A problem-based learning approach to GPU computing
A problem-based learning approach to GPU computing
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基于问题的 GPU 计算学习方法
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
J. Westall
中科院分区：
文献类型：
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作者：
R. Geist;J. Levine;J. Westall
与CPU相比，现代GPU表现出每瓦计算性能的高比率，因此当前的超级计算机设计通常包括多个GPU机架，以便以最小的能源成本实现高万亿次浮点运算。因此，GPU编程变得越来越重要，但它仍然是一项具有挑战性的任务。本文介绍了自2010年以来，克莱姆森大学每年为高年级本科生和一年级研究生开设的GPU编程课程。该课程采用基于问题的学习，重点是一个大的，现实世界的问题，特别是偏微分方程的并行解决方案的系统。虽然系统解决偏微分方程是有用的，在其本身的权利，这个问题被用来作为一种工具，探索设计问题，面对那些试图实现新的性能水平的架构。
Compared to CPUs, modern GPUs exhibit a high ratio of computing performance per watt, and so current supercomputer designs often include multiple racks of GPUs in order to achieve high teraflop counts at minimal energy cost. GPU programming is thus becoming increasingly important, and yet it remains a challenging task. This paper describes a course in GPU programming for senior undergraduates and first-year graduates that has been taught at Clemson University annually since 2010. The course uses problem-based learning, with focus on a large, real-world problem, in particular, a system for parallel solution of partial differential equations. Although the system for solving PDEs is useful in its own right, the problem is used as a vehicle in which to explore design issues that face those attempting to achieve new levels of performance on architectures.