QoS Management on Heterogeneous Architecture for Multiprogrammed, Parallel, and Domain-Specific Applications

• Published in: IEEE systems journal Vol. 11; no. 4; pp. 2096 - 2107
• Main Authors: Ying Zhang, Li Zhao, Illikkal, Ramesh, Iyer, Ravi, Herdrich, Andrew, Lu Peng
• Format: Journal Article
• Language: English
• Published: IEEE 01.12.2017
• Subjects: Bandwidth; Benchmark testing; Heterogeneous systems; Memory management; Multicore processing; performance attributes; Quality of service
• ISSN: 1932-8184; 1937-9234
• DOI: 10.1109/JSYST.2015.2464153

Quality-of-service (QoS) management is widely employed to provide differentiable performance to programs with distinctive priorities on conventional chip-multiprocessor (CMP) platforms. Recently, heterogeneous architecture integrating diverse processor cores on the same silicon has been proposed to better serve various application domains, and it is expected to be an important design paradigm of future processors. Therefore, the QoS management on emerging heterogeneous systems will be of great significance. Workloads on heterogeneous architectures can be multiprogrammed, heterogeneous, and/or domain specific depending on the form factor and device of interest. Considering the diverse characteristics of these three classes of workloads is important when managing QoS on heterogeneous architectures. For example, for parallel applications, considering the diverse characteristics of thread synchronization, data sharing, and parallelization pattern of representative parallel applications, governing the execution of multiple parallel programs with different performance requirements becomes a complicated yet significant problem. In this paper, we study QoS management for multiprogrammed, parallel, and domain-specific applications running on heterogeneous CMP systems. We comprehensively assess a series of task-to-core mapping policies on a real heterogeneous hardware (QuickIA) by characterizing their impacts on performance of individual applications. Our evaluation results show that the proposed QoS policies are effective to improve the performance of programs with highest priority while striking good tradeoff with system fairness.