Title

Implementing Big Data Algorithms on GPUs

Faculty Mentor(s)

Dr. Bryson Payne

Campus

Dahlonega

Proposal Type

Poster

Subject Area

Computer Science

Location

Library Third Floor, Open Area

Start Date

2-4-2014 11:00 AM

End Date

2-4-2014 1:00 PM

Description/Abstract

Algorithms for processing large, unstructured data sets have shown great promise in implementations on modern graphics processors (GPUs), with many implementations reporting 20-70x speedup over comparable CPU-only versions of the same algorithms. In this senior project research, our goal is to implement an efficient, highly scalable SQLite database on GPU, test an optimized implementation of a data sorting algorithm like GPU-Quicksort, and demonstrate the speed potential of GPU-enhanced computation on a typical big-data search and aggregation algorithm like MapReduce.

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Apr 2nd, 11:00 AM Apr 2nd, 1:00 PM

Implementing Big Data Algorithms on GPUs

Library Third Floor, Open Area

Algorithms for processing large, unstructured data sets have shown great promise in implementations on modern graphics processors (GPUs), with many implementations reporting 20-70x speedup over comparable CPU-only versions of the same algorithms. In this senior project research, our goal is to implement an efficient, highly scalable SQLite database on GPU, test an optimized implementation of a data sorting algorithm like GPU-Quicksort, and demonstrate the speed potential of GPU-enhanced computation on a typical big-data search and aggregation algorithm like MapReduce.