کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11002811 | 1449552 | 2018 | 27 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Orchestrating parallel detection of strongly connected components on GPUs
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Orchestrating parallel detection of strongly connected components on GPUs Orchestrating parallel detection of strongly connected components on GPUs](/preview/png/11002811.png)
چکیده انگلیسی
Detecting strongly connected components (SCC) is a practical graph analytics algorithm widely used in many application domains. To accelerate SCC detection, parallel algorithms have been proposed and implemented on GPUs. However, existing GPU implementations show unstable performance for various graphs, especially for real-world graphs, as these implementations do not have a clear understanding of the graph properties. In this paper, we analyze that graphs in SCC detection usually exhibit (1) skewed component sizes (the static property) and (2) dynamically changed graph structure (the dynamic property). To deal with these irregular graph properties, we propose a hybrid method that divides the algorithm into two phases and exploits different levels of parallelism for different-sized components. We also customize the graph traversal strategies for each phase to handle the dynamically changed graph structure. Our method is carefully implemented to take advantage of the GPU hardware. Evaluation with diverse synthetic and real-world graphs shows that our method substantially improves existing GPU implementations, both performance-wise and applicability-wise. It also achieves an average speedup of 5.6â¯Ã⯠and 1.5â¯Ã⯠over the sequential and OpenMP implementations on the CPU respectively.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Parallel Computing - Volume 78, October 2018, Pages 101-114
Journal: Parallel Computing - Volume 78, October 2018, Pages 101-114
نویسندگان
Xuhao Chen, Cheng Chen, Jie Shen, Jianbin Fang, Tao Tang, Canqun Yang, Zhiying Wang,