TY - GEN
T1 - A problem-based learning approach to GPU computing
AU - Geist, Robert
AU - Levine, Joshua A.
AU - Westall, James
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation under Grant Nos. IIS-1314757 and CNS-1126344. The School of Computing at Clemson University is an NVIDIA GPU Research Center and an NVIDIA GPU Teaching Center. Thanks to David Leubke, CliffWoolley, and Chandra Cheij of NVIDIA for their support in providing hardware and technical consultation.
Publisher Copyright:
Copyright © 2015 ACM.
PY - 2015/11/15
Y1 - 2015/11/15
N2 - 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 problembased 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 SIMD architectures.
AB - 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 problembased 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 SIMD architectures.
KW - GPU
KW - Lattice-Boltzmann
KW - Problem-based learning
UR - http://www.scopus.com/inward/record.url?scp=85009071362&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85009071362&partnerID=8YFLogxK
U2 - 10.1145/2831425.2833197
DO - 10.1145/2831425.2833197
M3 - Conference contribution
AN - SCOPUS:85009071362
T3 - Proceedings of EduHPC 2015: Workshop on Education for High-Performance Computing - Held in conjunction with SC 2015: The International Conference for High Performance Computing, Networking, Storage and Analysis
BT - Proceedings of EduHPC 2015
PB - Association for Computing Machinery, Inc
T2 - Workshop on Education for High-Performance Computing, EduHPC 2015
Y2 - 15 November 2015
ER -