0000000000671662

AUTHOR

James Morse

showing 2 related works from this author

MERCURY: A Transparent Guided I/O Framework for High Performance I/O Stacks

2017

The performance gap between processors and I/O represents a serious scalability limitation for applications running on computing clusters. Parallel file systems often provide mechanisms that allow programmers to disclose their I/O pattern knowledge to the lower layers of the I/O stack through a hints API. This information can be used by the file system to boost the application performance. Unfortunately, programmers rarely make use of these features, missing the opportunity to exploit the full potential of the storage system. In this paper we propose MERCURY, a transparent guided I/O framework able to optimize file I/O patterns in scientific applications, allowing users to control the I/O b…

File systemPOSIXComputer scienceScalabilityNon-blocking I/OOperating systemNetwork File SystemAsynchronous I/OLinux kernelLustre (file system)computer.software_genrecomputer2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)
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POSTER: Optimizing scientific file I/O patterns using advice based knowledge

2014

Before us, other works have used data prefetching to boost applications performance [1]–[8]. Our approach differs from these works since we do not rely on precise I/O pattern information to predict and prefetch every chunck of data in advance. Instead we use data prefetching to group many small requests in a few big ones, improving applications performance and utilization of the whole storage system. Moreover, we provide the infrastructure that enables users to access file system specific interfaces for guided I/O without modifying applications and hiding the intrinsic complexity that such interfaces introduce.

Input/outputInstruction prefetchFile systemDatabasebusiness.industryComputer scienceComputer data storagecomputer.software_genrebusinesscomputerAdvice (complexity)2014 IEEE International Conference on Cluster Computing (CLUSTER)
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