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About 2,975 papers for “parallel programming environment”
Grant Report, Dionisios Pothos, Barry Richards et al. · 2008
This paper describes research supported over the past two years by a grant from the Department of Trade and Industry to IC-Parc under the auspices of the London Parallel Applications Centre. The aim of the project was to assess the benefits of parallelism for the generic constraint-based planning architecture parcPLAN
Cited by 0
Segall, Zary, Rudolph, Larry · KiltHub Repository · 2018
Computer Science Department
Cited by 0Open access
SICARD, Nicolas, MERIGOT, Alain · OpenGrey (Institut de l'Information Scientifique et Technique) · 2004
Les Réseaux Associatifs constituent un modèle de calcul parallèle original permettant d'exprimer aisément des calculs sur des données distribuées sur un réseau. Ce réseau, représenté par un graphe, généralement sous forme de maille, sert à véhiculer les valeurs entre les sommets. Par des mécanismes d'activation ou de désactivation des arcs de ces graphes, il est alors possible de former des structures irrégulières sur lesquelles peuvent être appliquées des primitives de calcul globales appelées associations. Le traitement et l'analyse d'image sont les principales applications de ce modèle. Notre étude porte sur la mise en œuvre d'un modèle de programmation des Réseaux Associatifs sur machines parallèles à usage général, afin de proposer un environnement de programmation adapté à ce support d'exécution. Nous étudions dans un premier temps les spécifications du modèle de programmation et nous proposons une première approche d'extension pour la représentation simplifiée de régions d'une image par les Réseaux Virtuels. Nous nous intéressons ensuite aux conditions d'une parallélisation efficace des primitives de calcul sur réseaux de stations ou sur machines à mémoire partagée. Nous montrons enfin, au travers de deux applications classiques d'analyse d'image, en quoi ce modèle de programmation constitue une solution élégante et compacte pour l'expression des algorithmes d'analyse d'image et la parallélisation automatique efficace de programmes complets. Ce travail a conduit au développement d'ANET, ensemble de bibliothèques de programmation pour l'analyse d'image sur machines parallèles à mémoire partagée.
Cited by 0Open access
Jos'e Nagib, Jos'e Nagib Cotrim, Adam Beguelin et al. · 1995
Writing parallel programs for distributed multi-user computing environments is a difficult task. The Distributed object migration environment (Dome) addresses three major issues of parallel computing in an architecture independent manner: ease of programming, dynamic load balancing, and fault tolerance. Dome programmers, with modest effort, can write parallel programs that are automatically distributed over a heterogeneous network, dynamically load balanced as the program runs, and able to survive compute node and network failures. This paper provides the motivation for and an overview of Dome, including a preliminary performance evaluation of dynamic load balancing for distributed vectors. Dome programs are shorter and easier to write than the equivalent programs written with message passing primitives. The performance overhead of Dome is characterized, and it is shown that this overhead can be recouped by dynamic load balancing in imbalanced systems. Finally, we show that a parallel ...
Cited by 0
Stephen John Turner, Wentong Cai, Hung‐Khoon Tan · 2002
With the increased complexity of applications, parallel computing has proved to be an alternative to supercomputing in solving large problems. However developing parallel applications is more difficult compared to sequential programming. Visual technologies can be employed to aid the multi-dimensional tasks of parallel programming. This paper presents a case study of the use of an integrated visual programming environment in creating parallel applications. Techniques for the hierarchical construction of parallel programs are presented, together with an evaluation of the performance of the generated code for a matrix multiplication application.
