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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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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Due to the design of computer systems in the multi‐core and/or multi‐processor form, it is possible to use the maximum capacity of processors to run an application with the least time consumed through parallelisation. This is the responsibility of parallel compilers, which perform parallelisation in several steps by distributing iterations between different processors and executing them simultaneously to achieve lower runtime. The present paper focuses on the uniformisation of three‐level perfect nested loops as an important step in parallelisation and proposes a method called Towards Three‐Level Loop Parallelisation (TLP) that uses a combination of a Frog Leaping Algorithm and Fuzzy to achieve optimal results because in recent years, many algorithms have worked on volumetric data, that is, three‐dimensional spaces. Results of the implementation of the TLP algorithm in comparison with existing methods lead to a wide variety of optimal results at desired times, with minimum cone size resulting from the vectors. Besides, the maximum number of input dependence vectors is decomposed by this algorithm. These results can accelerate the process of generating parallel codes and facilitate their development for High‐Performance Computing purposes.
... 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 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 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.
"I hope that readers will learn to use the full expressibility and power of OpenMP. This book should provide an excellent introduction to beginners, and the performance section should help those with some experience who want to push OpenMP to its limits." --from the foreword by David J. Kuck, Intel Fellow, Software and Solutions Group, and Director, Parallel and Distributed Solutions, Intel Corporation OpenMP, a portable programming interface for shared memory parallel computers, was adopted as an informal standard in 1997 by computer scientists who wanted a unified model on which to base programs for shared memory systems. OpenMP is now used by many software developers; it offers significant advantages over both hand-threading and MPI. Using OpenMP offers a comprehensive introduction to parallel programming concepts and a detailed overview of OpenMP. Using OpenMP discusses hardware developments, describes where OpenMP is applicable, and compares OpenMP to other programming interfaces for shared and distributed memory parallel architectures. It introduces the individual features of OpenMP, provides many source code examples that demonstrate the use and functionality of the language constructs, and offers tips on writing an efficient OpenMP program. It describes how to use OpenMP in full-scale applications to achieve high performance on large-scale architectures, discussing several case studies in detail, and offers in-depth troubleshooting advice. It explains how OpenMP is translated into explicitly multithreaded code, providing a valuable behind-the-scenes account of OpenMP program performance. Finally, Using OpenMP considers trends likely to influence OpenMP development, offering a glimpse of the possibilities of a future OpenMP 3.0 from the vantage point of the current OpenMP 2.5. With multicore computer use increasing, the need for a comprehensive introduction and overview of the standard interface is clear. Using OpenMP provides an essential reference not only for students at both undergraduate and graduate levels but also for professionals who intend to parallelize existing codes or develop new parallel programs for shared memory computer architectures.
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.
Conference Paper
The development of microprocessors design has been shifting to multi-core architectures. Therefore, it is expected that parallelism will play a significant role in future generations of applications. Throughout the years, there has been a myriad number of parallel programming models proposed. In choosing a parallel programming model, not only the performance aspect is important, but also qualitative the aspect of how well parallelism is abstracted to developers. A model with a well abstraction of parallelism leads to a higher application-development productivity. In this paper, we propose seven criteria to qualitatively evaluate parallel programming models. Our focus is on how parallelism is abstracted and presented to application developers. As a case study, we use these criteria to investigate six well-known parallel programming models in the HPC community.
Programming Massively Parallel Processors: A Hands-on Approach, Third Edition shows both student and professional alike the basic concepts of parallel programming and GPU architecture, exploring, in detail, various techniques for constructing parallel programs. Case studies demonstrate the development process, detailing computational thinking and ending with effective and efficient parallel programs. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in-depth. For this new edition, the authors have updated their coverage of CUDA, including coverage of newer libraries, such as CuDNN, moved content that has become less important to appendices, added two new chapters on parallel patterns, and updated case studies to reflect current industry practices. Teaches computational thinking and problem-solving techniques that facilitate high-performance parallel computing Utilizes CUDA version 7.5, NVIDIA's software development tool created specifically for massively parallel environments Contains new and updated case studies Includes coverage of newer libraries, such as CuDNN for Deep Learning © 2017, 2013, 2010 David B. Kirk/NVIDIA Corporation and Wen-mei W. Hwu. Published by Elsevier Inc. All rights reserved.
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", Oct. 2011.