A buffering approach to manage I/O in a normalized cross-correlation earthquake detection code for large seismic datasets

Dawei Mu, Pietro Cicotti, Yifeng Cui, En-Jui Lee, Po Chen

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

Continued advances in high-performance computing architectures constantly move the computational performance forward widening performance gap with I/O. As a result, I/O plays an increasingly critical role in modern data-intensive scientific applications. We have developed a high-performance GPU-based software called cuNCC, which is designed to calculate seismic waveform similarity for subjects like hypocenter estimates and small earthquake detection. GPU's acceleration greatly reduced the compute time and we are currently investigating I/O optimizations, to tackle this new performance bottleneck. In order to find an optimal I/O solution for our cuNCC code, we had performed a series of I/O benchmark tests and implemented buffering in CPU memory to manage the output transfers. With this preliminary work, we were able to establish that buffering improves the I/O bandwidth achieved, but is only beneficial when I/O bandwidth is limited, since the cost of the additional memory copy may exceed improvement in I/O. However, in realistic environment where I/O bandwidth per node is limited, and small I/O transfers are penalized, this technique will improve overall performance. In addition, by using a large memory system, the point at which computing has to stop to wait for I/O is delayed, enablingfast computations on larger data sets.

原文English
主出版物標題PEARC 2017 - Practice and Experience in Advanced Research Computing 2017
主出版物子標題Sustainability, Success and Impact
發行者Association for Computing Machinery
ISBN(電子)9781450352727
DOIs
出版狀態Published - 2017 七月 9
事件2017 Practice and Experience in Advanced Research Computing, PEARC 2017 - New Orleans, United States
持續時間: 2017 七月 92017 七月 13

出版系列

名字ACM International Conference Proceeding Series
Part F128771

Other

Other2017 Practice and Experience in Advanced Research Computing, PEARC 2017
國家United States
城市New Orleans
期間17-07-0917-07-13

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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