705 research outputs found
MPICH-G2: A Grid-Enabled Implementation of the Message Passing Interface
Application development for distributed computing "Grids" can benefit from
tools that variously hide or enable application-level management of critical
aspects of the heterogeneous environment. As part of an investigation of these
issues, we have developed MPICH-G2, a Grid-enabled implementation of the
Message Passing Interface (MPI) that allows a user to run MPI programs across
multiple computers, at the same or different sites, using the same commands
that would be used on a parallel computer. This library extends the Argonne
MPICH implementation of MPI to use services provided by the Globus Toolkit for
authentication, authorization, resource allocation, executable staging, and
I/O, as well as for process creation, monitoring, and control. Various
performance-critical operations, including startup and collective operations,
are configured to exploit network topology information. The library also
exploits MPI constructs for performance management; for example, the MPI
communicator construct is used for application-level discovery of, and
adaptation to, both network topology and network quality-of-service mechanisms.
We describe the MPICH-G2 design and implementation, present performance
results, and review application experiences, including record-setting
distributed simulations.Comment: 20 pages, 8 figure
A genetic and evolutionary programming environment with spatially structured populations and built-in parallelism
The recent development of the Genetic and Evolutionary Computation field lead to a kaleidoscope of approaches to problem solving, which are based on a common background. These shared principles are used in order to develop a programming environment that enhances modularity, in terms of software design and implementation. The system's core encapsulates the main features of the Genetic and Evolutionary Algorithms, by identifying the entities at stake and implementing them as hierarchies of software modules. This architecture is enriched with the parallelization of the algorithms, based on spatially structured populations, following coarse-grained (Island Model) and fine-grained (Neighborhood Model) strategies. A distributed physical implementation, under the PVM environment, running in a local network, is described.Fundação para a Ciência e Tecnologia - PRAXIS/P/EEI/13096/98
An experience in building a parallel and distributed problem-solving environment
We describe our experimentation with the design and implementation of specific environments, consisting of heterogeneous computational, visualization, and control components. We illustrate the approach with the design of a problem-solving environment supporting the execution of genetic algorithms. We describe a prototype steering parallel execution, visualization, and steering. A life cycle for the development of applications based an genetic algorithms is proposed.publishersversionpublishe
A Tool for Programming Embarrassingly Task Parallel Applications on CoW and NoW
Embarrassingly parallel problems can be split in parts that are characterized
by a really low (or sometime absent) exchange of information during their
computation in parallel. As a consequence they can be effectively computed in
parallel exploiting commodity hardware, hence without particularly
sophisticated interconnection networks. Basically, this means Clusters,
Networks of Workstations and Desktops as well as Computational Clouds. Despite
the simplicity of this computational model, it can be exploited to compute a
quite large range of problems. This paper describes JJPF, a tool for developing
task parallel applications based on Java and Jini that showed to be an
effective and efficient solution in environment like Clusters and Networks of
Workstations and Desktops.Comment: 7 page
A Survey of Parallel Data Mining
With the fast, continuous increase in the number and size of databases, parallel data mining is a natural and cost-effective approach to tackle the problem of scalability in data mining. Recently there has been a considerable research on parallel data mining. However, most projects focus on the parallelization of a single kind of data mining algorithm/paradigm. This paper surveys parallel data mining with a broader perspective. More precisely, we discuss the parallelization of data mining algorithms of four knowledge discovery paradigms, namely rule induction, instance-based learning, genetic algorithms and neural networks. Using the lessons
learned from this discussion, we also derive a set of heuristic principles for designing efficient parallel data mining algorithms
An overview of recent research results and future research avenues using simulation studies in project management
