81. 5. Program Development And Debugging Environment For Multicomputers The implementation demonstrates the ability to port pvm programs easily onto the pvmAP1000 delivers reasonable run-time performance for pvm programs http://cap.anu.edu.au/cap/reports/report94/debugging.html

5. Program Development and Debugging Environment for Multicomputers 5.1 Research Group Chris Johnson (Project Leader) David Walsh David Sitsky Markus Zellner 5.2 Objectives Parallel program debugging is harder than sequential debugging because of the increased complexity, the probe effect of any debugging technique, the difficulty of globally controlling a distributed program during debugging, and the possible effects of non-determinism. These problems are common to all parallel debugging tools. Additional characteristics of parallel programming resulting from experience and new architectures to be addressed are: programming for target machines with large numbers of medium-scale processors, in the 100-1000 processor range: this is the kilo-processor machine exemplified by the AP1000. The problems arise from the mass of information in execution and the complexity of interactions. the use of both MIMD and SPMD programming paradigms, and the increasing use of programming abstractions, portable programming libraries and specialised languages for parallel machines. Both the abstract programming view and the detailed machine distribution of data and processes are relevant to the user's understanding of program performance. No single debugging tool can provide the best exploration of program behaviour for many different users. Under the collective name of

82. Opera Directory Online and postmortem visualization support for pvm programs. pvm. Official Parallel Virtual Machine Software to run pvm programs over ATM networks. http://portal.opera.com/directory/?cat=101603

83. NIST SP2 Primer Message Passing With PVM(e) Before executing a pvm(e) message passing program, a ``Virtual Machine (VM) must be In coding a message passing program in pvm, calls to communication 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.

84. General User Information A way of starting pvm and have a pvmprogram named pvm_master_program to run on n. nodes, is . qsub -I -l nodes=n /* Open an interactive session on n http://www-id.imag.fr/Grappes/icluster/UsersGuide.html

Home APACHE PROJECT HP labs Grenoble Help ... People General User Information (UNDER RECONSTRUCTION) Getting Help : Send mail to staff-grappeHP@imag.fr if you need help on the cluster. Access Your Account Environment 4. Software: Programming Tools Programming for MPI Mathematic Tracing and Scientific Libraries 5. Running Jobs Selecting MPI Libraries Compiling Running MPICH programs Running LAM programs ... Compiling and running Athapascan-1 programs 6. Using the Batch Scheduler Submitting Jobs to PBS Running Interactive Jobs Storage and file distribution Known bugs ... Quoting ID support inside publications Acces (OK) The cluster is reachable using ssf/ssh onto frontal-grappes.inrialpes.fr . From this machine, you can connect to one of the login servers of the icluster to compile or launch jobs on the cluster by using the command icluster which balances among login servers. To connect: ssf frontal-grappes.inrialpes.fr -t icluster or ssh frontal-grappes.inrialpes.fr -t icluster File transfer: the copy of files since/towards the cluster use scp: scp file frontal-grappes:destfile General development : All development work must be done on the cluster login nodes.

85. 5.3 Examples 5.3.1 Performance Analysis of pvm Programs. Assume that a performance analysis tool wants to measure the time spent by task 4178 in the pvm_send call. http://www.lrr.in.tum.de/~omis/OMIS/Version-2.0/version-2.0/node17.html

Next: 5.4 Interface Procedures Previous: 5.2 Introduction to Monitoring Services Subsections 5.3.1 Performance Analysis of PVM Programs 5.3.2 Debugging of MPI Programs 5.3 Examples In the following two subsections we will present short examples that will show how the monitoring interface supports different tools, namely a performance analysis system and a debugger. Although the basic services and extension services will not be defined until later in this document, their semantics should be intuitively clear in the examples. The primary goal is to give an impression of the interface's structure and expressiveness, rather than of its concrete services. 5.3.1 Performance Analysis of PVM Programs Assume that a performance analysis tool wants to measure the time spent by task 4178 in the call. In addition, the tool may want to know the total amount of data sent by this task, and it may want to store a trace of all barrier events. Then it may send the following service requests to the monitoring system: The tokens c_1...c_4 are identifiers for the conditional service requests. They are delivered by the monitoring system as a direct reply to the request. The fifth request, which is unconditional, does not yield such a token.

