Graduation Year
2016
Document Type
Thesis
Degree
M.S.C.S.
Degree Name
MS in Computer Science (M.S.C.S.)
Degree Granting Department
Computer Science and Engineering
Major Professor
Yicheng Tu, Ph.D.
Committee Member
Srinivas Katkoori, Ph.D.
Committee Member
Swaroop Ghosh, Ph.D.
Keywords
Database performance, High performance computing, Big data, Parallel queries on GPU, Data warehouse queries
Abstract
Current database management systems use Graphic Processing Units (GPUs) as dedicated accelerators to process each individual query, which results in underutilization of GPU. When a single query data warehousing workload was run on an open source GPU query engine, the utilization of main GPU resources was found to be less than 25%. The low utilization then leads to low system throughput. To resolve this problem, this paper suggests a way to transfer all of the desired data into the global memory of GPU and keep it until all queries are executed as one batch. The PCIe transfer time from CPU to GPU is minimized, which results in better performance in less time of overall query processing. The execution time was improved by up to 40% when running multiple queries, compared to dedicated processing.
Scholar Commons Citation
Cyrus, Sam, "Fast Computation on Processing Data Warehousing Queries on GPU Devices" (2016). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/6214