This paper gives an overview of three simulation studies in dynamic project scheduling integrating baseline scheduling with risk analysis and project control. This integration is known in the literature as dynamic scheduling. An integrated project control method is presented using a project control simulation approach that combines the three topics into a single decision support system. The method makes use of Monte Carlo simulations and connects schedule risk analysis (SRA) with earned value management (EVM). A corrective action mechanism is added to the simulation model to measure the efficiency of two alternative project control methods. At the end of the paper, a summary of recent and state-of-the-art results is given, and directions for future research based on a new research study are presented
SzámĂtĂłhálĂł alkalmazások teljesĂtmĂ©nyanalĂzise Ă©s optimalizáciĂłja = Performance analysis and optimisation of grid applications
SzámĂtĂłhálĂłn (griden) futĂł alkalmazások, elsĹ‘sorban workflow-k hatĂ©kony vĂ©grehajtására kerestĂĽnk Ăşjszerű megoldásokat a grid teljesĂtmĂ©nyanalĂzis Ă©s optimalizáciĂł terĂĽletĂ©n. ElkĂ©szĂtettĂĽk a Mercury monitort a grid teljesĂtmĂ©nyanalĂzis követelmĂ©nyeit figyelembe vĂ©ve. A párhuzamos programok monitorozására alkalmas GRM monitort integráltuk a reláciĂłs adatmodell alapĂş R-GMA grid informáciĂłs rendszerrel, illetve a Mercury monitorral. ElkĂ©szĂĽlt a Pulse, Ă©s a Prove vizualizáciĂłs eszköz grid teljesĂtmĂ©nyanalĂzist támogatĂł verziĂłja. ElkĂ©szĂtettĂĽnk egy state-of-the-art felmĂ©rĂ©st grid teljesĂtmĂ©nyanalĂzis eszközökrĹ‘l. Kidolgoztuk a P-GRADE rendszer workflow absztrakciĂłs rĂ©tegĂ©t, melyhez kapcsolĂłdĂłan elkĂ©szĂĽlt a P-GRADE portál. Ennek segĂtsĂ©gĂ©vel a felhasználĂłk egy web böngĂ©szĹ‘n keresztĂĽl szerkeszthetnek Ă©s hajthatnak vĂ©gre workflow alkalmazásokat számĂtĂłhálĂłn. A portál kĂĽlönbözĹ‘ számĂtĂłhálĂł implementáciĂłkat támogat. LehetĹ‘sĂ©get biztosĂt informáciĂł gyűjtĂ©sĂ©re teljesĂtmĂ©nyanalĂzis cĂ©ljábĂłl. Megvizsgáltuk a portál erĹ‘forrás brĂłkerekkel valĂł egyĂĽttműködĂ©sĂ©t, felkĂ©szĂtettĂĽk a portált a sikertelen futások javĂtására. A vĂ©grehajtás optimalizálása megkövetelheti az alkalmazás egyes rĂ©szeinek áthelyezĂ©sĂ©t más erĹ‘forrásokra. Ennek támogatására továbbfejlesztettĂĽk a P-GRADE alkalmazások naplĂłzhatĂłságát, Ă©s illesztettĂĽk a Condor feladatĂĽtemezĹ‘jĂ©hez. Sikeresen kapcsoltunk a rendszerhez egy terhelĂ©s elosztĂł modult, mely kĂ©pes a terheltsĂ©gĂ©tĹ‘l fĂĽggĹ‘en áthelyezni a folyamatokat. | We investigated novel approaches for performance analysis and optimization for efficient execution of grid applications, especially workflows. We took into consideration the special requirements of grid performance analysis when elaborated Mercury, a grid monitoring infrastructure. GRM, a performance monitor for parallel applications, has been integrated with R-GMA, a relational grid information system and Mercury as well. We developed Pulse and Prove visualisation tools for supporting grid performance analysis. We wrote a comprehensive state-of-the art survey of grid performance tools. We designed a novel abstraction layer of P-GRADE supporting workflows, and a grid portal. Users can draft and execute workflow applications in the grid via a web browser using the portal. The portal supports multiple grid implementations and provides monitoring capabilities for performance analysis. We tested the integration of the portal with grid resource brokers and also augmented it with some degree of fault-tolerance. Optimization may require the migration of parts of the application to different resources and thus, it requires support for checkpointing. We enhanced the checkpointing facilities of P-GRADE and coupled it to Condor job scheduler. We also extended the system with a load balancer module that is able to migrate processes as part of the optimization
An Object-Oriented Programming Environment for Parallel Genetic Algorithms
This thesis investigates an object-oriented programming environment for building parallel applications based on genetic algorithms (GAs). It describes the design of the Genetic Algorithms Manipulation Environment (GAME), which focuses on three major software development requirements: flexibility, expandability and portability. Flexibility is provided by GAME through a set of libraries containing pre-defined and parameterised components such as genetic operators and algorithms. Expandability is offered by GAME'S object-oriented design. It allows applications, algorithms and genetic operators to be easily modified and adapted to satisfy diverse problem's requirements. Lastly, portability is achieved through the use of the standard C++ language, and by isolating machine and operating system dependencies into low-level modules, which are hidden from the application developer by GAME'S application programming interfaces. The development of GAME is central to the Programming Environment for Applications of PArallel GENetic Algorithms project (PAPAGENA). This is the principal European Community (ESPRIT III) funded parallel genetic