Electronic Theses and Dissertations

Date of Award


Document Type


Degree Name

M.S. in Engineering Science


Computer and Information Science

First Advisor

Byunghyun Jang

Second Advisor

Conrad Cunningham

Third Advisor

Feng Wang

Relational Format



The efficiency of concurrent data structures is crucial to the performance of multi-threaded programs in shared-memory systems. The arbitrary execution of concurrent threads, however, can result in an incorrect behavior of these data structures. Graphics Processing Units (GPUs) have appeared as a powerful platform for high-performance computing. As regular data-parallel computations are straightforward to implement on traditional CPU architectures, it is challenging to implement them in a SIMD environment in the presence of thousands of active threads on GPU architectures. In this thesis, we implement a concurrent queue data structure and evaluate its performance on GPUs to understand how it behaves in a massively-parallel GPU environment. We implement both blocking and non-blocking approaches and compare their performance and behavior using both micro-benchmark and real-world application. We provide a complete evaluation and analysis of our implementations on an AMD Radeon R7 GPU. Our experiment shows that non-blocking approach outperforms blocking approach by up to 15.1 times when sufficient thread-level parallelism is present.


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