Performance considerations in software multicasts

An efficient numerical method to simulate the conjugate heat transfer between a fluid and moving bodies is presented. The numerical approach is based on hierarchical Cartesian meshes and formulated for immersed boundaries such that simulations can be performed for solid bodies of arbitrary shape and without interpolation assumption on parallel high-performance computing systems. To ensure a consistent temperature and heat flux across the fluid–solid interface, a direct iterative procedure incorporating the solid and the fluid domains is used such that a fully energy conserving method is obtained. The method is validated by comparing simulation results for generic three-dimensional problems of stationary and moving geometries with reference data, for which also the conservation properties and the accuracy of the method are demonstrated. Results for the heat transfer of a sphere at a fixed position in an adiabatic domain, the flow over a backward-facing step, and a shape-transforming body are shown. Additionally, results for moving bodies are presented, which demonstrate the accuracy and versatility of the developed method. Finally, the efficient implementation of the algorithm for usage on parallel computing systems is shown by scaling and benchmark plots.

The advancements in technology in this contemporary time require special computing devices to able to manage the increase of the computational power. However, the limitation of the sequential computing systems stands as an obstacle in fulfilling these requirements. Thus, the research community looking for a viable solution able to solve the grand computing problems. A parallel computing system is a collection of processing elements that cooperate and communicate to provide a fast solution for a problem. A new symmetric network named Shifted Peterson Network (SPN) has been proposed in this paper. The basic module (BM) of the suggested network is composed of ten nodes, and the BMs of SPN are connected through external nodes to construct an advanced level of this network. Furthermore, the connectivity of SPN is based on a shifting mechanism of the binary digits of each node. The architecture of SPN discussed in this paper, in addition, the static network performance parameters of this network has been evaluated and compared to popular conventional interconnection networks. The networks evaluated in terms of node degree, diameter, average distance, arc connectivity, bisection width, cost, wiring complexity, packing density, and message traffic density. SPN showed good results in almost all aspects compared to these networks. Therefore, SPN is a suitable choice for the massively parallel computer (MPC) systems.

In recent times the work on networks of processors has become very important, due to the low cost and the availability of these systems. This is why it is interesting to study algorithms on networks of processors. In this paper we study on networks of processors different Eigenvalue Solvers. In particular, the Power method, deflation, Givens algorithm, Davidson methods and Jacobi methods are analized using PVM and MPI. The conclusion is that the solution of Eigenvalue Problems can be accelerated by using networks of processors and typical parallel algorithms, but the high cost of communications in these systems gives rise to small modifications in the algorithms to achieve good performance.

Reducing energy consumption without affecting computational performance is a significant research driver in computer engineering. The Partitioned Global Address Space (PGAS) programming model provides a global address space for ease-of-use while providing locality-awareness for efficient execution. For symmetric multiprocessor (SMP) clusters, PGAS locality-awareness offers opportunities for intelligent energy management due to the inherent latencies in the interconnection network fabric. In this paper, the second in a series that explores PGAS power optimization, we present a detailed examination of how data locality-awareness can be exploited to improve the energy consumption of workload sharing on SMP clusters. We present a systematic methodology for analyzing a given algorithm's potential for locality-aware power optimization. We show how to automate the code modifications for an iterative stencil loop algorithm using a profiler that analyzes the algorithms' non-local access patterns and optimizes the code for thread-local data pre-fetch. We explore the use of dynamic voltage frequency scaling (DVFS) in conjunction with time-based optimizations and provide an extensible and precise power measurement framework for generating the experimental results. We compare optimized and CPU-default approaches to DVFS application at the program-execution and processor architecture level. We show how the latency of the cluster's interconnection network can impact the opportunities for power optimization by comparing the programs' energy/time behavior when the cluster nodes are connected by Ethernet or Infiniband network fabrics. For our representative iterative stencil loop algorithm, locality aware power optimizations show significant improvements in energy efficiency over native code running in default processor power modes.

