JiST: an efficient approach to simulation using virtual machines

Discrete event simulators are important scientific tools and their efficient design and execution is the subject of much research. In this paper, we propose a new approach for constructing simulators that leverages virtual machines and combines advantages from the traditional systems‐based and language‐based simulator designs. We introduce JiST, a Java‐based simulation system that executes discrete event simulations both efficiently and transparently by embedding simulation semantics directly into the Java execution model. The system provides standard benefits that the modern Java runtime affords. In addition, JiST is efficient, out‐performing existing highly optimized simulation runtimes. As a case study, we illustrate the practicality of the JiST framework by applying it to the construction of SWANS, a scalable wireless ad hoc network simulator. We simulate million node wireless networks, which represents two orders of magnitude increase in scale over what existing simulators can achieve on equivalent hardware and at the same level of detail. Copyright © 2005 John Wiley & Sons, Ltd.     

Distributed computing paradigms for collaborative signal and information processing in sensor networks

In this paper, we report the development of an energy-efficient, high-performance distributed computing paradigm to carry out Collaborative Signal and Information Processing (CSIP) in sensor networks using mobile agents. In this paradigm, the processing code is moved to the sensor nodes through mobile agents, in contrast to the client/server-based computing, where local data are transferred to a processing center. Although the client/server paradigm has been widely used in distributed computing, the many advantages of the mobile agent paradigm make it more suitable for sensor networks. The paper first presents simulation models for both the client/server paradigm and the mobile agent paradigm. We use the execution time, energy and energy*delay as metrics to measure the performance. Several experiments are designed to show the effect of different parameters on the performance of the paradigms. Experimental results show that the mobile agent paradigm performs much better when the number of nodes is large while the client/server paradigm is advantageous when the number of nodes is small. Based on this observation, we then propose a cluster-based hybrid computing paradigm to combine the advantages of these two paradigms. There are two schemes in this paradigm and simulation results show that there is always one scheme which performs better than either the client/server or the mobile agent paradigms. Thus, the cluster-based hybrid computing provides an energy-efficient and high-performance solution to CSIP.     

Mobile agent‐based computational steering for distributed applications

The mobile agent‐based computational steering (MACS) for distributed applications is presented in this article. In the MACS, a mobile agent platform, Mobile‐C, is embedded in a program through the Mobile‐C library to support C/C++ mobile agent code. Runtime replaceable algorithms of a program are represented as agent services in C/C++ source code and can be replaced with new ones through mobile agents. In the MACS, a mobile agent created and deployed by a user from the steering host migrates to computing hosts successively to replace algorithms of running programs that constitute a distributed application without the need of stopping the execution and recompiling the programs. The methodology of dynamic algorithm alteration in the MACS is described in detail with an example of matrix operation. The Mobile‐C library enables the integration of Mobile‐C into any C/C++ programs to carry out computational steering through mobile agents. The source code level execution of mobile agent code facilitates handling issues such as portability and secure execution of mobile agent code. In the MACS, the network load between the steering and computing hosts can be reduced, and the successive operations of a mobile agent on multiple computing hosts are not affected whether the steering host stays online or not. The employment of the middle‐level language C/C++ enables the MACS to accommodate the diversity of scientific and engineering fields to allow for runtime interaction and steering of distributed applications to match the dynamic requirements imposed by the user or the execution environment. An experiment is used to validate the feasibility of the MACS in real‐world mobile robot applications. The experiment replaces a mobile robot's behavioral algorithm with a mobile agent at runtime. Copyright © 2009 John Wiley & Sons, Ltd.     

