Extending OmpSs for OpenCL Kernel Co-Execution in Heterogeneous Systems

Abstract

Abstract-Heterogeneous systems have a very high potential performance but present difficulties in their programming. OmpSs is a well known framework for task based parallel applications, which is an interesting tool to simplify the programming of these systems. However, it does not support the co-execution of a single OpenCL kernel instance on several compute devices. To overcome this limitation, this paper presents an extension of the OmpSs framework that solves two main objectives: the automatic division of datasets among several devices and the management of their memory address spaces. To adapt to different kinds of applications, the data division can be performed by the novel HGuided load balancing algorithm or by the well known Static and Dynamic. All this is accomplished with negligible impact on the programming. Experimental results reveal that there is always one load balancing algorithm that improves the performance and energy consumption of the system.

Trends on heterogeneous and innovative hardware and software systems

“…Perez, Stafford, Beivide, Mateo, Turel, Ayguadé and Martorell propose in Auto-tuned OpenCL kernel co-execution in OmpSs for heterogeneous systems [7] a novel extension to the OmpSs programming model to allow the co-execution of a single OpenCL kernel in several devices, including the Auto-Tune algorithm that provides adaptive load balancing strategies. Experimental results reveal that the co-execution of single kernels on all the devices in the node is beneficial in terms of performance and energy consumption, and that the proposed scheduling algorithm gives the best overall results.…”