Optimizing parallel I/O performance of HPC applications

Behzad, Babak

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

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

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

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

Copyright and License Information

Copyright 2015 Babak Behzad

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