NDLI: Dynamic Model-Driven Parallel I/O Performance Tuning

Content Provider	IEEE Xplore Digital Library
Author	Behzad, B. Byna, S. Wild, S.M. Prabhat Snir, M.
Copyright Year	2015
Description	Author affiliation: Argonne Nat. Lab., Argonne, IL, USA (Wild, S.M.) \|\| Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA (Behzad, B.; Snir, M.) \|\| Lawrence Berkeley Nat. Lab., Berkeley, CA, USA (Byna, S.; Prabhat)
Abstract	Parallel I/O performance depends highly on the interactions among multiple layers of the parallel I/O stack. The most common layers include high-level I/O libraries, MPI-IO middleware, and parallel file system. Each of these layers offers various tunable parameters to control intermediary data transfer points and the final data layout. Due to the interdependencies and the number of combinations of parameters, finding a good set of parameter values for a specific application's I/O pattern is challenging. Recent efforts, such as autotuning with genetic algorithms (GAs) and analytical models, have several limitations. For instance, analytical models fail to capture the dynamic nature of shared supercomputing systems and are application-specific. GA-based tuning requires running many time-consuming experiments for each input size. In this paper, we present a strategy to generate automatically an empirical model for a given application pattern. Using a set of real measurements from running an I/O kernel as training set, we generate a nonlinear regression model. We use this model to predict the top-20 tunable parameter values that give efficient I/O performance and rerun the I/O kernel to select the best set of parameter under the current conditions as tunable parameters for future runs of the same I/O kernel. Using this approach, we demonstrate 6X - 94X speedup over default I/O time for different I/O kernels running on multiple HPC systems. We also evaluate performance by identifying interdependencies among different sets of tunable parameters.
Starting Page	184
Ending Page	193
File Size	984578
Page Count	10
File Format	PDF
ISBN	9781467365987
DOI	10.1109/CLUSTER.2015.37
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Training Computational modeling Tuning Predictive models Libraries Atmospheric modeling Performance Modeling Parallel I/O Parallel I/O Tuning Performance Optimization
Content Type	Text
Resource Type	Article

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