High Performance Computing for Computational Science – VECPAR 2016 High Performance Computing for Computational Science – VECPAR 2016

High Performance Computing for Computational Science – VECPAR 2016

12th International Conference, Porto, Portugal, June 28-30, 2016, Revised Selected Papers

Inês Dutra والمزيد
    • ‏39٫99 US$
    • ‏39٫99 US$

وصف الناشر

This book constitutes the thoroughly refereed post-conference proceedings of the 12fth International Conference on High Performance Computing in Computational Science,
VECPAR 2016, held in Porto, Portugal, in June 2016.
The 20 full papers presented were carefully reviewed and selected from 36 submissions. The papers are organized in topical sections on applications; performance modeling and analysis; low level support; environments/libraries to support parallelization.

النوع
كمبيوتر وإنترنت
تاريخ النشر
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١٣ يوليو
اللغة
EN
الإنجليزية
عدد الصفحات
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الناشر
Springer International Publishing
البائع
Springer Nature B.V.
الحجم
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‫م.ب.‬
Parallel Processing and Applied Mathematics Parallel Processing and Applied Mathematics
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Parallel Processing and Applied Mathematics Parallel Processing and Applied Mathematics
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Euro-Par 2020: Parallel Processing Euro-Par 2020: Parallel Processing
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High Performance Computing for Computational Science – VECPAR 2018 High Performance Computing for Computational Science – VECPAR 2018
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Euro-Par 2016: Parallel Processing Euro-Par 2016: Parallel Processing
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High Performance Computing for Computational Science -- VECPAR 2010 High Performance Computing for Computational Science -- VECPAR 2010
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Machine Learning and Knowledge Discovery in Databases. Research Track and Applied Data Science Track Machine Learning and Knowledge Discovery in Databases. Research Track and Applied Data Science Track
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Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track
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Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track
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