21. Using Derived Data Types With MPI | Linux Magazine programs written for distributed memory, parallel computers, including Beowulf clusters, utilize the Message Passing Interface (MPI) or Parallel Virtual Machine (PVM) programming http://www.linux-mag.com/id/1332

22. A Comparison Of The Iserver-Occam, Parix, Express, And PVM Programming Environme A Comparison of the IserverOccam, Parix, Express, and PVM Programming Environments on a Parsytec GCel http://wotan.liu.edu/docis/dbl/hpcnhp/1994__253_ACOTIP.htm

The Digital Librarian's Digital Library search D O CIS  Do cuments in  C omputing and I nformation  S cience Home Journals and Conference Proceedings High-Performance Computing and Networking A Comparison of the Iserver-Occam, Parix, Express, and PVM Programming Environments on a Parsytec GCel Peter M. A. Sloot, Alfons G. Hoekstra, Louis O. Hertzberger Journal Title: High-Performance Computing and Networking Date: 1994 This data comes from DBLP This page is maintained by Angela Cornwell and Thomas Krichel It was last updated on 2006-04-12

23. NIST SP2 Primer: Message Passing With PVM(e) Getting started with PVM programming. With some exceptions (noted in section on differences between PVM and PVMe), PVM and PVMe are source code compatible. http://math.nist.gov/~KRemington/Primer/mp-pvme.html

Message passing with PVM(e) PVMe is the IBM proprietary version of the widely used PVM message passing library from Oak Ridge National Laboratory. Its compatibility with the public domain PVM package generally lags one release behind. (For example, the current release of PVMe is compatible with PVM 3.2.6) We will assume that the reader has a basic understanding of the concept of message passing communication of data on distributed memory parallel architectures. PVM(e) is described here primarily by example, but for the interested reader, extensive PVM documentation is available from the PVM authors at PVM on Netlib and from the online documentation available on danube Setting up the PVM environment Before executing a PVM(e) message passing program, a ``Virtual Machine" (VM) must be initiated by the user. This is done by invoking what is known as the PVM(e) daemon, a process which sets up and maintains the information needed for PVM(e) processes to communicate with one another. In general, the user can ``customize" the VM by specifying which hosts it should include, the working directory for PVM processes on each host, the path to search for executables on each host, and so on. PVMe behaves slightly differently than PVM, since nodes are controlled through a Resource Manager. Rather than specifying particular nodes for the virtual machine, the user requests a certain number of nodes, and the Resource Manager reserves these nodes for that one particular user. This is to allow the user dedicated access to the High Performance Switch for the duration of their PVMe job.

24. Parallel Processing Info Computing Parallel Processing Info. PVM ; Programming http://www.seekon.com/Comp/Parallel_Processing/

25. Avaya Partner ACS Phone System, The Number One Small Business Phone System Avaya's Partner phone system is the world's number one selling small business telephone system. A Partner Phone System can be programmed by Carroll Communications and shipped ready http://www.carrollcommunications.com/phone_system_partner_acs.html

Avaya Partner ACS Telephone System Phone equipment Merlin magix Internet Marketing Services T1 lines ... Business Phone Systems Don ’t just survive... Thrive. Almost every business today faces the mounting pressure to sustain and even grow revenue. As your customers scrutinize their budgets, small businesses need ways to thrive in an increasingly competitive environment. Enabling greater productivity across your business without adding people or substantially increasing costs – is an imperative every small business must embrace to stay ahead of competitors and in front of its customers. Productivity improvements help keep businesses lean, while driving greater customer service and in turn, more revenue. One solution has helped over one million small businesses do all that and more: the Avaya PARTNER® Advanced Communications System. Over a million sold to small businesses like yours! Every small and mid-size business needs ways to reduce costs and improve the way it operates. Like every business, you’re looking to keep all your customers, add new ones and grow at the pace that’s right for you. Avaya understands this. With over one hundred years of experience as a leader in communications, we know that the right solution for your business is one that helps you increase profitability, improve productivity and gain competitive advantages. Small Business big plans?

26. Parallel/Distributed Computing Using Parallel Virtual Machine What is PVM? Message passing system that enables us to create a virtual parallel computer using a network of workstations. Basic book PVM Parallel Virtual Machine A Users' Guide and http://www.csc.liv.ac.uk/~igor/COMP308/ppt/Lect_22.pdf

27. Scientific Commons A Comparison Of The Iserver-Occam, Parix A Comparison of the IserverOccam, Parix, Express, and PVM Programming Environments on a Parsytec GCel (1994 http://en.scientificcommons.org/8075310

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28. Urban Dictionary: HPC High Performance Computing Using multiple teraflop speed computers ,bound together by a MPI or PVM programming, to achieve a goal faster than one http://www.urbandictionary.com/define.php?term=HPC

