Runtime Support for Multiple Offload-Based Programming Models on Clustered Manycore Accelerators

Abstract

Heterogeneous systems coupling a main host processor with one or more manycore accelerators are being adopted virtually at every scale to achieve ever-increasing GOps/Watt targets. The increased hardware complexity of such systems is paired at the application level by a growing number of applications concurrently running on the system. Techniques that enable efficient accelerator resources sharing, supporting multiple programming models will thus be increasingly important for future heterogeneous SoCs. In this paper we present a runtime system for a cluster-based manycore accelerator, optimized for the concurrent execution of offloaded computation kernels from different programming models. The runtime supports spatial partitioning, where clusters can be grouped into several virtual accelerator instances. Our runtime design is modular and relies on a generic component for resource (cluster) scheduling, plus specialized components which deploy generic offload requests into the target programming model semantics. We evaluate the proposed runtime system on two real heterogeneous systems, focusing on two concrete use cases: i) single-user, multi-application high-end embedded systems and ii) multi-user, multi-workload low-power microservers. In the first case, our approach achieves 93 percent efficiency in terms of available accelerator resource exploitation. In the second case, our support allows 47 percent performance improvement compared to single-programming model systems.