Use of GPU architecture to optimize Rabin fingerprint data chunking algorithm by concurrent programmin

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.