Automatically Tuning Parallel and Parallelized Programs

In today’s multicore era, parallelization of serial code is essential in order to exploit the architectures performance potential. Parallelization, especially of legacy code, however, proves to be a challenge as manual efforts must either be directed towards algorithmic modifications or towards analysis of computationally intensive sections of code for the best possible parallel performance, both of which are difficult and time-consuming. Automatic parallelization uses sophisticated compile-time techniques in order to identify parallelism in serial programs, thus reducing the burden on the program developer. Similar sophistication is needed to improve the performance of hand-parallelized programs. A key difficulty is that optimizing compilers are generally unable to estimate the performance of an application or even a program section at compile time, and so the task of performance improvement invariably rests with the developer. Automatic tuning uses static analysis and runtime performance metrics to determine the best possible compile-time approach for optimal application performance. This paper describes an offline tuning approach that uses a source-to-source parallelizing compiler, Cetus, and a tuning framework to tune parallel application performance. The implementation uses an existing, generic tuning algorithm called Combined Elimination to study the effect of serializing parallelizable loops based on measured whole program execution time, and provides a combination of parallel loops as an outcome that ensures to equal or improve performance of the original program. We evaluated our algorithm on a suite of hand-parallelized C benchmarks from the SPEC OMP2001 and NAS Parallel benchmarks and provide two sets of results. The first ignores hand-parallelized loops and only tunes application performance based on Cetus-parallelized loops. The second set of results considers the tuning of additional parallelism in hand-parallelized code. We show that our implementatio