algorithms project. It has two main goals: to provide a general-purpose tool kit, supporting the development and analysis of large-scale parallel genetic algorithms (PGAs) applications, and to demonstrate the potential of applying evolutionary computing in diverse problem domains. The research reported in this thesis is divided in two parts: i) the analysis of GA models and the study of existing GA programming environments from an application developer perspective; ii) the description of a general-purpose programming environment designed to help with the development of GA and PGA-based computer programs. The studies carried out in the first part provide the necessary understanding of GAs' structure and operation to outline the requirements for the development of complex computer programs. The second part presents GAME as the result of combining development requirements, relevant features of existing environments and innovative ideas, into a powerful programming environment. The system is described in terms of its abstract data structures and sub-systems that allow the representation of problems independently of any particular GA model. GAME's programming model is also presented as general-purpose object-oriented framework for programming coarse-grained parallel applications. GAME has a modular architecture comprising five modules: the Virtual Machine, the Parallel Execution Module, the Genetic Libraries, the Monitoring Control Module, and the Graphic User Interface. GAME's genetic-oriented abstract data structures, and the Virtual Machine, isolates genetic operators and algorithms from low-level operations such as memory management, exception handling, etc. The Parallel Execution Module supports GAME's object- oriented parallel programming model. It defines an application programming interface and a runtime library that allow the same parallel application, created within the environment, to run on different hardware and operating system platforms. The Genetic Libraries outline a hierarchy of components implemented as parameterised versions of standard and custom genetic operators, algorithms and applications. The Monitoring Control Module supports dynamic control and monitoring of simulations, whereas the Graphic User Interface defines a basic framework and graphic 'widgets' for displaying and entering data. This thesis describes the design philosophy and rationale behind these modules, covering in more detail the Virtual Machine, the Parallel Execution Module and the Genetic Libraries. The assessment discusses the system's ability to satisfy the main requirements of GA and PGA software development, as well as the features that distinguish GAME from other programming environments
Evolving hardware with genetic algorithms
Genetic techniques are applied to the problem of electronic circuit design, with an emphasis on VLSI circuits. The goal is to have a tool which has the performance and flexibility to attack a wide range of problems. A genetic algorithm is used to design a circuit specified by the desired input /output characteristics. A software system is implemented to synthesize and optimize circuits using an asynchronous parallel genetic algorithm. The software is designed with object-oriented constructs in order to maintain scalability and provide for future enhancements. The system is executed on a heterogeneous network of workstations ranging from Sun Sparc Ultras to HP multiprocessors. Testing of this software is done with examples of both digital and analog CMOS VLSI circuits. Performance is measured in both the quality of the solutions and in the time it took to evolve them
Parallel implementation of stochastic simulation for large-scale cellular processes
Experimental and theoretical studies have shown the importance of stochastic processes in genetic regulatory networks and cellular processes. Cellular networks and genetic circuits often involve small numbers of key proteins such as transcriptional factors and signaling proteins. In recent years stochastic models have been used successfully for studying noise in biological pathways, and stochastic modelling of biological systems has become a very important research field in computational biology. One of the challenge problems in this field is the reduction of the huge computing time in stochastic simulations. Based on the system of the mitogen-activated protein kinase cascade that is activated by epidermal growth factor, this work give a parallel implementation by using OpenMP and parallelism across the simulation. Special attention is paid to the independence of the generated random numbers in parallel computing, that is a key criterion for the success of stochastic simulations. Numerical results indicate that parallel computers can be used as an efficient tool for simulating the dynamics of large-scale genetic regulatory networks and cellular processes
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