Application of parallel programming methods for simulating the impact of polymer dispersed systems on oil reservoirs on a hybrid computer system that uses the central processor cores along with the graphics processing...
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Application of parallel programming methods for simulating the impact of polymer dispersed systems on oil reservoirs on a hybrid computer system that uses the central processor cores along with the graphics processing unit is discussed. The efficiency of the proposed approach for solving practical problems of simulating waterflooding of oil reservoirs using polymer dispersed systems on computers with hybrid architecture is demonstrated.
Experimental results show that parallel programs can be evolved more easily than sequential programs in genetic parallel programming (GPP). GPP is a novel genetic programming paradigm which evolves parallel program so...
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Experimental results show that parallel programs can be evolved more easily than sequential programs in genetic parallel programming (GPP). GPP is a novel genetic programming paradigm which evolves parallel program solutions. With the rapid development of lookup-table-based (LUT-based) field programmable gate arrays (FPGAs), traditional circuit design and optimization techniques cannot fully exploit the LUTs in LUT-based FPGAs. Based on the GPP paradigm, we have developed a combinational logic circuit learning system, called GPP logic circuit synthesizer (GPPLCS), in which a multilogic-unit processor is used to evaluate LUT circuits. To show the effectiveness of the GPPLCS, we have performed a series of experiments to evolve combinational logic circuits with two- and four-input LUTs. In this paper, we present eleven multi-output Boolean problems and their evolved circuits. The results show that the GPPLCS can evolve more compact four-input LUT circuits than the well-known LUT-based FPGA synthesis algorithms.
The impact of the parallel programming model on scientific computing is examined. A comparison is made between SISAL, a functional language with implicit parallelism, and SR, an imperative language with explicit paral...
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The impact of the parallel programming model on scientific computing is examined. A comparison is made between SISAL, a functional language with implicit parallelism, and SR, an imperative language with explicit parallelism. Both languages are modern, high-level, concurrent programming languages. Five different scientific applications were programmed in each language, and evaluated for programmability and performance. The performance of these two concurrent languages on a shared-memory multiprocessor is compared to each other and to programs written in C with parallelism provided by library calls. (C) 1996 Academic Press, Inc.
The transactional memory in multicore processors has been a major research area over past several years. Many transactional memory systems have been proposed to be used to solve the synchronization problem of multicor...
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The transactional memory in multicore processors has been a major research area over past several years. Many transactional memory systems have been proposed to be used to solve the synchronization problem of multicore processors. Hardware transactional memory is one of the critical methods to speedup communications in multicore environment. In this paper, we give a review of the current hardware transactional memory systems for multicore processors. We take a top-down approach to characterizing and classifying various hardware transactional design issues and present a taxonomy of hardware transactional memory systems which is consist of the five fundamental design issues: version management, conflict detection, contention management, virtualization and nesting. Finally, we discussed the active research challenge: the relationship between transactional memory and Input/Output operations and system calls. Crown Copyright (C) 2010 Published by Elsevier BM. All rights reserved.
Contribution This study reveals that the programming paradigm is relevant to obtain advanced programming skills. Background parallel computing has become mandatory for computer science students. The increasing amount ...
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Contribution This study reveals that the programming paradigm is relevant to obtain advanced programming skills. Background parallel computing has become mandatory for computer science students. The increasing amount of computational resources required by emerging applications need experienced programmers that fully exploit hardware resources. However, the hardware platforms and programming languages to leverage them evolve at a dizzying pace, making very challenging for students the successful learning of the continuously changing high-performance computing concepts. Research Questions (a) Is the learning curve of the programming language too steep to begin learning parallel programming fundamentals? (b) Are emergent learning methodologies making even more difficult to learn parallel programming in general? Methodology It is analyzed the main challenges for succeeding in parallel programming courses at the undergraduate level in two different learning modalities, namely on-campus and online. It is analyzed the main tools available within a learning management system, showing their impact on online studies. Findings Our results reveal that the steep learning curve for parallel programming is one of the main barriers to student success, leading to an early drop out of the subject. On-campus studies mitigate this problem through a close relationship between students and educators. Online studies, however, do not have this tight relationship by its definition.
parallel programming offers the ability to simultaneously improve the performance and reduce the energy consumption of software running on heterogeneous computing systems. Software developers have long preferred to av...
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parallel programming offers the ability to simultaneously improve the performance and reduce the energy consumption of software running on heterogeneous computing systems. Software developers have long preferred to avoid parallel programming, if possible, for reasons such as perceived difficulty, lack of portability between systems, and the pace of improvement in computer hardware. However, generational changes in computer hardware are now focused on specialized components and increased computational cores, and the continued evolution of these systems places increased emphasis on achieving improvements via the use of these components. This thesis investigates parallel programming techniques that make use of components common to modern heterogeneous systems, and proposes that the difficulty and lack of portability need not be barriers to large improvements. Using a variety of heterogeneous systems, algorithms were implemented and then transformed using multiple cores, SIMD execution units, and GPUs. Reductions in execution time ranging from 71-94% and energy consumption of 76-98% were observed, demonstrating the effectiveness of using specialized components for improved performance and reduced energy consumption.
Computationally intensive techniques that offer potential clinical use have arisen in nuclear medicine. Examples include iterative reconstruction, 3D PET data acquisition and reconstruction, and 3D image volume manipu...
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Computationally intensive techniques that offer potential clinical use have arisen in nuclear medicine. Examples include iterative reconstruction, 3D PET data acquisition and reconstruction, and 3D image volume manipulation including image registration. One obstacle in achieving clinical acceptance of these techniques is the computational time required. This study focuses on methods to reduce the computation time for 3D PET reconstruction through the use of fast computer hardware, vector and parallel programming techniques, and algorithm optimization. The strengths and weaknesses of i860 microprocessor based workstation accelerator boards are investigated in implementations of 3D PET reconstruction.
TODAY, THERE IS A HEALTHY DIVERSITY OF PROJECTS AIMING TO HARNESS JAVA FORSCIENTIFI(OR "GRANDE"COMPUTING. IN FACT, THIS DEPARTMENT LOOKED AT SEVERAL OF THESE APPROACHESIN A RECENT ARTICLE. HERE, WE WILL FOCU...
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TODAY, THERE IS A HEALTHY DIVERSITY OF PROJECTS AIMING TO HARNESS JAVA FORSCIENTIFI(OR "GRANDE"COMPUTING. IN FACT, THIS DEPARTMENT LOOKED AT SEVERAL OF THESE APPROACHESIN A RECENT ARTICLE. HERE, WE WILL FOCUS ON A particular project that's ongoing at our PervasiveTechnology Lab at Indiana University. The HPJava (high-performance Javaproject aims to supportscientifiand parallel computing in a modern, object-oriented, Internet-friendly environment―theJava platform. HPJava leverages popular high-performance Fortran (HPFlanguage and library featuressuch as "scientific" multidimensional array syntax and distributed arrays, while at a morelanguage-independent level, it introduces a slightly unusual parallel programming model, somewherein between the classical HPF and message-passing interface (MPIextremes. We'll say more about thislater. See the "HPJava History" sidebar for some of the language's developmental roots.
Historically, the principal achievement of compiler technology has been to make it possible to program in a high-level, machine-independent style. The absence of compiler technology to provide such a style for paralle...
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Historically, the principal achievement of compiler technology has been to make it possible to program in a high-level, machine-independent style. The absence of compiler technology to provide such a style for parallel computers is the main reason these systems have not found widespread acceptance. This paper examines the prospects for machine-independent parallel programming, concentrating on Fortran D and High Performance Fortran, which support machine-independent expression of ''data parallelism.''
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