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Programming Paradigms in High Performance Computing

Programming Paradigms in High Performance Computing
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Author(s): Venkat N. Gudivada (Marshall University, USA), Jagadeesh Nandigam (Grand Valley State University, USA) and Jordan Paris (Marshall University, USA)
Copyright: 2015
Pages: 28
Source title: Research and Applications in Global Supercomputing
Source Author(s)/Editor(s): Richard S. Segall (Arkansas State University, USA), Jeffrey S. Cook (Independent Researcher, USA) and Qingyu Zhang (Shenzhen University, China)
DOI: 10.4018/978-1-4666-7461-5.ch013

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Abstract

Availability of multiprocessor and multi-core chips and GPU accelerators at commodity prices is making personal supercomputers a reality. High performance programming models help apply this computational power to analyze and visualize massive datasets. Problems which required multi-million dollar supercomputers until recently can now be solved using personal supercomputers. However, specialized programming techniques are needed to harness the power of supercomputers. This chapter provides an overview of approaches to programming High Performance Computers (HPC). The programming paradigms illustrated include OpenMP, OpenACC, CUDA, OpenCL, shared-memory based concurrent programming model of Haskell, MPI, MapReduce, and message-based distributed computing model of Erlang. The goal is to provide enough detail on various paradigms to help the reader understand the fundamental differences and similarities among the paradigms. Example programs are chosen to illustrate the salient concepts that define these paradigms. The chapter concludes by providing research directions and future trends in programming high performance computers.

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