Adaptive data parallel computing on workstation clusters

Parallel workstation clusters and MPI for sparse systems in computational science

In this paper, we present a parallel iterative solution for large banded systems of linear equations based on incomplete LU-factorization (ILU). A master—workers parallel computing scheme is used. The proposed algorithm incurs reduced storage and communication overhead as compared to previous methods. The reduction in communication overhead has been achieved by pipelining the inter-workers message passing and limiting the communication between master and workers to initial work distribution and final results collection.The performance of the proposed algorithm is evaluated analytically and experimentally using Parallel Virtual Machine (PVM) as a parallel programming environment on a cluster of Linux work-stations. The obtained performance results show that the proposed algorithm exhibits higher efficiency for coarse grain computations corresponding to large matrix sizes and reduced number of processors.

Implementation of a parallel Genetic Algorithm on a cluster of workstations: Traveling Salesman Problem, a case study

The JPVM library is a software system for explicit message-passing based distributed memory MIMD parallel programming in Java. The library supports an interface similar to the C and Fortran interface provided by the Parallel Virtual Machine (PVM) system, but with syntax and semantics modifications afforded by Java and better matched to Java programming styles. The similarity between JPVM and the widely used PVM system supports a quick learning curve for experienced PVM programmers, thus making the JPVM system an accessible, low-investment target for migrating parallel applications to the Java platform. At the same time, JPVM offers novel features not found in standard PVM such as thread safety, multiple communication end-points per task, and default-case direct message routing. JPVM is implemented entirely in Java, and is thus highly portable among platforms supporting some version of the Java Virtual Machine. This feature opens up the possibility of utilizing resources commonly excluded from network parallel computing systems such as Macintosh and Windows-NT based systems. Initial applications performance results achieved with a prototype JPVM system indicate that the Java-implemented approach can offer good performance at appropriately coarse granularities.

The author presents the architecture of a new family of parallel operating systems based on an alternative design approach when compared to multiprogramming, namely, generic construction of parallel virtual machines. This construction is made according to needs of models for parallel programming which shall be supported as subsystems by the parallel operating system kernel. The author presents the PAROS architecture and its generic kernel ParX which has been designed for message passing parallel computers. Several parallel virtual machines are also described, such as for instance a diffusion virtual machine, and a message passing-shared data virtual machine. >

The linear quadtree is a useful data structure for representing an image for the sake of the storage saving and further image manipulations. In this paper we propose a linear quadtree coding scheme and implement this algorithm on the parallel virtual machine (PVM). Our goal is to demonstrate the applicability of using the PVM in combining the computing power of computers in a network to solve this kind of image processing problems. The processors in the PVM are organized as a master-slave paradigm and various numbers of processors are applied for different PVM’s to compare their performances. Experimental results show that the speedup of solving this image encoding problem in parallel is quite satisfactory. With such a PVM environment which is easily accessible in the public domain, high performance computing is truly possible without additional hardware cost.

Heterogeneous Network-Based Concurrent Computing Systems

The Parallel Virtual Machine (PVM) system is a software framework that enables heterogeneous concurrent computing in heterogeneous environments. Since its conception in 1989, it has evolved into a very popular and widely used platform for cluster and network computing. The programming model supported by PVM, straightforward formulation of the API, robust portable implementations, and timeliness have all played an important role in PVM's impact on high performance computing. We discuss the overall design of PVM and describe its ongoing evolution, recently introduced features, and future directions.KeywordsVirtual MachineMessage PassingRemote Procedure CallCommunication VolumeParallel Virtual MachineThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

