Low-overhead dynamic sharing of graphics memory space in GPU virtualization environments

Gu, Minwoo; Park, Younghun; Kim, Young-Jae; Park, Sung-Yong

doi:10.1007/s10586-019-02967-5

Cited by 2 publications

(1 citation statement)

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“…Moreover, UVM enables GPU kernel execution while memory is oversubscribed by automatically evicting data that is no longer needed in the GPU memory to the CPU side. This is extremely important and helpful in facilitating large workloads (especially deep learning models) and GPU virtualization [7], [10] with limited memory sizes.…”

Section: Introductionmentioning

confidence: 99%

UVMBench: A Comprehensive Benchmark Suite for Researching Unified Virtual Memory in GPUs

Gu¹,

Wu²,

Li³

et al. 2020

Preprint

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The recent introduction of Unified Virtual Memory (UVM) in GPUs offers a new programming model that allows GPUs and CPUs to share the same virtual memory space, shifts the complex memory management from programmers to GPU driver/ hardware, and enables kernel execution even when memory is oversubscribed. Meanwhile, UVM may also incur considerable performance overhead due to the tracking and data migration along with the special handling of page faults and page table walk. As UVM is attracting significant attention from the research community to develop innovative solutions to these problems, in this paper, we propose a comprehensive UVM benchmark suite named UVMBench to facilitate future research on this important topic. The proposed UVMBench consists of 34 representative benchmarks from a wide range of application domains. The suite also features unified programming implementation and diverse memory access patterns across benchmarks, thus allowing thorough evaluation and comparison with current state-of-the-art. A set of experiments have been conducted on real GPUs to verify and analyze the benchmark suite behaviors under various scenarios.

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Section: Introductionmentioning

confidence: 99%