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API Comparison of CPU-To-GPU Command Offloading Latency on Embedded Platforms (Artifact)

Authors Roberto Cavicchioli, Nicola Capodieci , Marco Solieri , Marko Bertogna

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DARTS.5.1.4.pdf
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  • 3 pages

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ACM Subject Classification
  • Computer systems organization → System on a chip
  • Computer systems organization → Real-time system architecture
Keywords
  • GPU
  • Applications
  • Heterogeneus systems

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Abstract

High-performance heterogeneous embedded platforms allow offloading of parallel workloads to an integrated accelerator, such as General Purpose-Graphic Processing Units (GP-GPUs). A time-predictable characterization of task submission is a must in real-time applications. We provide a profiler of the time spent by the CPU for submitting stereotypical GP-GPU workload shaped as a Deep Neural Network of parameterized complexity. The submission is performed using the latest API available: NVIDIA CUDA, including its various techniques, and Vulkan. Complete automation for the test on Jetson Xavier is also provided by scripts that install software dependencies, run the experiments, and collect results in a PDF report.

Cite As Get BibTex

Roberto Cavicchioli, Nicola Capodieci, Marco Solieri, and Marko Bertogna. API Comparison of CPU-To-GPU Command Offloading Latency on Embedded Platforms (Artifact). In Special Issue of the 31st Euromicro Conference on Real-Time Systems (ECRTS 2019). Dagstuhl Artifacts Series (DARTS), Volume 5, Issue 1, pp. 4:1-4:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/DARTS.5.1.4

Author Details

Roberto Cavicchioli
  • Università di Modena e Reggio Emilia, Italy
Nicola Capodieci
  • Università di Modena e Reggio Emilia, Italy
Marco Solieri
  • Università di Modena e Reggio Emilia, Italy
Marko Bertogna
  • Università di Modena e Reggio Emilia, Italy

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  MD5 Sum: 3978b2398eab0687e51009e681c0ada9 (Get MD5 Sum)

Related Article

  • Roberto Cavicchioli, Nicola Capodieci, Marco Solieri, and Marko Bertogna, "Novel Methodologies for Predictable CPU-To-GPU Command Offloading", in 31st Euromicro Conference on Real-Time Systems (ECRTS 2019), LIPIcs, Vol. 133, pp. 22:1 - 22:22, 2019. https://dx.doi.org/10.4230/LIPIcs.ECRTS.2019.22
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