A Schwarz domain decomposition method for solution of transient unsaturated water flow on parallel computers

With the development of multicore-processor, the parallel computer system are gaining a rapid progress from supercomputer to personal computer. But the knowledge on parallel computing is seldom involved in the traditional undergraduate stage of computer specialty. Under the current situation, it is very necessary to set the parallel computing as an independent course in the teaching plan of computer specialty. In this paper, the necessity of setting parallel computing course and how to construct the curriculum are discussed. Materials and the arrangement of teaching content, curriculum experiments, and the relationship with the other subjects are also discussed in detail.

ABSTRACTAdding the number of computing nodes is a common approach to achieving higher performance in a parallel computing system. However, with constraint of fixed system architecture and fixed algorithm structure, it is difficult to improve the performance of parallel computing only by extending its scale absolutely. To realize such extension with fixed structure, we analyze key factors from architecture and parallel task, which affect the scalability, and then use the weighted graph to model architecture as well as parallel task. Especially, focusing on the case that architecture graph and parallel task graph are homogeneous, we propose the extension method of graph similarity; for the case that architecture graph and parallel task graph are heterogeneous, a critical-path-unchanged scaling method is proposed. Actually, the above two extending methods do not change the graph's structure. They only adjust the node weight and edge-weight in the relevant graph. Furthermore, through mathematical derivation, some conclusions about the new scaling methods are drawn. Finally, in order to verify the effectiveness, some simulative experiments are conducted on the platform SimGrid. The experimental results show that the proposed methods can realize iso-speed-efficiency extension, and can guide practical extensions for parallel computing.

Summary The hybrid MPI-OpenMP model is a natural parallel programming paradigm for emerging parallel architectures that are based on symmetric multiprocessor (SMP) clusters. This paper presents a hybrid implementation adapted for an implicit finite-element code developed for groundwater transport simulations. The original code was parallelized for distributed memory architectures using MPI (Message Passing Interface) using a domain decomposition strategy. OpenMP directives were then added to the code (a straightforward loop-level implementation) to use multiple threads within each MPI process. To improve the OpenMP performance, several loop modifications were adopted. The parallel performance results are compared for four modern parallel architectures. The results show that for most of the cases tested, the pure MPI approach outperforms the hybrid model. The exceptions to this observation were mainly due to a limitation in the MPI library implementation on one of the architectures. A general conclusion is that while the hybrid model is a promising approach for SMP cluster architectures, at the time of this writing, the payoff may not be justified for converting all existing MPI codes to hybrid codes. However, improvements in OpenMP compilers combined with potential MPI limitations in SMP nodes may make the hybrid approach more attractive for a broader set of applications in the future.

Problem statement: Multicasting communication network accepted a single message from an application and delivered copies of the message to multiple recipients at different locations. Recently, there has been an explosion of research literature on multicast communication environment. The objective of this study were to contribute the complexity of supporting current multicast applications, (i) the lack of reliable multicast transport mechanisms at the network level and (ii) the lack of network support for large scale multicast communication. The scaling problem of secure multicast key distribution compounded for the case where sender-specific keys need to be distributed to a group and required for sender-specific authentication of data traffic and minimize control overhead (iii) compare RC4, AES-128,RS(2) and RS(3) computation time of both algorithms. Approach: Algorithms were collected and performed computation time. In general the multicast key distribution scheme implemented for distributing 128 bit session keys. Thus the Maximum Distance Separable Codes (MDS Codes) needed for their encoding and decoding process. In rekeying scheme errors were occurred during over period of time or at a particular point of time and to eliminate all these errors in the level of encryption and decryption mechanism. The MDS codes played an important role in providing security services for multicast, such as traffic, integrity, authentication and confidentiality, is particularly problematic since it requires securely distributing a group (session) key to each of a group’s receivers. Results: First we showed that internet multicasting algorithms based on reverse path forwarding were inherently unreliable and present a source-tree-based reliable multicasting scheme also. The new scheme proposed and used as an inter-gateway protocol and worked on top of the previously developed distance vector and link state internet routing schemes. Next, to support large scale applications, we presented a scheme for partial multicasting and introduced a new network level operation, called gather. The partial multicasting mechanism allowed messages to be delivered to subsets of multicast destinations, while the gather operation aids gateways in selectively suppressing redundant messages, thus reducing the message complexity. Conclusion: Hence the findings suggested the control overhead reasonably minimized and using simulations, we investigated the efficiency of our schemes in supporting scalable application domain based multicast communication.

