University of Illinois Urbana-Champaign

Optimizing parallel I/O performance of HPC applications

Behzad, Babak

Content Files
BEHZAD-DISSERTATION-2015.pdf

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https://hdl.handle.net/2142/89120

Description

Title
Optimizing parallel I/O performance of HPC applications
Author(s)
Behzad, Babak
Issue Date
2015-11-23
Director of Research (if dissertation) or Advisor (if thesis)
Snir, Marc
Doctoral Committee Chair(s)
Snir, Marc
Committee Member(s)
  • Winslett, Marianne
  • Gropp, William
  • Hildebrand, Dean
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2016-03-02T20:23:36Z
Keyword(s)
  • High Performance Computing (HPC)
  • Parallel Computing
  • Parallel I/O
  • Big Data
  • Storage Performance Tuning
  • Autotuning
Abstract
Parallel I/O is an essential component of modern High Performance Computing (HPC). Obtaining good I/O performance for a broad range of applications on diverse HPC platforms is a major challenge, in part because of complex inter-dependencies between I/O middleware and hardware. The parallel file system and I/O middleware layers all offer optimization parameters that can, in theory, result in better I/O performance. Unfortunately, the right combination of parameters is highly dependent on the application, HPC platform, and problem size/concurrency. Scientific application developers do not have the time or expertise to take on the substantial burden of identifying good parameters for each problem configuration. They resort to using system defaults, a choice that frequently results in poor I/O performance. We expect this problem to be compounded on exascale class machines, which will likely have a deeper software stack with hierarchically arranged hardware resources. We present a line of solution to this problem containing an autotuning system for optimizing I/O performance, I/O performance modeling, I/O tuning, I/O kernel generation, and I/O patterns. We demonstrate the value of these solution across platforms, applications, and at scale.
Graduation Semester
2015-12
Type of Resource
text
Permalink
http://hdl.handle.net/2142/89120
Copyright and License Information
Copyright 2015 Babak Behzad

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