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  • Simulation-Aided Performance Evaluation of Server-Side Input/Output Optimizations (Michael Kuhn, Julian Kunkel, Thomas Ludwig), In 20th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, pp. 562–566, (Editors: Rainer Stotzka, Michael Schiffers, Yiannis Cotronis), IEEE Computer Society (Los Alamitos, Washington, Tokyo), PDP 2012, Munich Network Management Team, Garching, Germany, ISBN: 978-0-7695-4633-9, ISSN: 1066-6192, 2012
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Abstract

The performance of parallel distributed file systems suffers from many clients executing a large number of operations in parallel, because the I/O subsystem can be easily overwhelmed by the sheer amount of incoming I/O operations. Many optimizations exist that try to alleviate this problem. Client-side optimizations perform preprocessing to minimize the amount of work the file servers have to do. Server-side optimizations use server-internal knowledge to improve performance. The HDTrace framework contains components to simulate, trace and visualize applications. It is used as a testbed to evaluate optimizations that could later be implemented in real-life projects. This paper compares existing client-side optimizations and newly implemented server-side optimizations and evaluates their usefulness for I/O patterns commonly found in HPC. Server-directed I/O chooses the order of non-contiguous I/O operations and tries to aggregate as many operations as possible to decrease the load on the I/O subsystem and improve overall performance. The results show that server-side optimizations beat client-side optimizations in terms of performance for many use cases. Integrating such optimizations into parallel distributed file systems could alleviate the need for sophisticated client-side optimizations. Due to their additional knowledge of internal workflows server-side optimizations may be better suited to provide high performance in general.

BibTeX

@inproceedings{SPEOSIOKKL12,
	author	 = {Michael Kuhn and Julian Kunkel and Thomas Ludwig},
	title	 = {{Simulation-Aided Performance Evaluation of Server-Side Input/Output Optimizations}},
	year	 = {2012},
	booktitle	 = {{20th Euromicro International Conference on Parallel, Distributed and Network-Based Processing}},
	editor	 = {Rainer Stotzka and Michael Schiffers and Yiannis Cotronis},
	publisher	 = {IEEE Computer Society},
	address	 = {Los Alamitos, Washington, Tokyo},
	pages	 = {562--566},
	conference	 = {PDP 2012},
	organization	 = {Munich Network Management Team},
	location	 = {Garching, Germany},
	isbn	 = {978-0-7695-4633-9},
	issn	 = {1066-6192},
	doi	 = {https://doi.org/10.1109/PDP.2012.35},
	abstract	 = {The performance of parallel distributed file systems suffers from many clients executing a large number of operations in parallel, because the I/O subsystem can be easily overwhelmed by the sheer amount of incoming I/O operations. Many optimizations exist that try to alleviate this problem. Client-side optimizations perform preprocessing to minimize the amount of work the file servers have to do. Server-side optimizations use server-internal knowledge to improve performance. The HDTrace framework contains components to simulate, trace and visualize applications. It is used as a testbed to evaluate optimizations that could later be implemented in real-life projects. This paper compares existing client-side optimizations and newly implemented server-side optimizations and evaluates their usefulness for I/O patterns commonly found in HPC. Server-directed I/O chooses the order of non-contiguous I/O operations and tries to aggregate as many operations as possible to decrease the load on the I/O subsystem and improve overall performance. The results show that server-side optimizations beat client-side optimizations in terms of performance for many use cases. Integrating such optimizations into parallel distributed file systems could alleviate the need for sophisticated client-side optimizations. Due to their additional knowledge of internal workflows server-side optimizations may be better suited to provide high performance in general.},
}

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