Supporting Preemptive Task Executions and Memory Copies in GPGPUs

Abstract

GPGPUs (General Purpose Graphic Processing Units) provide massive computational power. However, applying GPGPU technology to real-time computing is challenging due to the non-preemptive nature of GPGPUs. Especially, a job running in a GPGPU or a data copy between a GPGPU and CPU is non-preemptive. As a result, a high priority job arriving in the middle of a low priority job execution or memory copy suffers from priority inversion. To address the problem, we present a new lightweight approach to supporting preemptive memory copies and job executions in GPGPUs. Moreover, in our approach, a GPGPU job and memory copy between a GPGPU and the hosting CPU are run concurrently to enhance the responsiveness. To show the feasibility of our approach, we have implemented a prototype system for preemptive job executions and data copies in a GPGPU. The experimental results show that our approach can bound the response times in a reliable manner. In addition, the response time of our approach is significantly shorter than those of the unmodified GPGPU runtime system that supports no preemption and an advanced GPGPU model designed to support prioritization and performance isolation via preemptive data copies.