@inproceedings{c7d811e0d8594419b9887e1d8e387080,
title = "Improving parallel I/O autotuning with performance modeling",
abstract = "Various layers of the parallel I/O subsystem offer tunable parameters for improving I/O performance on large-scale computers. However, searching through a large parameter space is challenging. We are working towards an autotuning framework for determining the parallel I/O parameters that can achieve good I/O performance for different data write patterns. In this paper, we characterize parallel I/O and discuss the development of predictive models for use in effectively reducing the parameter space. Applying our technique on tuning an I/O kernel derived from a large-scale simulation code shows that the search time can be reduced from 12 hours to 2 hours, while achieving 54X I/O performance speedup.",
keywords = "Autotuning, Parallel I/O, Performance modeling, Performance optimization",
author = "Babak Behzad and Surendra Byna and Wild, {Stefan M.} and Prabhat and Marc Snir",
year = "2014",
doi = "10.1145/2600212.2600708",
language = "English (US)",
isbn = "9781450327480",
series = "HPDC 2014 - Proceedings of the 23rd International Symposium on High-Performance Parallel and Distributed Computing",
publisher = "Association for Computing Machinery",
pages = "253--256",
booktitle = "HPDC 2014 - Proceedings of the 23rd International Symposium on High-Performance Parallel and Distributed Computing",
address = "United States",
note = "23rd ACM Symposium on High-Performance Parallel and Distributed Computing, HPDC 2014 ; Conference date: 23-06-2014 Through 27-06-2014",
}