Teaching High Performance Computing through Parallel Programming Marathons

2019 ◽  
Author(s):  
Leandro Marzulo ◽  
Calebe Bianchini ◽  
Leandro Santiago ◽  
Victor Ferreira ◽  
Brunno Goldstein ◽  
...  
Keyword(s):  
High Performance Computing ◽  
Parallel Programming ◽  
High Performance ◽  
Performance Computing

scholarly journals A taxonomy of task-based parallel programming technologies for high-performance computing

2018 ◽  
Vol 74 (4) ◽  
pp. 1422-1434 ◽  
Author(s):  
Peter Thoman ◽  
Kiril Dichev ◽  
Thomas Heller ◽  
Roman Iakymchuk ◽  
Xavier Aguilar ◽  
...  
Keyword(s):  
High Performance Computing ◽  
Parallel Programming ◽  
High Performance ◽  
Performance Computing

Research and Application of Parallel Computing under Linux

2013 ◽  
Vol 756-759 ◽  
pp. 2825-2828
Author(s):  
Xue Chun Wang ◽  
Quan Lu Zheng
Keyword(s):  
Parallel Computing ◽  
Parallel Processing ◽  
High Performance Computing ◽  
Parallel Programming ◽  
High Performance ◽  
Performance Testing ◽  
Divide And Conquer ◽  
Parallel Computer ◽  
Super Computing ◽  
Performance Computing

Parallel computing is in parallel computer system for parallel processing of data and information, often also known as the high performance computing or super computing. The content of parallel computing were introduced, the realization of parallel computing and MPI parallel programming under Linux environment were described. The parallel algorithm based on divide and conquer method to solve rectangle placemen problem was designed and implemented with two processors. Finally, Through the performance testing and comparison, we verified the efficiency of parallel computing.

Parallel computing

2018 ◽  
Author(s):  
Joseph F. Boudreau ◽  
Eric S. Swanson
Keyword(s):  
Monte Carlo ◽  
Parallel Computing ◽  
High Performance Computing ◽  
Parallel Programming ◽  
High Performance ◽  
Parallel Application ◽  
Client Server ◽  
Parallel Code ◽  
Performance Computing

This chapter describes various approaches to concurrency, or “parallel programming”. An overview of high performance computing is followed with a review of Flynn’s taxonomy of parallel computing. Three methods for implementing parallel code using the frameworks provided by MPI, openMP, and C++ threads are presented. The use of the C++ constructs mutex and future to resolve issues of synchronization are discussed. All methods are illustrated with an embarrassingly parallel application to a Monte Carlo integral and common pitfalls are presented. The chapter closes with a discussion and example of the utility of forking processes and the use of C++ sockets and their application in a client/server environment.

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