Maximizing speedup through self-tuning of processor allocation

Abstract

Addresses the problem of maximizing application speedup through run-time self-selection of an appropriate number of processors on which to run. Automatic run-time selection of processor allocations is important because many parallel applications exhibit peak speedups at allocations that are data- or time-dependent. We propose the use of a run-time system that: (a) dynamically measures job efficiencies at different allocations, (b) uses these measurements to calculate speedups, and (c) automatically adjusts a job's processor allocation to maximize its speedup. Using a set of 10 applications that includes both hand-coded parallel programs and compiler-parallelized sequential programs, we show that our run-time system can reliably determine dynamic allocations that match the best possible static allocation, and that it has the potential to find dynamic allocations that outperform any static allocation.