Vaidy S. Sunderam

PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing

Part 1 Introduction: heterogeneous network computing trends in distributed computing PVM overview other packages. Part 2 The PVM system. Part 3 Using PVM: how to obtain the PVM software setup to use PVM setup summary starting PVM common startup problems running PVM programs PVM console details host file options. Part 4 Basic programming techniques: common parallel programming paradigms workload allocation porting existing applications to PVM. Part 5 PVM user interface: process control information dynamic configuration signalling setting and getting options message passing dynamic process groups. Part 6 Program examples: fork-join dot product failure matrix multiply one-dimensional heat equation. Part 7 How PVM works: components messages PVM daemon libpvm library protocols message routing task environment console program resource limitations multiprocessor systems. Part 8 Advanced topics: XPVM porting PVM to new architectures. Part 9 Troubleshooting: geting PVM installed getting PVM running compiling applications running applications debugging and tracing debugging the system. Appendices: history of PVM versions PVM 3 routines.

PVM: a framework for parallel distributed computing

The PVM system is a programming environment for the development and execution of large concurrent or parallel applications that consist of many interacting, but relatively independent, components. It is intended to operate on a collection of heterogeneous computing elements interconnected by one or more networks. The participating processors may be scalar machines, multiprocessors, or special-purpose computers, enabling application components to execute on the architecture most appropriate to the algorithm. PVM provides a straightforward and general interface that permits the description of various types of algorithms (and their interactions), while the underlying infrastructure permits the execution of applications on a virtual computing environment that supports multiple parallel computation models. PVM contains facilities for concurrent, sequential, or conditional execution of application components, is portable to a variety of architectures, and supports certain forms of error detection and recovery.

The PVM concurrent computing system: evolution, experiences, and trends

Abstract The PVM system, a software framework for heterogeneous concurrent computing in networked environments, has evolved in the past several years into a viable technology for distributed and parallel processing in a variety of disciplines. PVM supports a straightforward but functionally complete message passing model, and is capable of harnessing the combined resources of typically heterogeneous networked computing platforms to deliver high levels of performance and functionality. In this paper, we describe the architecture of PVM system, and discuss its computing model, the programming interface it supports, auxiliary facilities for process groups and MPP support, and some of the internal implementation techniques employed. Performance issues, dealing primarily with communication overheads, are analyzed, and recent findings as well as experimental enhancements are presented. In order to demonstrate the viability of PVM for large scale scientific supercomputing, the paper includes representative case studies in materials science, environmental science, and climate modeling. We conclude with a discussion of related projects and future directions, and comment on near and long-term potential for network computing with the PVM system.

Network-based concurrent computing on the PVM system

Concurrent computing environments based on loosely coupled networks have proven effective as resources for multiprocessing. Experiences with and enhancements to PVM (Parallel Virtual Machine) are described in this paper. PVM is a software package that allows the utilization of a heterogeneous network of parallel and serial computers as a single computational resource. This report also describes an interactive graphical interface to PVM, and porting and performance results from production applications. 23 refs., 5 figs., 5 tabs.

Computational Science – ICCS 2005

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PIOUS: a scalable parallel I/O system for distributed computing environments

PIOUS is a parallel file system architecture that provides cost-effective, scalable bandwidth in a network computing environment. PIOUS employs data declustering, to exploit the combined file I/O and buffer cache capacities of networked computing resources, and transaction-based concurrency control, to guarantee access consistency without explicit synchronization. This paper presents preliminary results from a prototype PIOUS implementation.<<ETX>>

Characterizing Grids: Attributes, Definitions, and Formalisms

Grid systems and technologies have evolved over nearly a decade; yet, there is still no widely accepted definition for Grids. In particular, the essential attributes that distinguish Grids from other distributed computing environments have not been articulated. Most approaches to definition adopt a static view and consider only the properties and components of, or the applications supported by, Grids. The definition proposed in this paper is based on the runtime semantics of distributed systems. Rather than attempt to simply compare static characteristics of Grids and other distributed computing environments, this paper analyzes operational differences, from the viewpoint of an application executing in both environments. Our definition is expressed formally as an Abstract State Machine that facilitates the analysis of existing Grid systems or the design of new ones with rigor and precision. This new, semantical approach proposes an alternative to the currently accepted models for determining whether or not a distributed system is a Grid.

Harness: a next generation distributed virtual machine

Abstract Heterogeneous Adaptable Reconfigurable Networked SystemS (HARNESS) is an experimental metacomputing system [L. Smarr, C.E. Catlett, Communications of the ACM 35 (6) (1992) 45–52] built around the services of a highly customizable and reconfigurable Distributed Virtual Machine (DVM). The successful experience of the HARNESS design team with the Parallel Virtual Machine (PVM) project has taught us both the features which make the DVM model so valuable to parallel programmers and the limitations imposed by the PVM design. HARNESS seeks to remove some of those limitations by taking a totally different approach to creating and modifying a DVM.

TOWARDS SELF-ORGANIZING DISTRIBUTED COMPUTING FRAMEWORKS: THE H2O APPROACH

A novel component-based, service-oriented framework for distributed metacomputing is described. Adopting a provider-centric view of resource sharing, this framework emphasizes lightweight software infrastructures that maintain minimal state, and interface to current and emerging distributed computing standards. In this model, resource owners host a software backplane onto which owners, clients, or third-party resellers may load components or component-suites that deliver value added services without compromising owner security or control. Standards-based descriptions of services facilitate publication and discovery via established schemes. The architecture of the container framework, design of components, security and access control schemes, and preliminary experiences are described in this paper.

Performance of the NAS Parallel Benchmarks on PVM-Based Networks

The NAS parallel benchmarks are a set of applications that embody the key characteristics of typical processing in computational aerodynamics. Five of these, the kernel benchmarks, have been implemented on the PVM system, a software system for network-based concurrent computing, with a view to determining the efficacy of networked environments for high-performance computational aerodynamics applications. We present results of porting and executing the NPB kernels in three different duster environments using low- to medium-powered workstations on Ethernet and two types of FDDI networks. Our results indicate that mediocre to good performance could be obtained despite the communications-intensive nature of the applications. In most cases, we were able to achieve performance levels within an order of magnitude of a Cray Y/MP-1 on eight-workstation clusters via optimizations to the PVM infrastructure alone, i.e., with little or no algorithmic modifications. However, our results also indicate that further improvements are possible and that network-based computing has the potential to be a viable technology for high-performance scientific computing.