Performance-Based and Aging-Aware Resource Allocation for Concurrent GPU Applications

Abstract

GPUs are an important part in the effort to overcome performance thresholds and unlock the true potential of computing as they offer increased computational capabilities and are cost efficient. Until now, GPUs are designed to execute one application at a time so the field of concurrent GPU applications is not exhaustively explored. When multiple applications that belong to different types, e.g., compute or memory intensive, are executed on the same platform concurrently, significant performance degradation and imbalances in terms of component aging may occur. These imbalances can lead to weak system reliability, further performance degradation and acceleration of failure time. In this paper, we propose a resource allocating algorithm that mitigates the aging imbalances without inserting overhead during the execution, limiting aging imbalance among Streaming Multiprocessors (SMs) to a standard deviation of 0.4%. Additionally, the proposed algorithm improves SM allocation for each application, achieving up to 33% higher throughput.