Exploiting co-execution with oneAPI: heterogeneity from a modern perspective

TL;DR

This paper explores how oneAPI's co-execution strategies can optimize performance and energy efficiency in heterogeneous systems by leveraging static and dynamic load balancing on CPU-GPU architectures.

Contribution

It introduces co-execution strategies within oneAPI, integrating load-balancing algorithms and evaluating their impact on HPC benchmarks with improved efficiency.

Findings

Dynamic load balancing enhances performance.

Unified shared memory further improves efficiency.

Co-execution is beneficial for heterogeneous HPC workloads.

Abstract

Programming efficiently heterogeneous systems is a major challenge, due to the complexity of their architectures. Intel oneAPI, a new and powerful standards-based unified programming model, built on top of SYCL, addresses these issues. In this paper, oneAPI is provided with co-execution strategies to run the same kernel between different devices, enabling the exploitation of static and dynamic policies. On top of that, static and dynamic load-balancing algorithms are integrated and analyzed. This work evaluates the performance and energy efficiency for a well-known set of regular and irregular HPC benchmarks, using an integrated GPU and CPU. Experimental results show that co-execution is worthwhile when using dynamic algorithms, improving efficiency even more when using unified shared memory.