Optimizing parallel I/O performance of HPC applications

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2016

Author dc:creator

• Behzad, Babak

Contributors dc:contributor

• Snir, Marc
• Winslett, Marianne
• Gropp, William
• Hildebrand, Dean

• High Performance Computing (HPC)
• Parallel Computing
• Parallel I/O
• Big Data
• Storage Performance Tuning
• Autotuning

Statement dc:rights

• Copyright 2015 Babak Behzad

Language dc:language

en

Handle dc:identifier

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

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/89120