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A Review on New Paradigm's of Parallel Programming A Review on New Paradigm's of Parallel Programming A Review on New Paradigm's of Parallel Programming A Review on New Paradigm's of Parallel Programming Models in High Performance Computing Models in High Performance Computing Models in High Performance Computing Models in High Performance Computing

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Abstract

High Performance Computing (HPC) is use of multiplecomputer resources to solve large critical problems.Multiprocessor and Multicore is two broad parallelcomputers which support parallelism. ClusteredSymmetric Multiprocessors (SMP) is the most fruitful wayout for large scale applications. Enhancing theperformance of computer application is the main role ofparallel processing. Single processor performance onhigh-end systems often enjoys a noteworthy outlayadvantage when implemented in parallel on systemsutilizing multiple, lower-cost, and commoditymicroprocessors. Parallel computers are going mainstream because clusters of SMP (SymmetricMultiprocessors) nodes provide support for an amplecollection of parallel programming paradigms. MPI andOpenMP are the trendy flavors in a parallel programming.In this paper we have taken a review on parallelparadigm’s available in multiprocessor and multicoresystem.

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... Reducing application runtime is a major challenge in software engineering. Parallelism plays an important role in achieving this goal, for which there are two general methods [1,2]. The first method is parallel programing, which requires a lot of programmer skills, and the second method is automatic parallelism, which frees the programmer from getting involved in how programs run in parallel, and is majorly focussed on solving the problem. ...
... Therefore, only calculating φ and θ is sufficient for the other two vectors. For v 1 and v 3 , if φ 1 ¼ φ 2 , then the desired volume is the volume of a part of the cone φ ¼ φ 1 , which can be calculated as in Equation (3). But if φ 1 ≠ φ 2 (assuming φ 1 < φ 2 ), by moving from the first vector to the second, the value increases. ...
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... To improve the performance of applications, multi-processor and multi-core systems can be used which decrease the overhead costs from serial programming. There are generally two methods for parallelization [1]: automatic parallelization and parallel programming. In automatic parallelization, the parallelizing compilers turn the serial program into parallel automatically and in parallel programming, the whole program is divided into smaller works from the main work and these are assigned to different B Shabnam Mahjoub shabnam.mahjoub@yahoo.com 1 Department of Computer Engineering, Langaroud Branch, Islamic Azad University, Langaroud, Iran processors. ...
... In automatic parallelization, the parallelizing compilers turn the serial program into parallel automatically and in parallel programming, the whole program is divided into smaller works from the main work and these are assigned to different B Shabnam Mahjoub shabnam.mahjoub@yahoo.com 1 Department of Computer Engineering, Langaroud Branch, Islamic Azad University, Langaroud, Iran processors. Considering difficulty of the second method, the major contribution of this paper will focus on the first method, namely automatic paralleling [2][3][4][5][6]. ...
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One of the factors increasing the execution time of computational programs is the loops, and parallelization of the loops is used to decrease this time. One of the steps of parallelizing compilers is uniformization of non-uniform loops in wavefront method which is considered as a NP-hard problem. In this paper, a new method has been presented to make uniform the non-uniform two-level perfect nested loops using the frog-leaping algorithm, called UTFLA, which is a combination of deterministic and stochastic methods, because the challenge most of loop paralleling methods, old or dynamic or new ones, face is the high algorithm execution time. UTFLA has been designed in a way to find the best results with the lowest amount of basic dependency cone size in the minimum possible time and gives more appropriate results in a more reasonable time compared to other methods.
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This work organizes all of parallel programming into a set of design patterns. It was up to date as of 2004 when programming was largely restricted to the CPU. It does not, however, address data parallel hardware (vector units and GPUs). Hence, for CPU programming this is still a great book. For GPUs, however, you'll need to wait for the next addition.
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Exploring how concurrent programming can be assisted by language-level techniques, Introduction to Concurrency in Programming Languages presents high-level language techniques for dealing with concurrency in a general context. It provides an understanding of programming languages that offer concurrency features as part of the language definition. The book supplies a conceptual framework for different aspects of parallel algorithm design and implementation. It first addresses the limitations of traditional programming techniques and models when dealing with concurrency. The book then explores the current state of the art in concurrent programming and describes high-level language constructs for concurrency. It also discusses the historical evolution of hardware, corresponding high-level techniques that were developed, and the connection to modern systems, such as multicore and manycore processors. The remainder of the text focuses on common high-level programming techniques and their application to a range of algorithms. The authors offer case studies on genetic algorithms, fractal generation, cellular automata, game logic for solving Sudoku puzzles, pipelined algorithms, and more. Illustrating the effect of concurrency on programs written in familiar languages, this text focuses on novel language abstractions that truly bring concurrency into the language and aid analysis and compilation tools in generating efficient, correct programs. It also explains the complexity involved in taking advantage of concurrency with regard to program correctness and performance.
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Leveraging the full power of multicore processors demands newtools and new thinking from the software industry.Concurrency has long been touted as the "next big thing" and "theway of the future," but for the past 30 years, mainstream softwaredevelopment has been able to ignore it. Our parallel future hasfinally arrived: new machines will be parallel machines, and thiswill require major changes in the way we develop software. Theintroductory article in this issue ("The Future of Microprocessors"by Kunle Olukotun and Lance Hammond) describes the hardwareimperatives behind this shift in computer architecture fromuniprocessors to multicore processors, also known as CMPs (chipmultiprocessors). (For related analysis, see "The Free Lunch IsOver: A Fundamental Turn Toward Concurrency in Software.")
The Sourcebook of Parallel Computing
  • J Dongarra
  • I Foster
  • G Fox
  • W Gropp
  • K Kennedy
  • L Torczon
  • A White
J. Dongarra, I. Foster, G. Fox, W. Gropp, K. Kennedy, L. Torczon and A. White. The Sourcebook of Parallel Computing, Morgan Kaufmann Publishers, San Francisco, 2003.
API Specification for Parallel Programming
  • Openmp
OpenMP. "API Specification for Parallel Programming",http://openmp.org/wp/openmpspecifications. Oct. 2011.