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Asynchronous Iterative Algorithm for Computing Incomplete Factorizations on GPUs Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Predicting Performance of Non-contiguous I/O with Machine Learning

Authors : Julian Kunkel, Michaela Zimmer, Eugen Betke

Published in: High Performance Computing

Publisher: Springer International Publishing

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Abstract

Data sieving in ROMIO promises to optimize individual non-contiguous I/O. However, making the right choice and parameterizing its buffer size accordingly are non-trivial tasks, since predicting the resulting performance is difficult. Since many performance factors are not taken into account by data sieving, extracting the optimal performance for a given access pattern and system is often not possible. Additionally, in Lustre, settings such as the stripe size and number of servers are tunable, yet again, identifying rules for the data-centre proves challenging indeed.
In this paper, we (1) discuss limitations of data sieving, (2) apply machine learning techniques to build a performance predictor, and (3) learn and extract best practices for the settings from the data. We used decision trees as these models can capture non-linear behavior, are easy to understand and allow for extraction of the rules used. Even though this initial research is based on decision trees, with sparse training data, the algorithm can predict many cases sufficiently. Compared to a standard setting, the decision trees created are able to improve performance significantly and we can derive expert knowledge by extracting rules from the learned tree. Applying the scheme to a set of experimental data improved the average throughput by 25–50 % of the best parametrization’s gain. Additionally, we demonstrate the versatility of this approach by applying it to the porting system of DKRZ’s next generation supercomputer and discuss achievable performance gains.

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previous chapter Dynamically Adaptable I/O Semantics for High Performance Computing
next chapter A Best Practice Analysis of HDF and NetCDF- Using Lustre
Footnotes
1
Experimental data is taken from Schmidtke’s thesis [15].
 
2
Note that for a tree of depth one, 80 choices are made for which no measurement is available; these values are excluded from the calculation of the average performance. For bigger trees, less than a handful of choices are not quantifiable. Therefore, we believe this comparison to be fair.
 
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Metadata
Title
Predicting Performance of Non-contiguous I/O with Machine Learning
Authors
Julian Kunkel
Michaela Zimmer
Eugen Betke
Copyright Year
2015
Book
High Performance Computing

Print ISBN: 978-3-319-20118-4

Electronic ISBN: 978-3-319-20119-1

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-20119-1_19