کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4978150 | 1452258 | 2017 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Parallel non-divergent flow accumulation for trillion cell digital elevation models on desktops or clusters
ترجمه فارسی عنوان
تجمع موازی غوطه ور نشده برای مدل های تریلیون سلولی دیجیتالی روی دسکتاپ یا خوشه
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزار
چکیده انگلیسی
Continent-scale datasets challenge hydrological algorithms for processing digital elevation models. Flow accumulation is an important input for many such algorithms; here, I parallelize its calculation. The new algorithm works on one or many cores, or multiple machines, and can take advantage of large memories or cope with small ones. Unlike previous parallel algorithms, the new algorithm guarantees a fixed number of memory access and communication events per raster cell. In testing, the new algorithm ran faster and used fewer resources than previous algorithms, exhibiting â¼30% strong and weak scaling efficiencies up to 48 cores and linear scaling across datasets ranging over three orders of magnitude. The largest dataset tested had two trillion (2·1012) cells. With 48 cores, processing required 24 min wall-time (14.5 compute-hours). This test is three orders of magnitude larger than any previously performed in the literature. Complete, well-commented source code and correctness tests are available on Github.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Environmental Modelling & Software - Volume 92, June 2017, Pages 202-212
Journal: Environmental Modelling & Software - Volume 92, June 2017, Pages 202-212
نویسندگان
Richard Barnes,