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Title: Taming parallel I/O complexity with auto-tuning

We present an auto-tuning system for optimizing I/O performance of HDF5 applications and demonstrate its value across platforms, applications, and at scale. The system uses a genetic algorithm to search a large space of tunable parameters and to identify effective settings at all layers of the parallel I/O stack. The parameter settings are applied transparently by the auto-tuning system via dynamically intercepted HDF5 calls. To validate our auto-tuning system, we applied it to three I/O benchmarks (VPIC, VORPAL, and GCRM) that replicate the I/O activity of their respective applications. We tested the system with different weak-scaling configurations (128, 2048, and 4096 CPU cores) that generate 30 GB to 1 TB of data, and executed these configurations on diverse HPC platforms (Cray XE6, IBM BG/P, and Dell Cluster). In all cases, the auto-tuning framework identified tunable parameters that substantially improved write performance over default system settings. In conclusion, we consistently demonstrate I/O write speedups between 2x and 100x for test configurations.
Authors:
 [1] ;  [1] ;  [2] ;  [3] ;  [3] ;  [4] ;  [4] ;  [1]
  1. Univ. of Illinois, Urbana-Champaign, IL (United States)
  2. Rice Univ., Houston, TX (United States)
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  4. The HDF Group, Champaign, IL (United States)
Publication Date:
OSTI Identifier:
1311633
Report Number(s):
LBNL--1005953
Journal ID: ISSN 1063-9635; ir:1005953
Grant/Contract Number:
AC02-06CH11357
Type:
Accepted Manuscript
Journal Name:
Proceedings of the ACM/IEEE Supercomputing Conference
Additional Journal Information:
Journal Volume: 2013; Conference: SC13-International Conference for High Performance Computing, Networking, Storage and Analysis, Denver, CO (United States), 17-22 Nov 2013; Journal ID: ISSN 1063-9635
Publisher:
ACM/IEEE
Research Org:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Org:
Computational Research Division; USDOE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING parallel I/O; auto-tuning; performance optimization; parallel file systems