Understanding and exploiting the internals of GPU resource allocation for critical systems
Visualitza/Obre
10.1109/ICCAD45719.2019.8942170
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/335884
Tipus de documentText en actes de congrés
Data publicació2019
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
Tots els drets reservats. Aquesta obra està protegida pels drets de propietat intel·lectual i
industrial corresponents. Sense perjudici de les exempcions legals existents, queda prohibida la seva
reproducció, distribució, comunicació pública o transformació sense l'autorització del titular dels drets
ProjecteCOMPUTACION DE ALTAS PRESTACIONES VII (MINECO-TIN2015-65316-P)
SuPerCom - Sustainable Performance for High-Performance Embedded Computing Systems (EC-H2020-772773)
SuPerCom - Sustainable Performance for High-Performance Embedded Computing Systems (EC-H2020-772773)
Abstract
Critical real-time systems require strict resource provisioning in terms of memory and timing. The constant need for higher performance in these systems has led industry to recently include GPUs. However, GPU software ecosystems are by their nature closed source, forcing system engineers to consider them as black boxes, complicating resource provisioning. In this work we reverse engineer the internal operations of the GPU system software to increase the understanding of their observed behaviour and how resources are internally managed. This way, we allow system engineers to accurately determine the exact amount of resources required by their critical systems, avoiding underprovisioning. We first apply our methodology on a wide range of GPU hardware showing its generality in obtaining the properties of the GPU memory allocators. Next, we demonstrate the benefits of such knowledge in resource provisioning of two case studies from the automotive domain, where the actual memory consumption is up to 5.6 × more than the memory requested by the application.
CitacióCalderón, A. [et al.]. Understanding and exploiting the internals of GPU resource allocation for critical systems. A: IEEE/ACM International Conference on Computer-Aided Design. "2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD): November 4–7, 2019, Westin Westminster, CO: digest of technical papers". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 1-8. ISBN 978-1-7281-2350-9. DOI 10.1109/ICCAD45719.2019.8942170.
ISBN978-1-7281-2350-9
Versió de l'editorhttps://ieeexplore.ieee.org/document/8942170
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