کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4964564 1447812 2017 30 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Hybrid-optimization strategy for the communication of large-scale Kinetic Monte Carlo simulation
ترجمه فارسی عنوان
استراتژی بهینه سازی ترکیبی برای ارتباط شبیه سازی مین کارلو سینتیک در مقیاس بزرگ
کلمات کلیدی
جنبشی مونت کارلو، تجمیع ارتباطات، حافظه مشترک، مجموعه های همجوار،
موضوعات مرتبط
مهندسی و علوم پایه شیمی شیمی تئوریک و عملی
چکیده انگلیسی
The parallel Kinetic Monte Carlo (KMC) algorithm based on domain decomposition has been widely used in large-scale physical simulations. However, the communication overhead of the parallel KMC algorithm is critical, and severely degrades the overall performance and scalability. In this paper, we present a hybrid optimization strategy to reduce the communication overhead for the parallel KMC simulations. We first propose a communication aggregation algorithm to reduce the total number of messages and eliminate the communication redundancy. Then, we utilize the shared memory to reduce the memory copy overhead of the intra-node communication. Finally, we optimize the communication scheduling using the neighborhood collective operations. We demonstrate the scalability and high performance of our hybrid optimization strategy by both theoretical and experimental analysis. Results show that the optimized KMC algorithm exhibits better performance and scalability than the well-known open-source library-SPPARKS. On 32-node Xeon E5-2680 cluster (total 640 cores), the optimized algorithm reduces the communication time by 24.8% compared with SPPARKS.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Physics Communications - Volume 211, February 2017, Pages 113-123
نویسندگان
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