流应用的特点以及传统处理器在处理流应用上的不足,使得支持数据并行的流处理器的设计成为当前体系结构研究领域的一个热点.文中针对Imagine流处理器体系结构的特点,提出了流分割和流压缩两种流的优化组织方法.模拟结果表明,流分割和流压缩使得流应用程序能充分利用Imagine的并行结构、流水结构和多级带宽存储结构,从而减少流程序的执行时间.
Due to the characteristics of stream applications and the insufficiency of conventional processors when running stream programs, stream processors which support data-level parallelism become the research hotspot. This paper presents two means, stream partition (SP) and stream compression (SC), to optimize streams on Imagine. The results of simulation show that SP and SC can make stream applications take full advantage of the parallel clusters, pipelines and three-level memory hierarchy of the Imagine processor, and then reduce the execution time of stream programs.