Abstract
Recently GPU is widely utilized in scientific computing and engineering applications, owing primarily to the evolution of GPU architecture. Firstly, we analyze some key performance characters of GPU in detail, and the relationships among GPU architecture, programming model and memory hierarchy. Secondly, we present three performance optimization strategies: Prefetching, Streamlizing, and Task Division. Adequate experiments have been done to abstract the relationships among different factors and efficiency. Finally, we map the HPL benchmark to testify our strategies and achieve certain speedup.
This work is supported by National High Technology Development 863 Program of China under Grant No.2009AA01Z102 and National Natural Science Foundation of China under Grant No.60873016.
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Ma, A., Cai, J., Cheng, Y., Ni, X., Tang, Y., Xing, Z. (2009). Performance Optimization Strategies of High Performance Computing on GPU. In: Dou, Y., Gruber, R., Joller, J.M. (eds) Advanced Parallel Processing Technologies. APPT 2009. Lecture Notes in Computer Science, vol 5737. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03644-6_12
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DOI: https://doi.org/10.1007/978-3-642-03644-6_12
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