Data centric framework for large-scale high-performance parallel computation

Kenji Ono, Yasuhiro Kawashima, Tomohiro Kawanabe

Research output: Contribution to journalConference articlepeer-review

7 Citations (Scopus)


Supercomputer architectures are being upgraded using different level of parallelism to improve computing performance. This makes it difficult for scientists to develop high performance code in a short time. From the viewpoint of productivity and software life cycle, a concise yet effective infrastructure is required to achieve parallel processing. In this paper, we propose a usable building block framework to build parallel applications on large-scale Cartesian data structures. The proposed framework is designed such that each process in a simulation cycle can easily access the generated data files with usable functions. This framework enables us to describe parallel applications with fewer lines of source code, and hence, it contributes to the productivity of the software. Further, this framework was considered for improving performance, and it was confirmed that the developed flow simulator based on this framework demonstrated considerably excellent weak scaling performance on the K computer.

Original languageEnglish
Pages (from-to)2336-2350
Number of pages15
JournalProcedia Computer Science
Publication statusPublished - 2014
Externally publishedYes
Event14th Annual International Conference on Computational Science, ICCS 2014 - Cairns, QLD, Australia
Duration: Jun 10 2014Jun 12 2014

All Science Journal Classification (ASJC) codes

  • Computer Science(all)


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