Cited by 1
Daisaku Arita, Y. Hamada, Satoshi Yonemoto et al. · Lecture notes in computer science · 2000
Cited by 7
Andria Arisal, Heru Suhartanto · 2024
This paper presents HPC-in-Containers, a novel containerized parallel computing environment using Docker. It is designed to facilitate learning parallel programming concepts, where users do not have to deploy a multicomputer infrastructure. Instead, they can use their desktop and laptop to easily have a multicomputer environment by utilizing containerization on the multi-core CPU. The environment offers a comprehensive platform for exploring various parallel programming paradigms, including distributed memory parallelism, shared memory parallelism, and hybrid approaches. We include source code samples for each paradigm to demonstrate its capabilities: parallel multicomputer implementations using MPI, parallel thread implementations for single computers using OpenMP, and hybrid parallelization techniques combining both MPI and OpenMP. Furthermore, to enhance user interaction and ease of use, the Docker image incorporates a Jupyter Notebook interface with a C interpreter, allowing users to write, execute, and visualize parallel code through a web browser. This feature makes HPC-in-Containers particularly suitable for educational settings, enabling students to experiment with parallel programming concepts in a controlled, reproducible environment. Our work contributes to the field of parallel computing education by providing a flexible, scalable, and easily deployable platform that bridges the gap between theoretical concepts and practical implementation. It offers a valuable resource for educators, students, and researchers seeking to explore and master the intricacies of parallel programming in a containerized setting.
Cited by 0
Jiang, Z. · Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) · 2000
S.2749-2756
Cited by 0
Licínio Oliveira, Luís Lopes, Fernando Silva et al. · 2007
P is a next-generation Internet computing platform, building upon other experiments and implementing new ideas for high-performance parallel computing in the Internet environment. This paper describes its run-time system, programming model and how it compares to current state-of-the-art systems.
Cited by 0
JrJung Lyu, Hsing Luh, Ming‐Chang Lee · American Journal of Applied Sciences · 2004
This study developed a parallel algorithm to efficiently solve linear programming models. The proposed algorithm utilizes the Dantzig-Wolfe Decomposition Principle and can be easily implemented in a general distributed computing environment. The analytical performance of the well-known method, including the speedup upper bound and lower bound limits, was derived. Numerical experiments are also provided in order to verify the complexity of the proposed algorithm. The empirical results demonstrate that the speedup of this parallel algorithm approaches linearity, which means that it can take full advantage of the distributed computing power as the size of the problem increases.
Cited by 6Open access
T. Born, Wolfgang Obelöer, Lorenz Schäfers et al. · 2002
TRAPPER is a graphical environment aimed to support the programming of parallel embedded systems. TRAPPER comprises components for the design, mapping, visualization and optimization of parallel applications. One goal of this article is to motivate the requirements for a monitoring system in such a graphical programming environment. Because the existing monitoring system DELTA-T already fulfils a lot of these requirements we decided to integrate it. The result of this integration is shown in detail. The main features are automatic observation of software and hardware events at different granularity, different transport strategies of the monitoring data, and a flexible programmer interface to interact with the monitor. All features may be switched on and off by the graphical user interface of TRAPPER. Measurements show the low overhead of our implementation. The monitoring system is currently being used as part of TRAPPER in several industrial automotive research applications.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Cited by 9
David Callahan, Ken Kennedy · 2014
No abstract available.
Cited by 125
Mahmoud A. Saber, Nikolay Mirenkov · 2005
Cited by 0
D. L. Dai, S. K. S. Gupta, S. D. Kaushik et al. · 2007
EXTENT stands for an EXpert system for TENsor product formula Translation. In this paper, we describe the development of programs for fast Fourier transforms (FFT) for vector multiprocessors and distributed-memory machines using the EXTENT system for formula translation. Several forms of the FFT have been presented in the literature. Each form has different characteristics and is best suited for good performance on a particular architecture. The tensor product has been shown to be a uniform representation for all of these forms. We augment the tensor product representation of the FFT to include a mixed radix and two-level representation. The code generation methodology, including the generation of the complicated indexing code, characteristic of FFT programs, is described. Extensions of the code generation for the FFT forms on vector multiprocessor and distributed-memory machines are described. Performance results for the automatically generated FFT codes on the Cray Y-MP and Intel iPS...
Cited by 0
Joachim Maier · 1993
Parallel programming is hard enough, nevertheless parallel computers, which often are not far away from prototype status, have low reliability. This makes it difficult or sometimes impossible to successfully execute long-running applications, as they are typical for the `Grand-Challenge &apos; problems. Pact is a parallel programming environment with usertransparent fault-tolerance based on atomic actions. Virtual shared memory with user-defined dataunits and an easy-to-use synchronization model make parallel programming easier without losing too much efficiency. In this article the design for a massively parallel implementation on the intel Paragon^TM is presented which due to the availability of threads and a very efficient asynchronous message-passing by using message-passing processors promises even less runtime overhead than the current prototype which is running on a Tandem^TM multiprocessor system. 1 Introduction Distributed memory machines like the intel Paragon ^TM usually su...
