Section: New Results

Granularity Control for Parallel Programs

Participant : Arthur Charguéraud.

Arthur Charguéraud studied the development of techniques for controlling granularity in parallel programs. Granularity control is an essential problem because creating too many tasks may induce overwhelming overheads, while creating too few tasks may harm the ability to process tasks in parallel. Granularity control turns out to be especially challenging for nested parallel programs, i.e., programs in which parallel constructs such as fork-join or parallel-loops can be nested arbitrarily.

The proposed approach combines the use of asymptotic complexity functions provided by the programmer, with runtime measurements to estimate the constant factors that apply. Exploiting these two sources of information makes it possible to predict with reasonable accuracy the execution time of tasks. Such predictions may be used to guide the generation of tasks, by sequentializing computations of sufficiently small size. An analysis is developed, establishing that task creation overheads are indeed bounded to a small fraction of the total runtime. These results extend prior work by the same authors [52], extending them with a carefully-designed algorithm for ensuring convergence of the estimation of the constant factors deduced from the measures, even in the face of noise and cache effects, which are taken into account in the analysis. The approach is demonstrated on a range of benchmarks taken from the state-of-the-art PBBS benchmark suite. These results have been accepted for publication at PPoPP'19 [14].