Visible to the public Performance Analysis and Optimization of Nonhydrostatic ICosahedral Atmospheric Model (NICAM) on the K Computer and TSUBAME2.5

TitlePerformance Analysis and Optimization of Nonhydrostatic ICosahedral Atmospheric Model (NICAM) on the K Computer and TSUBAME2.5
Publication TypeConference Paper
Year of Publication2016
AuthorsYashiro, Hisashi, Terai, Masaaki, Yoshida, Ryuji, Iga, Shin-ichi, Minami, Kazuo, Tomita, Hirofumi
Conference NameProceedings of the Platform for Advanced Scientific Computing Conference
Date PublishedJune 2016
PublisherACM
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-4126-4
KeywordsClimate, Extreme-Scale Computing, GCM, K Computer, Memory-Bound, OpenACC, pubcrawl, pubcrawl170201, science of security, TSUBAME2.5, Weather
Abstract

We summarize the optimization and performance evaluation of the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) on two different types of supercomputers: the K computer and TSUBAME2.5. First, we evaluated and improved several kernels extracted from the model code on the K computer. We did not significantly change the loop and data ordering for sufficient usage of the features of the K computer, such as the hardware-aided thread barrier mechanism and the relatively high bandwidth of the memory, i.e., a 0.5 Byte/FLOP ratio. Loop optimizations and code cleaning for a reduction in memory transfer contributed to a speed-up of the model execution time. The sustained performance ratio of the main loop of the NICAM reached 0.87 PFLOPS with 81,920 nodes on the K computer. For GPU-based calculations, we applied OpenACC to the dynamical core of NICAM. The performance and scalability were evaluated using the TSUBAME2.5 supercomputer. We achieved good performance results, which showed efficient use of the memory throughput performance of the GPU as well as good weak scalability. A dry dynamical core experiment was carried out using 2560 GPUs, which achieved 60 TFLOPS of sustained performance.

URLhttps://dl.acm.org/doi/10.1145/2929908.2929911
DOI10.1145/2929908.2929911
Citation Keyyashiro_performance_2016