Adaptive and Architecture-Independent Task Granularity for Recursive Applications

Abstract

In the last few decades, modern applications have become larger and more complex. Among the users of these applications, the need to simplify the process of identifying units of work increased as well. With the approach of tasking models, this want has been satisfied. These models make scheduling units of work much more user-friendly. However, with the arrival of tasking models, came granularity management. Discovering an application’s optimal granularity is a frequent and sometimes challenging task for a wide range of recursive algorithms. Often, finding the optimal granularity will cause a substantial increase in performance.

With that in mind, the quest for optimality is no easy task. Many aspects have to be considered that are directly related to lack or excess of parallelism in applications. There is no general solution as the optimal granularity depends on both algorithm and system characteristics. One commonly used method to find an optimal granularity consists in experimentally tuning an application with different granularities until an optimal is found. This paper proposes several heuristics which, combined with the appropriate monitoring techniques, allow a runtime system to automatically tune the granularity of recursive applications. The solution is independent of the architecture, execution environment or application being tested. A reference implementation in OmpSs—a task-parallel programming model—shows the programmability, ease of use and competitive performance of the proposed solution. Results show that the proposed solution is able to achieve, for any scenario, at least 75% of the performance of optimally tuned applications.