Cited by 0
Chen, Tzung-shi; Shih, Kuei-ping; Sheu, Jang-ping, 石貴平 · 1996
A state-of-the-art parallel programming environment called UPPER (User-interactive Parallel Programming EnviRonment) is presented in this paper. Parallel machines which execute programs concurrently on hundreds or thousands of processors provide far more computational power than does a uniprocessor. However, designing parallel programs on parallel machines manually is very difficult and error-prone. Due to these problems, many tools which help programmers translate sequential programs into parallelized programs or even help them design parallel programs have been developed. The proposed environment also has the same purpose. The major components of this environment include a parallelizing compiler system and simulators of the given target machines. The parallelizing compiler system introduces new and existing techniques for compiler-time analysis, and the simulator can simulate execution of the translated parallelized program on the target machine and show the simulated performance rep...
Cited by 0
Terboven, Christian, Hahnfeld, Jonas · TIB KMO / FLOWWORKS GmbH · 2021
In high-performance computing (HPC), interactive access to HPC systems and the ability to perform experiments in rapid-prototyping environments are getting more important. Jupyter notebooks have become a popular tool to create and share documents that contain narrative text, visualizations and live code, and the xeus-cling kernel provides support for native C/C++ code via the Cling interpreter. In this talk, we will present how we extended these technologies to support parallel programming with MPI and OpenMP, the de-facto standards in HPC. In the context of parallel programming, the resulting environment supports rapid prototyping and e-learning equally well. We will present an overview about its capabilities, demonstrate selected use cases and resources that we developed. In addition, we incorporated support for runtime correctness checking of MPI and OpenMP parallel programs. We will explain how we added support to write out binary programs from Jupyter notebooks and how to run code under the control of existing open source correctness tools. In the technical part of this talk, we will focus on two contributions to xeus-cling that are most important for our use cases: support for printf and the ability to launch MPI programs. The implementation of the printf family relies on wrapper functions that are injected into LLVM's just-in-time (JIT) compilation. To launch MPI programs, we extended xeus-cling to write out binary programs based on the incremental abstract syntax tree (AST) that Cling builds internally. We will also discuss why we chose this path over teaching the interpreter to start multiple processes.
Cited by 0Open access
Péter Kacsuk, Gábor Dózsa, Tibor Fadgyas et al. · 1999
Abstract. In this paper, we describe a graphical editor GRED as part of the integrated programming environment GRADE that is intended to support designing, debugging and performance tuning of messagepassing programs running on a heterogeneous network of computers. The GRED editor hides the cumbersome details of the underlying lowlevel message-passing system (which is currently the PVM system) by providing visual abstractions but allows the programmer to define local computations of the individual processes in C (or in Fortran in the future) independently from the visually supported process management and inter-process communication activities. Visual representatation of the critical (i.e. message-passing related) parts of the code can help the user in grasping the complex structure and runtime behavior of the whole parallel application, even if he or she is not an expert in the field of concurrent programming. 1
Cited by 0
STERNIERI A, ANELLI P, EMILIANI U. et al. · CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) · 1999
Cited by 0
Patrick Amestoy, Iain Duff, Jean-Yves L’Excellent et al. · OpenGrey (Institut de l'Information Scientifique et Technique) · 1998
PARASOL is an ESPRIT IV Long Term Research Project whose main goal is to build and test a portable library for solving large sparse systems of equations on distributed memory systems. There are twelve partners in five countries, ve of whom are code developers and five end users. The software is written in Fortran 90 and uses MPI for message passing. There are routines for both direct and iterative solution of symmetric and unsymmetric systems. The final library will be in the public domain. We will discuss the PARASOL Project with particular emphasis on the algorithms and software for direct solution that are being developed by RAL and CERFACS in collaboration with ENSEEIHT-IRIT in Toulouse. The underlying algorithm is a multifrontal one with a switch to ScaLAPACK processing towards the end of the factorization (and solution). We will discuss the algorithms, the interface, and their current status and illustrate the performance of the direct solver on a range of problems from the PARASOL end users.
Cited by 1