CAJ | 학술논문

当前GPU集群的主流编程模型是MPI与CUDA的松散耦合，采用这种编程模型进行编程，存在编程复杂度大、程序的可移植性差、执行效率低等问题。为此，提出一种面向通用计算GPU集群的任务自动分配系统StreamMAP。对编译器进行改造，以编译制导的方式提供集群任务的计算资源需求，通过运行时系统动态地发现、建立并维护系统资源拓扑，设计一种较为契合GPU集群应用特征的任务分配策略。实验结果表明，StreamMAP系统能降低集群应用程序的编程复杂度，使之较为高效地利用GPU集群的计算资源，且程序的可移植性和可扩展性也得到了保证。
당전GPU집군적주류편정모형시MPI여CUDA적송산우합，채용저충편정모형진행편정，존재편정복잡도대、정서적가이식성차、집행효솔저등문제。위차，제출일충면향통용계산GPU집군적임무자동분배계통StreamMAP。대편역기진행개조，이편역제도적방식제공집군임무적계산자원수구，통과운행시계통동태지발현、건립병유호계통자원탁복，설계일충교위계합GPU집군응용특정적임무분배책략。실험결과표명，StreamMAP계통능강저집군응용정서적편정복잡도，사지교위고효지이용GPU집군적계산자원，차정서적가이식성화가확전성야득도료보증。
MPI+CUDA are the mainstream programming models of current GPU cluster architecture. However, by using such a low level programming model, programmers require detailed knowledge of the underlying architecture, which exerts a heavy burden. Besides, the program is less portability and inefficient. This paper proposes StreamMAP, an automatic task assignment system on GPU clusters. It provides powerful, yet concise language extension suitable to describe the compute resource demands of cluster tasks. It develops a run time system to maintain resource information, and supplies an automatic task assignment for GPU cluster. Experiments show that StreamMAP provides programmability, portability and scalability for GPU cluster application.