Performance Evaluation of Blocking and Non-Blocking Concurrent Queues on GPUs

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 <em>blocking</em> and <em>non-blocking</em> 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.

Name thesis:degree_name

M.S. in Engineering Science

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer and Information Science

Year dc:date.available

2019

Author dc:creator

• Pourmeidani, Hossein

Contributors dc:contributor

• Byunghyun Jang
• Conrad Cunningham
• Feng Wang

• capitalism
• industrial
• labor
• work
• Computer Sciences

Repository record dc:identifier

https://egrove.olemiss.edu/etd/1588

OAI identifier oai:identifier

oai:egrove.olemiss.edu:etd-2587