Performance auto-tuning of GPU programs using machine learning

使用机器学习自动调整 GPU 程序的性能

• 批准号: 451889-2013
• 负责人: Abdelrahman, Tarek
• 金额: $ 2.9万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=597133
• 关键词: Performance; auto; tuning; GPU; programs

There is a phenomenal growth in software applications for mobile devices such as smart phones and tablets. This growth increases the pressure on mobile processor manufacturers such as Intel, AMD and Qualcomm to make more powerful processors that consume less power. Today, these manufacturers are making processors with multiple cores (CPUs) combined with a general-purpose graphics-processing unit (GPU) on the same chip. The use of a GPU is attractive because the GPU can deliver a ten-fold increase in speed at a fraction of the power, compared to a CPU. However, this GPU benefit can be achieved only if programs are carefully written and tuned for the GPU--a process that is complex and error prone.

智能手机和平板电脑等移动设备的软件应用出现了显著增长。这种增长增加了英特尔（Intel）、AMD和高通（Qualcomm）等移动处理器制造商的压力，要求它们生产更强大、功耗更低的处理器。今天，这些制造商正在制造多核（cpu）和通用图形处理单元（GPU）在同一芯片上的处理器。GPU的使用很有吸引力，因为与CPU相比，GPU可以在一小部分功率下提供十倍的速度提升。然而，这种GPU的好处只能在仔细编写程序并针对GPU进行调整的情况下才能实现——这是一个复杂且容易出错的过程。