research-article

PTask: operating system abstractions to manage GPUs as compute devices

Authors:

Christopher J. Rossbach,

Mark Silberstein,

Emmett WitchelAuthors Info & Claims

SOSP '11: Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles

Pages 233 - 248

https://doi.org/10.1145/2043556.2043579

Published: 23 October 2011 Publication History

Abstract

We propose a new set of OS abstractions to support GPUs and other accelerator devices as first class computing resources. These new abstractions, collectively called the PTask API, support a dataflow programming model. Because a PTask graph consists of OS-managed objects, the kernel has sufficient visibility and control to provide system-wide guarantees like fairness and performance isolation, and can streamline data movement in ways that are impossible under current GPU programming models.

Our experience developing the PTask API, along with a gestural interface on Windows 7 and a FUSE-based encrypted file system on Linux show that the PTask API can provide important system-wide guarantees where there were previously none, and can enable significant performance improvements, for example gaining a 5× improvement in maximum throughput for the gestural interface.

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Published In

cover image ACM Conferences

SOSP '11: Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles

October 2011

417 pages

ISBN:9781450309776

DOI:10.1145/2043556

General Chair:
Ted Wobber
MSR Silicon Valley
,
Program Chair:
Peter Druschel
MPI-SWS

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 23 October 2011

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https://doi.org/10.1109/TPDS.2024.3429010
Sinha SDwivedi SAzizian M(2024)Towards Deterministic End-to-end Latency for Medical AI Systems in NVIDIA Holoscan2024 ACM/IEEE 15th International Conference on Cyber-Physical Systems (ICCPS)10.1109/ICCPS61052.2024.00028(235-246)Online publication date: 13-May-2024
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