Jack Jean (Committee Member), Meilin Liu (Advisor), T.K. Prasad (Committee Member)
Master of Science (MS)
With the limitations given by the power consumption (power wall), memory wall and the instruction level parallelism, the computing industry has turned its direction to multi-core architectures. Nowadays, the multi-core and many-core architectures are becoming the trend of the processor design. But how to exploit these architectures is the primary challenge for the research community. To take advantage of the multi-core architectures, the software design has undergone fundamental changes.
Sorting is a fundamental, important problem in computer science. It is utilized in many applications such as databases and search engines. In this thesis, we will investigate and auto-tune two parallel sorting algorithms, i.e., radix sort and sample sort on two parallel architectures, the many-core nVIDIA CUDA enabled graphics processors, and the multi-core Cell Broadband Engine. We redesign and manually tune these two parallel sorting algorithms to take advantage of multiple-level parallelism simultaneously, i.e., thread level parallelism, loop level parallelism, data level parallelism (SIMD instructions). At the same time, we try to take advantage of the high-speed shared memory. The experimental results showed that the parallel implementation of these two sorting algorithms on these two multi-core architectures achieved significant performance improvement compared to the corresponding sequential version.
Department or Program
Department of Computer Science
Year Degree Awarded
Copyright 2011, all rights reserved. This open access ETD is published by Wright State University and OhioLINK.