Data deduplication is introduced as a popular technique used to increase storage efficiency used in various data centers and corporate backup environments. There are various caching techniques and metadata checking available to prevent excessive file scanning. Due to the nature of content addressable chunking algorithm being a serial operation, the data deduplication chunking process often times become the performance bottleneck. This project introduces a parallelized Rabin fingerprint algorithm suitable for GPU hardware architecture that aims to optimize the performance of the deduplication process.
|Commitee:||Hoffman, Michael, Lam, Shui|
|School:||California State University, Long Beach|
|Department:||Computer Engineering and Computer Science|
|School Location:||United States -- California|
|Source:||MAI 55/04M(E), Masters Abstracts International|
|Keywords:||Content addressable storage, Cuda, Deduplication, GPUs, Rabin fingerprint|
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