Kernel-Based Resource Allocation for Improving GPU Throughput While Minimizing the Activity Divergence of SMs

Abstract

Graphics Processing Units (GPUs) have been established as a major part of modern computing systems. As technology scales down, GPUs integrate more computing elements that accelerate massively parallel applications. Due to the increase of GPU cores, sophisticated resource allocation techniques are required in order to take advantage of the underlying architecture. At the same time, circuit aging rises as a challenging problem due to the reduction of chip dimensions, temperature, and utilization of GPU resources. Aging increases the switching delay of the transistors resulting in performance degradation, synchronization and lifetime problems. This becomes more prominent in GPUs due to the different behavior and characteristics of GPU applications. Applications utilize differently the computing resources and they consequently result in imbalanced aging. In this paper, we employ a kernel-based resource allocation for optimizing GPU throughput while simultaneously minimizing the activity divergence of Streaming Multiprocessors (SMs). The proposed methodology achieves improved throughput by effectively utilizing the characteristics of the application kernels offloaded on the platform, and reduced aging divergence among the SMs. Results show that our technique improves the GPU throughput by 18% and 13.8% for different GPU micro-architectures, while minimizing the aging divergence up to 89.6% comparing to other aging-aware methodologies.