Eliminating Redundant Computation and Exposing Parallelism through Data-Triggered Threads

摘要

Unlike threads in parallel programs created by conventional programming, data-triggered threads are initiated when a memory value is changed. By expressing computation through these threads, computation is executed only when the data changes and is skipped whenever the data does not change. The authors' model achieves performance speedups of up to 5.9x, averaging 45.6 percent, with SPEC2000 benchmarks.