Parallel Processing of the Evolving Tree Transformation System

Heterogeneous Network-Based Concurrent Computing Systems

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.

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.

In modern day pattern recognition, neural nets are used extensively. General use of a feedforward neural net consists of a training phase followed by a classification phase. Classification of an unknown test vector is very fast and only consists of the propagation of the test vector through the neural net. Training involves an optimization procedure and is very time consuming since a feasible local minimum is sought in weight space. If the training algorithm is based on error backpropagation the optimization procedure consists of the following steps: computation of the activation of the net when all the training examples are presented to it;computation of an error function based on the activation;computation of the gradients at a point in weight space; and finally,the adaptation of the weight values of the net. In this paper we present an analysis of a parallel implementation of the backpropagation algorithm using conjugate-gradient optimization for a three-layered, feedforward neural network, using networked workstations as a virtual parallel machine. The instance of the virtual machine is the PVM system, developed at Oak Ridge National Laboratory. We compare the overall performance of the parallel machine with averaged sequential runs in a typical research environment. From this, we identify the general requirements such as the size of the data set and neural net which render the parallel implementation useful, compared with the sequential execution of the same neural net training procedure.

For many large scientific applications, computing on a cluster is a viable, economical alternative to a dedicated parallel machine. Application performance on a cluster is largely determined by the speed of the underlying communication network. The authors use a parallel network approach to improve the communication network performance. More specifically, they use multiple networks based on Ethernet to improve the performance. The authors modified the parallel virtual machine (PVM) environment to take advantage of multiple Ethernets by using round robin scheduling strategy. The modified PVM, referred as PPVM, showed an improvement of 35% over PVM under no background load, and an improvement of 100% under background load on two channels. >

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.

Paradigms for the parallel programming of heterogeneous machines through an interface compiler

It is often necessary to get steady-state waveforms of a power electronic circuit for its design or analysis. Direct steady-state analysis techniques have been researched for efficient simulation. The shooting method is one of these and it is often utilized. It is known that its multiple type form is numerically more stable than the usual single type one. Furthermore it is suitable for parallel processing and it is possible to reduce its computation time. Today parallel computing systems, or PC cluster systems which are based on PVM (parallel virtual machine) or MPI (message passing interface), are easily available. This paper proposes an efficient steady-state simulation technique based on the multiple shooting method for a power electronic circuit by parallel processing. First the principle is investigated and made clear how it works. Then it is extended to be applicable to the envelope following analysis for much more efficient simulation by combining the principles. Finally the developed method can utilize both advantages of the two principles. It takes a long CPU time to get steady-state solution even by the single shooting method. By applying the single shooting envelope following technique, it is reduced to 1/10 for an example case. By applying the proposed multiple shooting EF technique, it is reduced about to 1/30 with 10 CPUs. This shows effectiveness of the method.

Parallel PCG Algorithm On Distributed Network By PVM

Parallel workstation clusters and MPI for sparse systems in computational science

Adaptive data parallel computing on workstation clusters

The automatic parallelization of C has always been frustrated by pointer arithmetic, irregular control flow and complicated data aggregation. Each of these problems is similar to familiar challenges encountered in the parallelization of more rigidly-structured languages, such as FORTRAN77. By creating a mapping from one language to the other, we can expose the capabilities of existing automatically parallelizing compilers to the C language. In this paper, we describe our approach to mapping applications written in C to a form suitable for the Polaris source-to-source FORTRAN compiler. We also describe the improvements in the compiled applications realized by this second level of transformation and show results for a small application in comparison to commercial compilers. We describe our model of a Virtual Speculative Parallel Machine as the target of our compiler.

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