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Proceedings ArticleDOI

Supporting GPU sharing in cloud environments with a transparent runtime consolidation framework

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TLDR
A framework to enable applications executing within virtual machines to transparently share one or more GPUs is presented and it is found that even when contention is high the consolidation algorithm is effective in improving the throughput, and that the runtime overhead of the framework is low.
Abstract
Driven by the emergence of GPUs as a major player in high performance computing and the rapidly growing popularity of cloud environments, GPU instances are now being offered by cloud providers. The use of GPUs in a cloud environment, however, is still at initial stages, and the challenge of making GPU a true shared resource in the cloud has not yet been addressed.This paper presents a framework to enable applications executing within virtual machines to transparently share one or more GPUs. Our contributions are twofold: we extend an open source GPU virtualization software to include efficient GPU sharing, and we propose solutions to the conceptual problem of GPU kernel consolidation. In particular, we introduce a method for computing the affinity score between two or more kernels, which provides an indication of potential performance improvements upon kernel consolidation. In addition, we explore molding as a means to achieve efficient GPU sharing also in the case of kernels with high or conflicting resource requirements. We use these concepts to develop an algorithm to efficiently map a set of kernels on a pair of GPUs. We extensively evaluate our framework using eight popular GPU kernels and two Fermi GPUs. We find that even when contention is high our consolidation algorithm is effective in improving the throughput, and that the runtime overhead of our framework is low.

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Citations
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Proceedings ArticleDOI

Multi-tenancy on GPGPU-based servers

TL;DR: 'Rain', a system level abstraction for GPU "hyperthreading" that makes it possible to efficiently utilize GPUs without compromising fairness among multiple tenant applications, is proposed.
Book ChapterDOI

A general-purpose virtualization service for HPC on cloud computing: an application to GPUs

TL;DR: The generic virtualization service GVirtuS (Generic Virtualization Service), a framework for development of split-drivers for cloud virtualization solutions, focuses on GPU virtualization.
Proceedings ArticleDOI

Energy-Aware Workload Consolidation on GPU

TL;DR: A novel runtime framework is developed that dynamically consolidates instances from different workloads from multiple user processes into a single GPU workload and provides 2X to 22X energy benefit over a multicore CPU.
Proceedings ArticleDOI

COSMIC: middleware for high performance and reliable multiprocessing on xeon phi coprocessors

TL;DR: A new, user-level middleware called COSMIC is proposed that improves performance and reliability of multiprocessing on coprocessors like the Xeon Phi, and increases multiprocessioning reliability by exploiting programmer-specified per-processCoprocessor memory requirements to completely avoid memory oversubscription and crashes.
Journal ArticleDOI

A Cloud Gaming System Based on User-Level Virtualization and Its Resource Scheduling

TL;DR: This paper proposes GCloud, a GPU/CPU hybrid cluster for cloud gaming based on the user-level virtualization technology, and presents a performance model to analyze the server-capacity and games' resource-consumptions, which categorizes games into two types: CPU-critical and memory-of-critical.
References
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Proceedings ArticleDOI

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