29. Computer Terminology The F77+PVM programming model that we are using is, however, much simpler, in that the node is the smallest element of the computer that can be programmed, and it is always used http://www.netlib.org/benchmark/top500/reports/report94/benrep3/node4.html

Next: How to get Up: Introduction Previous: Programming Models Computer Terminology Nevertheless, most of our benchmarks are written to the distributed-memory MIMD programming model, with so-called scalable distributed-memory hardware in mind. The hardware of such computers consists of a large number of "nodes" connected by a communication network (typically with a mesh or hypercube topology), across which messages pass between the nodes. Each node typically contains one or more microprocessors for performing arithmetic (perhaps some with vector processing capabilities), communication chips that are used to interface with the network, and local memory. For this reason, the computational parts of the computer are commonly referred to as either "nodes" or "processors", and the computer is scaled up in size by increasing their number. Both names are acceptable, but "nodes" is perhaps preferable for use in descriptions of the hardware, because we can then say that one node may contain several processors. The F77+PVM programming model that we are using is, however, much simpler, in that the node is the smallest element of the computer that can be programmed, and it is always used as if it contained a single processor, because it runs a single F77 program. If the hardware actually uses several processors to run the single program faster, this should be beneficial to the benchmark result, but it is hidden from the programmer. Thus from the programmer's view, there is no useful distinction between node and processor, and in this document we have tried to use the term "processor" consistently to mean the "logical processor" of the F77+PVM programming model, whether or not it may be implemented by one or several physical processors.

30. Function Decomposition A NAME=384 /A A diagram providing an overview of this example is shown in Figure (and will also be used in a later chapter dealing with graphical PVM programming). http://www.netlib.org/pvm3/book/node33.html

Next: Porting Existing Applications Up: Workload Allocation Previous: Data Decomposition Function Decomposition Parallelism in distributed-memory environments such as PVM may also be achieved by partitioning the overall workload in terms of different operations. The most obvious example of this form of decomposition is with respect to the three stages of typical program execution, namely, input, processing, and result output. In function decomposition, such an application may consist of three separate and distinct programs, each one dedicated to one of the three phases. Parallelism is obtained by concurrently executing the three programs and by establishing a "pipeline" (continuous or quantized) between them. Note, however, that in such a scenario, data parallelism may also exist within each phase. An example is shown in Figure , where distinct functions are realized as PVM components, with multiple instances within each component implementing portions of different data partitioned algorithms. Although the concept of function decomposition is illustrated by the trivial example above, the term is generally used to signify partitioning and workload allocation by function

31. Urban Dictionary: Earth Simulator High Performance Computing Using multiple teraflop speed computers ,bound together by a MPI or PVM programming, to achieve a goal faster than one http://www.urbandictionary.com/define.php?term=earth simulator

32. The JPVM Home Page Java has been found to be easy to learn and scalable to complex programming problems, and thus might help avoid some of the incidental complexity in PVM programming http://www.cs.virginia.edu/~ajf2j/jpvm.html

JPVM The Java Parallel Virtual Machine NOTE: If you are currently using JPVM, please download the latest version below (v0.2.1, released Feb.2, 1999). It contains an important bug fix to pvm_recv. JPVM is a PVM-like library of object classes implemented in and for use with the Java Programming language. PVM is a popular message passing interface used in numerous heterogeneous hardware environments ranging from distributed memory parallel machines to networks of workstations. Java is the popular object oriented programming language from Sun Microsystems that has become a hot-spot of development on the Web. JPVM, thus, is the combination of both - ease of programming inherited from Java, high performance through parallelism inherited from PVM. Why a Java PVM? The reasons against are obvious - Java programs suffer from poor performance, running more than 10 times slower than C and Fortran counterparts in a number of tests I ran on simple numerical kernels. Why then would anyone want to do parallel programming in Java? The answer for me lies in a combination of issues including the difficulty of programming - parallel programming in particular, the increasing gap between CPU and communications performance, and the increasing availability of idle workstations. Developing PVM programs is typically not an easy undertaking for non-toy problems. The available language bindings for PVM (i.e., Fortran, C, and even C++) don't make matters any easier. Java has been found to be easy to learn and scalable to complex programming problems, and thus might help avoid some of the incidental complexity in PVM programming, and allow the programmer to concentrate on the inherent complexity - there's enough of that to go around.

