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Parallelizing Alternating Direction Implicit Solver on GPUs

Zhangping Wei, Byunghyun Jang, Yaoxin Zhang, Yafei Jia
National Center for Computational Hydroscience & Engineering, The University of Mississippi, University, MS 38677 U.S.A.
Procedia Computer Science, Volume 18, Pages 389-398, 2013

@article{Wei2013389,

   title={"ParallelizingAlternatingDirectionImplicitSolveronGPUs"},

   journal={"ProcediaComputerScience"},

   volume={"18"},

   pages={"389-398"},

   year={"2013"},

   note={"2013InternationalConferenceonComputationalScience"},

   issn={"1877-0509"},

   doi={"http://dx.doi.org/10.1016/j.procs.2013.05.202"},

   url={"http://www.sciencedirect.com/science/article/pii/S1877050913003451"},

   author={"ZhangpingWeiandByunghyunJangandYaoxinZhangandYafeiJia"},

   keywords={"ParallelComputing"}

}

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We present a parallel Alternating Direction Implicit (ADI) solver on GPUs. Our implementation significantly improves ex- isting implementations in two aspects. First, we address the scalability issue of existing Parallel Cyclic Reduction (PCR) implementations by eliminating their hardware resource constraints. As a result, our parallel ADI, which is based on PCR, no longer has the maximum domain size limitation. Second, we optimize inefficient data accesses of parallel ADI solver by leveraging hardware texture memory and matrix transpose techniques. These memory optimizations further make already parallelized ADI solver twice faster, achieving overall more than 100 times speedup over a highly optimized CPU version. We also present the analysis of numerical accuracy of the proposed parallel ADI solver.
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