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.
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.