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.
All Science Journal Classification (ASJC) codes
- Nuclear and High Energy Physics
- Nuclear Energy and Engineering
- Electrical and Electronic Engineering
Maceina, T. J., & Manduchi, G. (2017). Assessment of General Purpose GPU Systems in Real-Time Control. IEEE Transactions on Nuclear Science, 64(6), 1455 - 1460. . https://doi.org/10.1109/TNS.2017.2691061