The optimization of multicast communication for regular interconnection networks (IN) has been the subject of many studies in the literature. Extensions, however, the analysis of IN with arbitrary topology and background traffic poses a great challenge. In this context, a novel heuristic algorithm for multicast communication is proposed for arbitrary IN with finite capacity and bursty background traffic. The scheme facilitates interprocess communication amongst parallel computers and Grid computing networks as well as local and wide area networks and it is applicable to a single-port mode of message passing communication. The scheme utilizes a queue-by-queue decomposition algorithm for arbitrary open queueing network models, based on the principle of maximum entropy, in conjunction with an information theoretic decomposition criterion and graph theoretic concepts. Evidence based on empirical studies indicates the suitability of the scheme for achieving an optimal multicast communication cost.

We construct and analyse integration methods for solving initial value problems for implicit differential equations (IDEs) that can be efficiently used on parallel computer systems. We construct an IDE method for general IDEs of arbitrarily high index, and two methods that can be applied to partitioned IDEs. The partitioned IDE methods both exploit the special form of the problem and converge faster than the general IDE method. The first partitioned IDE method is suitable for higher-index problems, the second partitioned IDE method only applies to index 1 problems, but is considerably less expensive on parallel computers. This paper presents the results presented in June 1995 at the Seminario Matematico e Fisico organized by the Mathematics Department of the Polytechnics University of Milano.

Several existing parallel computer systems partition their system resources or consist of systems from different geographical locations. This work is focusing on extending the original goal-oriented parallel computer job scheduling policies to cover such systems. The goal-oriented parallel computer job scheduling policies are proposed recently to handle conflicting objectives by utilizing a combinatorial search technique to find the most compromise schedule within a time limit. In this paper, some modifications to the original goal-oriented parallel computer job scheduling policy design are proposed and evaluated. The proposed policy is evaluated against basic priority backfilling techniques widely used in the field. Both homogeneous and heterogeneous parallel computer systems in terms of the computing power are evaluated. And, the design decision on the partition selection heuristic is also evaluated in this study. The experimental results show that the proposed policy produces good scheduling performances even when inaccurate runtime information is used.

First of all the paper considers the problem of verification or formalization of the online visualization and parallel computing system from the point of view of dynamic systems as a development of the theory of computational complexity for random processes. Considering problems involving truly big data inevitably leads to the use of a block approach which is also used in both information theory and stochastic differential equations. As a natural metaphor the graph signals were chosen. This is a graph in nodes, of which a spectral function is defined in the examples considered this is a function of color (RGB), height or amount of data. In parallel computing, a block can be associated with a computing unit (processor) and consider the problem of entropy (performance) maximization. In the developed on-line visualization and concurrent computing system for geometric parallelization, it is possible to implement and compare a stationary random process (equiprobable messages implemented using broadcasting and mixins) and a steady-state random process (point-to-point messages), which have different analytical solutions. Together, this allows concluding that the proposed implementation of a stationary process has a certain novelty; in addition, it was intended to be more convenient for automated parallelization. The problems of automatic load balancing (interpolation problem) and optimal scalability of parallel computing (extrapolation problem) are also considered. Not much has been done in the field of visualization verification for example a mesh visualization has been proposed to be considered as a parameterized model of a white-noise random process. Of course, this work cannot be considered complete, but the direction that the authors called stochastic semantics is obviously promising. The authors intend to take a close look at the established perturbed processes in the field of visualizations including those that take into account the human factor (the sketches of the formalization in the form of a discussion are given).

A comparison of OpenMP and MPI for neural network simulations on a SunFire 6800

The marketplace of high-performance computing