A survey of architectural approaches for improving GPGPU performance, programmability and heterogeneity

Abstract

With the skyrocketing advances of process technology, the increased need to process huge amount of data, and the pivotal need for power efficiency, the usage of Graphics Processing Units (GPUs) for General Purpose Computing becomes a trend and natural. GPUs have high computational power and excellent performance per watt, for data parallel applications, relative to traditional multicore processors. GPUs appear as discrete or embedded with Central Processing Units (CPUs), leading to a scheme of heterogeneous computing. Heterogeneous computing brings as many challenges as it brings opportunities. To get the most of such systems, we need to guarantee high GPU utilization, deal with irregular control flow of some workloads, and struggle with far-friendly-programming models. The aim of this paper is to provide a survey about GPUs from two perspectives: (1) architectural advances to improve performance and programmability and (2) advances to enhance CPU–GPU integration in heterogeneous systems. This will help researchers see the opportunities and challenges of using GPUs for general purpose computing, especially in the era of big data and the continuous need of high-performance computing.