A generic multi virtual machines architecture for distributed parallel operating systems design

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.

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.

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.

The Evolving Tree Transformation System (ETTS) has been developed as a model of a reconfigurable learning machine. In this paper we present an examination of the opportunities for parallel processing in the Evolving Tree Transformation System (ETTS) as well as an implementation which takes advantage of the inherent parallelism. The implementation uses the distributed software system Parallel Virtual Machine (PVM) to handle process control and inter-process communication. Experiments conducted on the implementation are presented which reveal a strong correlation between performance, the size and nature of the input data and the size of the parallel machine. Using a sixteen processor virtual parallel machine, we are able to achieve a speedup of 13.5 using idealized data and a speedup of 7.6 using real (natural) data. Performance models for the parallel implementations are also presented.

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

With the rapid development of virtualization technology, virtual machine (VM) is widely used by cloud computing - the more and more popular computing paradigm. Thus, in order to guarantee the security of cloud computing, it is necessary to securely identify the kernel of VM, the software stack running on VM and the hardware platform which VM relies on. This paper designs a system of trusted connection based on virtual machine architecture and implements a system prototype. Our system not only can securely measure and identify the kernel of VM, the software stack running on VM and the hardware platform which VM relies on, but also can realize isolations to untrusted VMs. There are three main parts in our system: trusted chain, attestation and isolation. The experiments described in this paper prove that our system ensures trusted connection of VMs and achieves isolations to untrusted VMs. The performance of our system is also analyzed and evaluated. According to the analysis results, our system is practical in terms of performance.

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. >

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.

Parallel workstation clusters and MPI for sparse systems in computational science

Adaptive data parallel computing on workstation clusters

Image coding is imperative for successful implementation of visual communications applications. With the emergence of enhanced multimedia technology, numerous video applications have emerged like multimedia conferencing, video on demand and DVDs. Coding is an essential component of all video applications and it becomes necessary to have improved coding techniques for faster applications. This paper discusses how parallel video coding on load balanced multi-processor systems can help in incorporating efficient coding techniques like vector quantization into practical applications. Two parallel processing platforms will be discussed namely the heterogeneous network of workstations and the TI C40 DSP chips. The software platforms used for these are the Parallel Virtual Machines (PVM) programming model and Parallel C respectively. An integration of the two programming models by using a PVM to Parallel C translation and the effect of load balancing for improved performance will also be discussed.

Nowadays computing plays a vital role in information technology and all other fields. Yes, the cloud computing is one of the biggest milestone in most leading next generation technology and booming up in IT filed and business sectors. In our day to day life the data is being generated is enormous amount such as tera (TB), peta (PB), zeta (ZB) bytes. Hadoop Map Reduce is the popular distributed computing paradigm to process data intensive jobs in cloud. Completion time goals for deadline of map reduce jobs set by users are becoming crucial in existing cloud based data processing environments like Hadoop. In this paper proposes a real-time implementation of multi node Hadoop virtual cluster on open stack cloud environments and also it processes the huge data sets in parallel different virtual machines (VMs) and it compares average execution time for different node virtual clusters and various size inputs.

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.

The authors report on the use of formal methods for the development of parallel operating systems for two experimental declarative systems over a five-year period. A common specification approach has evolved as part of the development of these two very different systems: one being for a parallel graph reduction machine and written in a functional language enhanced with state-based objects, the other was written in C/sup ++/. A brief overview of each system is given before concentrating on the use of formal methods. A description is given of how both a technique for formally specifying sequential systems (VDM) and a technique for specifying concurrent systems (temporal logic) have been used together. In both cases, the issue of verification is addressed. >