Exploiting coarse-grained task, data, and pipeline parallelism in stream programs

As multicore architectures enter the mainstream, there is a press- ing demand for high-level programming models that can effectively map to them. Stream programming offers an attractive way to ex- pose coarse-grained parallelism, as streaming applications (image, video, DSP, etc.) are naturally represented by independent filters that communicate over explicit data channels. In this paper, we demonstrate an end-to-end stream compiler that attains robust multicore performance in the face of varying ap- plication characteristics. As benchmarks exhibit different amounts of task, data, and pipeline parallelism, we exploit all types of par- allelism in a unified manner in order to achieve this generality. Our compiler, which maps from the StreamIt language to the 16-core Raw architecture, attains a 11.2x mean speedup over a single-core baseline, and a 1.84x speedup over our previous work.