For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Cooperative GPGPU Scheduling for Consolidating Server Workloads
Yusuke SUZUKI Hiroshi YAMADA Shinpei KATO Kenji KONO
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2018/12/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Software System
GPGPU, cloud computing, operating systems,
Full Text: PDF(3.2MB)
>>Buy this Article
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.