MATLAB Distributed Computing Server (MDCS) and Parallel Virtual Machine (PVM) software are two types of distributed computing environments. MDCS is recently used in selecting the best network training algorithm and assessing the effect of parallelization. Since it is practical and user friendly, it gives PVM the opportunity to become the communication paradigm of choice. The PVM provides a powerful set of process control and dynamic resource management features. In the distributed parallel computing (DPC), however, both solutions have different strengths and limitations. Based on these concerns, this paper compares the numerical analysis for mathematical modeling of large sparse 2D and 3D of second order parabolic partial differential equations (PDE) on MDCS and PVM based on parallel performance indicators (PPI). The PDE geometry is discretized into a sparse grid structure using the FDM method. In using a method with the highest accuracy, Parallel Alternating Group Explicit (PAGE) scheme was chosen. The parallel strategies focus on the PAGE's convergence speed and various domain de-composition techniques, as well as a block iterative scheme and load balance using fine granular techniques. Furthermore, comparison of distributed computing environments also relies on multiple processors running on Unix-like operating systems with Fedora installed to support large-scale simulations. The analysis and validation of PPI for both communication software is also investigated in this paper. For a sequential algorithm, accuracy, estimation of error, and stability are employed as indicators. As a conclusion, based on the PPI and numerical analysis, the tables and graphs show that compared to MDCS, PVM is a better environment for solving multidimensional parabolic PDE modeling.KeywordsDistributed computingParabolic PDEsMDCSPVM

This paper presents GA-PVMINER, a parallel genetic algorithm that uses the Parallel Virtual Machine (PVM) to discover rules in a database. The system uses the Michigan's approach, where each individual represents a rule. A rule has the form if condition then prediction. GA-PVMINER is based on the concept learning framework, but it performs a generalization of the classification task, which can be called dependence modeling (sometimes also called generalized rule induction). In this task, different discovered rules can predict the value of different goal attributes in the prediction part of a rule, whereas in classification all discovered rules predict the value of the same goal attribute. The global population of genetic algorithm individuals is divided into several subpopulations, each assigned to a distinct processor. For each subpopulation, all the individuals represent rules with the same goal attribute in the prediction part of the rule. Different subpopulations evolve rules predicting different goal attributes. The system exploits both data parallelism and function parallelism.

Data mining is becoming increasingly important since the size of database grows even larger and the need to explore hidden rules from the database becomes widely recognized. We present PVMA algorithm, a parallel a priori algorithm that uses parallel virtual machine (PVM) to discovery rules in database. This algorithm exploits the data parallelism, by distributing the data being mined across all available processors.

When photovoltaic inverters operate in parallel, the output of each inverter’s impedance and the line impedance of aren’t consistent, resulting in the instantaneous voltage of each inverter at the common parallel point is not equal, so in the parallel inverters system, the circulating current is easy to form between each inverter, which affects the distribution of active power together with reactive power. In this paper, feedforward compensation is leaded-in active and reactive power control channels, and dynamic virtual complex impedance method for dynamic improvement of response performance of parallel virtual synchronous machine, so as to increase the degree of freedom of dynamic characteristic control and heighten the feedback speed. The parameters of D (damping drop coefficients) and K f (excitation coefficient) to make the inductive component of virtual impedance increase and counteract the resistive component, so as to the oscillation of output power is suppressed; Improving the stability of active power and reactive power and speed up the response speed of the system. The simulation display that parallel VSG in Matlab / Simulink make clear that the proposed control strategy is valid and practicable.

This paper discusses the design of a hybrid artificial neural network (ANN) system and its implementation in the Parallel Virtual Machine (PVM) environment. First, the PVM functions for supporting parallel applications and communications among multiple processes and multiple machines are investigated. Then, the design and construction of a hybrid ANN simulation software is proposed. It includes the user interface, control and SPMD computing levels. The software can be used for supporting parallel simulation of different kinds of learning algorithms and neural computing models.

Parallel implementation of 2D LGA and MD simulations

Tissue analysis is essential for dealing with a number of problems in cancer research. This research tries to address the problem of identifying normal, dysplastic and cancerous colonic mucosa by means of Texture analysis techniques. A genetic algorithm is used to interpret the results of those operations. Image- processing operations and genetic algorithms are both tasks requiring vast processing power. PVM (Parallel Virtual Machine), a message-passing library for distributed programming has been selected for implementing a parallel classification system. To further enhance PVM functionality a C++ object-oriented “PVM wrapper” was constructed.