Online data analysis and reduction: An important Co-design motif for extreme-scale computers
Ian T Foster, Mark Andrew Ainsworth, Julie Bessac, Franck Cappello, Jong Choi, Sheng Di, Zichao Wendy Di, Ali Murat Gok, Hanqi Guo, Kevin Huck, Christopher Kelly, Scott Klasky, Kerstin Kleese van Dam, Xin Liang, Kshitij R. Mehta, Manish Parashar, Tom Peterka, Line Catherine Pouchard, Tong Shu, Ozan Tuğluk · The International Journal of High Performance Computing Applications · 2021
A growing disparity between supercomputer computation speeds and I/O rates means that it is rapidly becoming infeasible to analyze supercomputer application output only after that output has been written to a file system. Instead, data-generating applications must run concurrently with data reduction and/or analysis operations, with which they exchange information via high-speed methods such as interprocess communications. The resulting parallel computing motif, online data analysis and reduction (ODAR), has important implications for both application and HPC systems design. Here we introduce the ODAR motif and its co-design concerns, describe a co-design process for identifying and addressing those concerns, present tools that assist in the co-design process, and present case studies to illustrate the use of the process and tools in practical settings.