Assessment of General Purpose GPU Systems in Real-Time Control

T.J. Maceina, G. Manduchi

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

The recent advances in GPU technology is offering great prospects in computation. However, the penetration of the GPU technology in real-Time control has been somewhat limited due to two main reasons: 1) control algorithms for real-Time applications involving highly parallel computation are not very common in practical applications and 2) the excellent performance in computation of GPUS is paid for by a penalty in memory transfer. As a consequence, GPU applications for real-Time controls suffer from an often unacceptable latency. We present the factors that affect the performance of GPUS in real-Time applications in fusion research in order to provide some hints to designers facing the option of using either a multithreaded, multicore CPU application or a GPU. In particular, we consider GPU usage in two common use cases in real-Time applications in fusion research: dense matrix-vector multiplication for large state space-based control and online image analysis for feature extraction in camera-based diagnostics. Two applications mimicking the two use cases have been developed using the Tesla K40 GPU architecture, and the performance results are reported.
Original languageEnglish
Article number7892841
Pages (from-to)1455 - 1460
Number of pages6
JournalIEEE Transactions on Nuclear Science
Volume64
Issue number6
DOIs
Publication statusPublished - 1 Jun 2017
Externally publishedYes

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All Science Journal Classification (ASJC) codes

  • Nuclear and High Energy Physics
  • Nuclear Energy and Engineering
  • Electrical and Electronic Engineering

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