Adaptive Parallelism in Compiler-Parallelized Code

As moderate-scale multiprocessors become widely used, we foresee an increased demand for effective compiler parallelization and efficient management of parallelism. While parallelizing compilers are achieving success at identifying parallelism, they are less adept at pre-determining the degree of parallelism in different program phases. Thus, a compiler-parallelized application may execute on more processors than it can effectively use, a waste of computational resources that becomes more acute as number of processors increases, particularly for systems used as multiprogrammed compute servers. This paper examines the dynamic parallelism behavior of multiprogrammed workloads using programs from the Specfp95 and Nas benchmark suites, automatically parallelized by the Stanford SUIF compiler. Our results demonstrate that even the programs with good overall speedups display wide variability in the number of processors each phase (or loop) can exploit. We propose and evaluate a run-time syst..