,respectively,compared with single power processor element. Finally,the conclusion is drawn that the peak bandwidth of memory access on Cell BE can be obtained in SPMV,simple computation is more efficient on heterogeneous processors and loop-unrolling can hide local storage access latency while executing scalar operation on SIMD cores.