HPC‐GAP: engineering a 21st‐century high‐performance computer algebra system

Symbolic computation has underpinned a number of key advances in Mathematics and Computer Science. Applications are typically large and potentially highly parallel, making them good candidates for parallel execution at a variety of scales from multi‐core to high‐performance computing systems. However, much existing work on parallel computing is based around numeric rather than symbolic computations. In particular, symbolic computing presents particular problems in terms of varying granularity and irregular task sizes that do not match conventional approaches to parallelisation. It also presents problems in terms of the structure of the algorithms and data. This paper describes a new implementation of the free open‐source GAP computational algebra system that places parallelism at the heart of the design, dealing with the key scalability and cross‐platform portability problems. We provide three system layers that deal with the three most important classes of hardware: individual shared memory multi‐core nodes, mid‐scale distributed clusters of (multi‐core) nodes and full‐blown high‐performance computing systems, comprising large‐scale tightly connected networks of multi‐core nodes. This requires us to develop new cross‐layer programming abstractions in the form of new domain‐specific skeletons that allow us to seamlessly target different hardware levels. Our results show that, using our approach, we can achieve good scalability and speedups for two realistic exemplars, on high‐performance systems comprising up to 32000 cores, as well as on ubiquitous multi‐core systems and distributed clusters. The work reported here paves the way towards full‐scale exploitation of symbolic computation by high‐performance computing systems, and we demonstrate the potential with two major case studies. © 2016 The Authors. Concurrency and Computation: Practice and Experience Published by John Wiley & Sons Ltd.     

A multi‐GPU algorithm for large‐scale neuronal networks

Large‐scale simulations of parts of the brain using detailed neuronal models to improve our understanding of brain functions are becoming a reality with the usage of supercomputers and large clusters. However, the high acquisition and maintenance cost of these computers, including the physical space, air conditioning, and electrical power, limits the number of simulations of this kind that scientists can perform. Modern commodity graphical cards, based on the CUDA platform, contain graphical processing units (GPUs) composed of hundreds of processors that can simultaneously execute thousands of threads and thus constitute a low‐cost solution for many high‐performance computing applications. In this work, we present a CUDA algorithm that enables the execution, on multiple GPUs, of simulations of large‐scale networks composed of biologically realistic Hodgkin–Huxley neurons. The algorithm represents each neuron as a CUDA thread, which solves the set of coupled differential equations that model each neuron. Communication among neurons located in different GPUs is coordinated by the CPU. We obtained speedups of 40 for the simulation of 200k neurons that received random external input and speedups of 9 for a network with 200k neurons and 20M neuronal connections, in a single computer with two graphic boards with two GPUs each, when compared with a modern quad‐core CPU. Copyright © 2010 John Wiley & Sons, Ltd.     

Integrating mobile agents into the mobile middleware

Mobile agents are a new paradigm for distributed computing that is especially well suited for mobile computing over global wireless networks. This paper describes the approach taken in the ACTS On TheMove project to integrate a mobile agent system into the Mobile Application Support Environment (MASE), a middleware for mobile computing. In this project, an existing mobile agent system was adapted for the requirements of mobile computing. We present the changes that had to be made to the agent system to adapt it to the wireless communication. We also present some of the application areas where a mobile agent system is suitable for mobile communication. We describe an agent based pre-fetcher application where an agent operaes disconnected from the user on the fixed network and prepares web pages for the anticipated next connection of the user using the Quality-of-Service trading functions available in MASE.     

QoS modeling and analysis of component‐based software systems: a stochastic approach

There is a growing demand for using commercial‐off‐the‐shelf (COTS) software components to facilitate the development of software systems. Among many research topics for component‐based software, quality‐of‐service (QoS) evaluation is yet to be given the importance it deserves. In this paper, we propose a novel analytical model to evaluate the QoS of component‐based software systems. We use the component execution graph (CEG) graph model to model the architecture at the process level and the interdependence among components. The CEG graph can explicitly capture sequential, parallel, selective and iterative compositions of components. For QoS estimation, each component in the CEG model is associated with execution rate, failure rate and cost per unit time. Three metrics of the QoS are considered and analytically calculated, namely make‐span, reliability and cost. Through a case study, we show that our model is capable of modeling real‐world COTS software systems effectively. Also, Monte‐Carlo simulation in the case study indicates that analytical results are consistent with simulation and all are covered by 95% confidence intervals. We also present a sensitivity analysis technique to identify QoS bottlenecks. This paper concludes with a comparison with related work. Copyright © 2007 John Wiley & Sons, Ltd.     

