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An Open-Source Virtualization Layer for CUDA Applications

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Euro-Par 2020: Parallel Processing Workshops (Euro-Par 2020)

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Abstract

GPUs have achieved widespread adoption for High-Performance Computing and Cloud applications. However, the closed-source nature of CUDA has hindered the development of otherwise commonly used virtualization techniques. In this paper, we evaluate the feasibility of building a GPU virtualization layer that isolates the GPU and CPU parts of CUDA applications to achieve better control of the interactions between applications and the CUDA libraries. We present our open-source tool that transparently intercepts CUDA library calls and executes them in a separate process using remote procedure calls. This allows the execution of CUDA applications on machines without a GPU and provides a basis for the development of tools that require fine-grained control of the GPU resources, such as checkpoint/restore and job schedulers.

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Notes

  1. 1.

    The Top500 list (https://www.top500.org/) from November 2019 that ranks the fastest HPC clusters contains no cluster that uses GPUs from different vendors.

  2. 2.

    The code is available at https://github.com/RWTH-ACS/cricket.

  3. 3.

    As of writing the latest GPU generation and CUDA version are Turing and CUDA 10.2.

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Acknowledgment

This research and development was supported by the German Federal Ministry of Education and Research under Grant 01IH16010C (Project ENVELOPE).

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Correspondence to Niklas Eiling .

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Eiling, N., Lankes, S., Monti, A. (2021). An Open-Source Virtualization Layer for CUDA Applications. In: Balis, B., et al. Euro-Par 2020: Parallel Processing Workshops. Euro-Par 2020. Lecture Notes in Computer Science(), vol 12480. Springer, Cham. https://doi.org/10.1007/978-3-030-71593-9_13

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  • DOI: https://doi.org/10.1007/978-3-030-71593-9_13

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