33. On The Viability Of Component Frameworks For High Performance Distributed Comput The Harness system, a software backplane enabling reconfigurable distributed concurrent computing is used to emulate the PVM programming environment. http://csdl.computer.org/comp/proceedings/hpdc/2002/1686/00/16860275abs.htm

34. Scientific Commons A. Parsytec Gcel A Comparison of the IserverOccam, Parix, Express, and PVM Programming Environments on (2008) A Parsytec Gcel http://en.scientificcommons.org/a_parsytec_gcel

35. Title Page For E-project-042705-074945 Budapest, Hungary, an existing parallel application that allows users to create and execute a parallel program in an efficient manner without knowledge of MPI or PVM programming. http://www.wpi.edu/Pubs/E-project/Available/E-project-042705-074945/

Title page for E-project-042705-074945 Project Type MQP Submission date Authors Domenic K Giancola, CS Amanda Jamin, CS URN E-project-042705-074945 Title GRID Portal Application Visualization Advisor Sarkozy, Gabor N, CS Availability unrestricted Abstract Parameter studies are useful applications for researchers; however, these programs, although helpful, tend to be computationally expensive and due to their long execution time become tedious to execute. In this project we explored a method of implementing a parameter study module for the P-GRADE Portal at MTA-SZTAKI; Budapest, Hungary, an existing parallel application that allows users to create and execute a parallel program in an efficient manner without knowledge of MPI or PVM programming. Files CS-GXS-0502.pdf Browse by Author Browse by Department Search all available E-projects Questions? Email project-questions@wpi.edu Maintained by webmaster@wpi.edu

36. DBLP Record 'conf/hpcn/SlootHH94' and Alfons G. Hoekstra and Louis O. Hertzberger}, title = {A Comparison of the IserverOccam, Parix, Express, and PVM Programming http://dblp.uni-trier.de/rec/bibtex/conf/hpcn/SlootHH94

DBLP Record ' conf/hpcn/SlootHH94 BibTeX DBLP DBLP Home Conferences ... Author 2002-01-04 by Michael Ley ley@uni-trier.de

37. G & G Inc's Training Page Parallel Virtual Machine (PVM) Programming others on request Each course taught is customized to meet the specific needs and time schedule of the customer. http://www.gginc.biz/taught.html

38. On The Viability Of Component Frameworks For High Performance Distributed Comput The Harness system, a software backplan e enabling reconfigurable distributed concurrent computing is used to emulat e the PVM programming environment. http://doi.ieeecomputersociety.org/10.1109/HPDC.2002.1029927

39. 148 The approach is to use the Parallel Virtual Machine (PVM) programming system which is platform independent and will result in a simulation tool that can run on, in general, a http://www.er.doe.gov/sbir/Awards_Abstracts/sbir/cycle15/phase1/148.htm

Parallel Virtual Machine Parallelization of the ARGUS Simulated Code FARTECH, Inc., 3146 Bunche Avenue, San Diego, CA 92122-2247; (619) 455-6607 Dr. Jin-Soo Kim, Principal Investigator Dr. Jin-Soo Kim, Business Official DOE Grant No. DE-FG03-97ER82380 Amount: $75,000 Commercial Applications and Other Benefits as described by the awardee: This capability would bring affordable, tractable, 3D electromagnetic/electrostatic PIC modeling to many designers for the first time, as well as significantly increase the speed, complexity, or scale-size of problems for designers having more substantial computational resources available. All design centers from industry to nationally-funded centers will benefit, since the product will be platform independent and will run on virtually every computer system used for accelerator component design today. Return to Table of Contents

40. "High Performance Cluster Computing : Volume 2, Programming And Applications" 3 MPI and PVM Programming 4 Linking MessagePassing Environments 5 Active Objects 6 Using Scoped Behavior to Optimize Data Sharing Idioms 7 Component-Based Development Approach http://www.cloudbus.org/~raj/cluster/v2toc.html

High Performance Cluster Computing : Programming and Applications, Volume 2 By Buyya, Raijkumar (editor) Hardcover; 664 Pages Published by Prentice Hall, USA Date Published: 05/1999 ISBN: 0130137855 Table of Contents Preface I Programming Environments and Development Tools 1 Parallel Programming Models and Paradigms 2 Parallel Programming Languages and Environments 3 MPI and PVM Programming 4 Linking Message-Passing Environments 5 Active Objects 6 Using Scoped Behavior to Optimize Data Sharing Idioms 7 Component-Based Development Approach 8 Hypercomputing with LiPS 9 An Efficient Tuple Space Programming Environment 10 Debugging Parallelized Code 11 WebOS: Operating System Services for Wide-Area Applications II Java for High Performance Computing 12 Distributed-Object Computing 13 Java and Different Flavors of Parallel Programming Models 14 The HPspmd Model and its Java Binding 15 Web-Based Parallel Computing with Java III Algorithms and Applications 16 Object-Oriented Implementation of Parallel Genetic Algorithms 17 Application-Specific Load Balancing on Heterogeneous Systems 18 Time Management in Parallel Simulation 19 Hardware System Simulation 20 Real-Time Resource Management Middleware: Open Systems and Applications 21 Data Placement in Shared-Nothing Database Systems 22 Parallel Inference with Very Large Knowledge Bases 23 MaRT: Lazy Evaluation for Parallel Ray Tracing 24 Fast Content-Based Image Retrieval 25 Climate Ocean Modeling 26 Computational Electromagnetics 27 CFD Simulation: A Case Study in Software Engineering

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