Authors: Izzet Yildirim (Illinois Institute of Technology), Hariharan Devarajan (Lawrence Livermore National Laboratory), Anthony Kougkas and Xian-He Sun (Illinois Institute of Technology), and Kathryn Mohror (Lawrence Livermore National Laboratory)
Abstract: Real-world HPC workloads impose a lot of pressure on storage systems as they are highly data dependent. On the other hand, as a result of recent developments in storage hardware, it is expected that the storage diversity in upcoming HPC systems will grow. This growing complexity in the storage system presents challenges to users, and often results in I/O bottlenecks due to inefficient usage. There have been several studies on reducing I/O bottlenecks. The earliest attempts worked to solve this problem by combining I/O characteristics with expert insight. The recent attempts rely on the performance analysis from the I/O characterization tools. However, the problem is multifaceted with many metrics to consider, hence difficult to do manually, even for experts. In this work, we develop a methodology that produces a multifaceted view of the I/O behavior of a workload to identify potential I/O bottlenecks automatically.
Best Poster Finalist (BP): no
Poster summary: PDF
Back to Poster Archive Listing