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

Yongbin Gu, Wenxuan Wu, Yunfan Li, Lizhong Chen
School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, USA
arXiv:2007.09822 [cs.AR], (20 Jul 2020)

@misc{gu2020uvmbench,

   title={UVMBench: A Comprehensive Benchmark Suite for Researching Unified Virtual Memory in GPUs},

   author={Yongbin Gu and Wenxuan Wu and Yunfan Li and Lizhong Chen},

   year={2020},

   eprint={2007.09822},

   archivePrefix={arXiv},

   primaryClass={cs.AR}

}

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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