Analyzing and Fine Tuning Workgroup Size of OpenCL Program on Diverse Computing Devices

• Main Authors: Yu, Chia-Lin, 余佳霖
• Other Authors: Tsao, Shiao-Li
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/7uggft

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 105 === The performance of an OpenCL kernel is significantly influenced by both the hardware and software attributes. To attain superior performance, users need to search through a huge tuning space to determine proper parameters. However, with the growth of variety and heterogeneity on the underlying computing devices, efficient and easy-to-apply automatic tuning technique become an essential. Among all possible tuning knobs, workgroup size, which would largely affect the performance, is commonly used for general OpenCL programs. However, existing portable tuning approaches can only be leveraged once the target device is available. In this thesis, we analyze the key factors that cause performance discrepancies under different workgroup sizes and present a dedicate workgroup size selection model. By abstracting the hardware details and modeling only the key factors, our approach provides a portable and efficient way to determine the suitable workgroup size without the requirement of target device. Among all the seven benchmarks and five distinct devices, our model is shown to filter out an average of 95.1% of the possible workgroup sizes with negligible overhead, while achieving an average of 95.7% best-known performance with the best candidate and 92.2% of the best-known performance with the worst candidate.