Cooperative GPGPU Scheduling for Consolidating Server Workloads

DOI Web Site 参考文献40件
  • SUZUKI Yusuke
    Department of Information and Computer Science, Keio University
  • YAMADA Hiroshi
    Department of Computer and Information Sciences, Tokyo University of Agriculture and Technology
  • KATO Shinpei
    Department of Computer Science, The University of Tokyo
  • KONO Kenji
    Department of Information and Computer Science, Keio University

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<p>Graphics processing units (GPUs) have become an attractive platform for general-purpose computing (GPGPU) in various domains. Making GPUs a time-multiplexing resource is a key to consolidating GPGPU applications (apps) in multi-tenant cloud platforms. However, advanced GPGPU apps pose a new challenge for consolidation. Such highly functional GPGPU apps, referred to as GPU eaters, can easily monopolize a shared GPU and starve collocated GPGPU apps. This paper presents GLoop, which is a software runtime that enables us to consolidate GPGPU apps including GPU eaters. GLoop offers an event-driven programming model, which allows GLoop-based apps to inherit the GPU eaters' high functionality while proportionally scheduling them on a shared GPU in an isolated manner. We implemented a prototype of GLoop and ported eight GPU eaters on it. The experimental results demonstrate that our prototype successfully schedules the consolidated GPGPU apps on the basis of its scheduling policy and isolates resources among them.</p>

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