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Protecting Real-Time GPU Kernels on Integrated CPU-GPU SoC Platforms

Authors Waqar Ali, Heechul Yun

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Subject Classification

ACM Subject Classification
  • Software and its engineering → Real-time schedulability
  • Computer systems organization → Heterogeneous (hybrid) systems
  • Computer systems organization → Processors and memory architectures
Keywords
  • GPU
  • memory bandwidth
  • resource contention
  • CPU throttling
  • fair scheduler

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Abstract

Integrated CPU-GPU architecture provides excellent acceleration capabilities for data parallel applications on embedded platforms while meeting the size, weight and power (SWaP) requirements. However, sharing of main memory between CPU applications and GPU kernels can severely affect the execution of GPU kernels and diminish the performance gain provided by GPU. For example, in the NVIDIA Jetson TX2 platform, an integrated CPU-GPU architecture, we observed that, in the worst case, the GPU kernels can suffer as much as 3X slowdown in the presence of co-running memory intensive CPU applications. In this paper, we propose a software mechanism, which we call BWLOCK++, to protect the performance of GPU kernels from co-scheduled memory intensive CPU applications.

Cite As Get BibTex

Waqar Ali and Heechul Yun. Protecting Real-Time GPU Kernels on Integrated CPU-GPU SoC Platforms. In 30th Euromicro Conference on Real-Time Systems (ECRTS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 106, pp. 19:1-19:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.ECRTS.2018.19

Author Details

Waqar Ali
  • University of Kansas, Lawrence, USA
Heechul Yun
  • University of Kansas, Lawrence, USA

Supplementary Materials

References

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