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A tutorial on Reed-Solomon coding for fault-tolerance in RAID-like systems
Software—Practice & Experience
Communications of the ACM
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SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Fast GPU ray tracing of dynamic meshes using geometry images
GI '06 Proceedings of Graphics Interface 2006
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Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
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FAST '07 Proceedings of the 5th USENIX conference on File and Storage Technologies
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FAST '07 Proceedings of the 5th USENIX conference on File and Storage Technologies
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ICPP '10 Proceedings of the 2010 39th International Conference on Parallel Processing
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FAST'04 Proceedings of the 3rd USENIX conference on File and storage technologies
GPUstore: harnessing GPU computing for storage systems in the OS kernel
Proceedings of the 5th Annual International Systems and Storage Conference
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Reed–Solomon coding is a method for generating arbitrary amounts of erasure correction information from original data via matrix–vector multiplication in finite fields. Previous work has shown that modern CPUs are not well-matched to this type of computation, requiring applications that depend on Reed–Solomon coding at high speeds (such as high-performance storage arrays) to use hardware implementations. This work demonstrates that high performance is possible with current cost-effective graphics processing units across a wide range of operating conditions and describes how performance will likely evolve in similar architectures. It describes the characteristics of the graphics processing unit architecture that enable high-speed Reed–Solomon coding. A high-performance practical library, Gibraltar, has been prototyped that performs Reed–Solomon coding on graphics processors in a manner suitable for storage arrays, along with applications with similar data resiliency needs. This library enables variably resilient erasure correcting codes to be used in a broad range of applications. Its performance is compared with that of a widely available CPU implementation, and a rationale for its API is presented. Its practicality is demonstrated through a usage example. Copyright © 2011 John Wiley & Sons, Ltd. (Gibraltar is available at , along with sample applications to test it.)