2020/05/27 by Martin Karp, Niclas Jansson, Karp, Martin +7
Computer Science · #Advanced Data Storage Technologies #Distributed #Embedded Systems Design Techniques #FOS: Computer and information sciences #G.4 #J.2 #Parallel #Parallel Computing and Optimization Techniques #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2005.13425
openalex publication_date 2020/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In the CFD solver Nek5000, the computation is dominated by the evaluation of small tensor operations. Nekbone is a proxy app for Nek5000 and has previously been ported to GPUs with a mixed OpenACC and CUDA approach. In this work, we continue this effort and optimize the main tensor-product operation in Nekbone further. Our optimization is done in CUDA and uses a different, 2D, thread structure to make the computations layer by layer. This enables us to use loop unrolling as well as utilize registers and shared memory efficiently. Our implementation is then compared on both the Pascal and Volta GPU architectures to previous GPU versions of Nekbone as well as a measured roofline. The results show that our implementation outperforms previous GPU Nekbone implementations by 6-10%. Compared to the measured roofline, we obtain 77 - 92% of the peak performance for both Nvidia P100 and V100 GPUs for inputs with 1024 - 4096 elements and polynomial degree 9.