A (ir)regularity-aware task scheduler for heterogeneous platforms

Abstract

This paper addresses the design, implementation and validation of an e ective scheduling scheme for both regular and irregular applications on heterogeneous platforms. The scheduler uses an empirical performance model to dynamically schedule the workload, organized into a given number of chunks, and follows the Heterogeneous Earliest Finish Time (HEFT) scheduling algorithm, which ranks the tasks based on both their computation and communication costs. The evaluation of the proposed approach is based on three case studies { the SAXPY, the FFT and the Barnes-Hut algorithms { two regular and one irregular application. The scheduler was evaluated on a heterogeneous platform with one quad-core CPU-chip accelerated by one or two GPU devices, embedded in the GAMA framework. The evaluation runs measured the e ectiveness, the e ciency and the scalability of the proposed method. Results show that the proposed model was e active in addressing both regular and irregular applications, on heterogeneous platforms, while achieving ideal ( 100%) levels of e ciency in the irregular Barnes-Hut algorithm.