86. Docs.sun.com: Prism 5.0 User's Guide About using MP Prism with pvm programs see Section 10.9. About using MP Prism with Sun MPI programs see Section 10.10 http://docs.sun.com/app/docs/doc/805-1552/6j1h2sm2i?a=view

87. FZJ-ZAM-BHB-0139: 7. Parallelization With Message Passing Example 7.3 shows a simple pvm program written in C (compare with program 7.1), To compile a pvm program, the Message Passing Toolkit has to be loaded. http://www.fz-juelich.de/zam/docs/bhb/bhb_html/d0139/node7.html

Next: 8. Code Optimization Up: Previous: 6. Parallelization on Cray Subsections 1. MPI 1. The Most Basic Procedures 2. How to Compile and Start an MPI Program Frequently asked questions, common errors and known bugs ... 4. Global Operations 7. Parallelization with Message Passing The CRAY T3E system supports the message-passing model which is basic for most distributed memory multicomputers. For portability reasons the message-passing libraries are also available on the PVP systems. A parallel program, which is run on a computer with distributed memory, needs a way to exchange information between the different processes. There are two ways in which this can be accomplished: using one-sided communications, where only the reading or writing process has to invoke a command, or using message-passing, where sender and receiver have to cooperate to enable the flow of information. While the shared-memory operations have a very high performance, the message-passing operations are slower. But the advantage of message-passing programs is the portability: programs using a standard message-passing library will not only run on the CRAY T3E, but on many other systems, e.g. the IBM SP2, the Intel Paragon, workstation clusters, and so on. While no Cray-specific message passing library is available for the CRAY T3E, two standard libraries are supported: MPI and PVM, which will be discussed in the next sections. The last section contains information on the shared-memory operations.

88. Citebase - Control And Debugging Of Distributed Programs Using Fiddle Monitoring pvm programs using the DAMS approach. Lecture Notes in Computer Science, 1497273280, 5th Euro pvm/MPI 1998. G/A, CKW00 JC Cunha, P. Kacsuk, http://citebase.eprints.org/cgi-bin/citations?id=oai:arXiv.org:cs/0309049

89. Beowulf Paper, CS5204 Many pvm programs are written in a Master/Slave model. For a pvm program to run in Master/Slave configuration, the master node will have to perform http://www.linuxchick.org/beowulf.html

The Beowulf Project Or, What Happens If the Supercomputing Researcher Graduates? Rachel Walls CS 5204 Dec 3, 2001 Introduction Supercomputing is a realm of information sciences that is not often in the news, but it runs many of the important applications behind topics that are in the news: weather forecasting, medical and biological research, and graphics and media rendering [13]. It takes an army of computers to process weather data to create a forecast, or to render scenes for films like Titanic or Antz , or to churn through data gathered about proteins, or to help Boeing design rockets [12]. When a project is expected to need Petaflops of computing time, like those for molecular dynamics at Los Alamos National Labs [5] or the ASCI project, which allows the DOE to test nuclear weapons without explosions [11], serious power is called for. And as different industries realize data sets that require that much number crunching, more work will need to be done on supercomputer class systems. The Beowulf project was started by a NASA contractor to bring supercomputing to any lab that needed it. Supercomputers built by commercial entities like SGI are prohibitively expensive for many scientific installations [5,1]. Supercomputing environments developed at research institutions may find themselves backed into corners by requirements of specific hardware doctoring and special communications equipment. They also generally only last as long as the researcher or group working on them is still working on their degree(s) [10]. Projects like IVY and Mirage, while adding significantly to the body of knowledge in distributed computing, may only run for a few years, or may not even be complete systems, as they are often built to facilitate research in a specific area or algorithm.

90. HICSS-31 : Software Technology Track - Computational Steering Minitrack The CUMULVS system, supporting the interactive steering of pvm programs, was released in September of 1996, and an initial presentation made at the 1996 pvm http://www.cs.wustl.edu/~eileen/HICSS-CS.html

HICSS-31 : Software Technology Track Computational Steering Minitrack The Conference : HICSS-31, the Hawaiian International Conference on System Science is the thirty-first in a series of conferences devoted to advances in the information, computer, and system sciences. The conference encompasses developments in both theory and practice, and is organized into tracks. The Computational Steering Minitrack is a component of the Software Technology track. Abbreviated Call for Papers : Computational steering permits users to dynamically adjust parameters of an executing computation or system, enabling researchers to monitor and guide their applications and systems. Despite advances in steering technology and infrastructure achieved by these researchers, numerous challenges remain. The Computational Steering Minitrack will bring together researchers, tool developers, and users of interactive computational steering techniques from industry, academia, and government to discuss developments, applications, problems, and solutions in this rapidly growing field. Topics will include, but are not limited to: Computational Steering applied to: Performance Optimization Scientific Visualization Scientific Computations Computational Fluid Dynamics Protein Folding Atmospheric Modeling Medicine Finite Element Analysis Modeling and Design Simulations Network Configuration and Management Combinatorial Optimization algorithms Parallel and Distributed algorithms Parallel and Distributed systems Real-time systems Object-Oriented systems

A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z

Page 5     81-90 of 90    Back | 1  | 2  | 3  | 4  | 5