2026-09-19T10:21:09Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/89120 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Snir, Marc Snir, Marc Winslett, Marianne Gropp, William Hildebrand, Dean Behzad, Babak 2016-03-02T20:23:36Z 2016-03-02T20:23:36Z 2018-03-03T10:15:34Z 2015-11-23 2015-12 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. Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2017-12-01 The student, Babak Behzad, accepted the attached license on 2015-11-20 at 13:31. The student, Babak Behzad, submitted this Dissertation for approval on 2015-11-20 at 13:45. This Dissertation was approved for publication on 2015-11-23 at 13:41. DSpace SAF Submission Ingestion Package generated from Vireo submission #8816 on 2016-03-02 at 14:06:01 Made available in DSpace on 2016-03-02T20:23:36Z (GMT). No. of bitstreams: 4 BEHZAD-DISSERTATION-2015.pdf: 2579773 bytes, checksum: e393f126dc8ca1229a99a983f6997d0b (MD5) ProQuestSign-BabakBehzad.pdf: 268035 bytes, checksum: f29cad9dd3b5d70629f7fa2b72875efc (MD5) BabakBehzad_CS_PhDThesis_Final_2015.zip: 19558300 bytes, checksum: ab9daeaa06c6144e16b86542046ac95e (MD5) LICENSE.txt: 4209 bytes, checksum: 648e218a7b53159b5f1fbf5c97c5c231 (MD5) Previous issue date: 2015-11-23 Embargo set by: Seth Robbins for item 91322 Lift date: 2018-03-02T20:24:31Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system U of I Only Restriction Lifted for Item 91322 on 2018-03-03T10:15:34Z. application/pdf http://hdl.handle.net/2142/89120 en Copyright 2015 Babak Behzad High Performance Computing (HPC) Parallel Computing Parallel I/O Big Data Storage Performance Tuning Autotuning Optimizing parallel I/O performance of HPC applications text text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.