A scalable HPF implementation of a finite‐volume computational electromagnetics application on a CRAY T3E parallel system

The time‐dependent Maxwell equations are one of the most important approaches to describing dynamic or wide‐band frequency electromagnetic phenomena. A sequential finite‐volume, characteristic‐based procedure for solving the time‐dependent, three‐dimensional Maxwell equations has been successfully implemented in Fortran before. Due to its need for a large memory space and high demand on CPU time, it is impossible to test the code for a large array. Hence, it is essential to implement the code on a parallel computing system. In this paper, we discuss an efficient and scalable parallelization of the sequential Fortran time‐dependent Maxwell equations solver using High Performance Fortran (HPF). The background to the project, the theory behind the efficiency being achieved, the parallelization methodologies employed and the experimental results obtained on the Cray T3E massively parallel computing system will be described in detail. Experimental runs show that the execution time is reduced drastically through parallel computing. The code is scalable up to 98 processors on the Cray T3E and has a performance similar to that of an MPI implementation. Based on the experimentation carried out in this research, we believe that a high‐level parallel programming language such as HPF is a fast, viable and economical approach to parallelizing many existing sequential codes which exhibit a lot of parallelism. Copyright © 2003 John Wiley & Sons, Ltd.     

A Stealth Integrity Targeted Cyber‐Attack in Distributed Electric Power Networks with Local Model Information

Electric power networks are critical infrastructures, and their correct operation is of vital importance. Nowadays, these systems are prone to cyber‐attacks because of new vulnerabilities in the system and access to shared networks. In this paper, a novel Stealth Integrity Targeted Attack (SITA) is proposed in the context of distributed power systems. A distributed power system comprises several sub‐networks, or zones with dedicated control and monitoring centers. The overall system is represented by linear time invariant state space models with coupled dynamical and algebraic equations. In the proposed strategy, the attacker has access to only one of the sub networks; therefore, the attacker only requires local information about one of the power system zones. Primarily, the proposed attack policy is defined based on zero‐dynamics of the sub network. The intruder injects predesigned signals to both the local generation unit controller as well as local unsecured and controllable loads in the attacked zone. Moreover, the local measurement system, or the sensors of the targeted zone are tampered. Furthermore, it will be proved that although the neighbor zones have physical connections with the attacked zone, the injected adversary signals are designed as they do not impact other zones directly in order to conceal the local attack from neighbor control centers as much as possible. We provide some advice to system administrators to make the intrusion unfeasible or to reveal the attack. The simulations on IEEE‐118 bus test system illustrate the validity of the assertions.     

Wideband model of on‐chip CMOS interconnects using space‐mapping technique

A new wideband model for on‐chip complementary metal–oxide–semiconductor (CMOS) interconnects is developed by virtue of a space‐mapping neural network (SMNN) technique. In this approach, two subneural networks are used for improving the reliability and generalization ability of the model. This approach also presents a new methodology for data generation and training of the two neural networks. Two different structures are used for the two subneural networks to address different physical effects. Instead of the S parameters, the admittances of sub‐block neural networks are used as optimization targets for training so that different physical effects can be addressed individually. This model is capable of featuring frequency‐variant characteristics of radio‐frequency interconnects in terms of frequency‐independent circuit components with two subneural networks. In comparison with results from rigorous electromagnetic (EM) simulations, this SMNN model can achieve good accuracy with an average error less than 2% up to 40 GHz. Moreover, it has much enhanced learning and generalization capabilities and as fast as equivalent circuit while preserves the accuracy of detailed EM simulations. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

A Direct Execution Approach to Simulating Mobile Agent Algorithms

Mobile agent technology has been applied to develop the solutions for various kinds of parallel and distributed computing problems. However, performance evaluation of mobile agent algorithms remains a difficult task, mainly due to the characteristics of mobile agents such as distributed and asynchronous execution, autonomy and mobility. This paper proposes a general approach based on direct execution simulation for evaluating the performance of mobile agent algorithms by collecting and analyzing the information about the agents during their execution. We describe the proposed generic simulation model, named MADES, the architecture of a software environment based on MADES, and a prototype implementation. A mobile agent-based distributed load balancing algorithm has been used for experiments with the prototype.     

Implementation and optimization of GPU‐based parallel one‐step leapfrog ADI‐FDTD for far‐field scattering problems

The one‐step leapfrog alternative‐direction‐implicit finite‐difference time‐domain (ADI‐FDTD), free from the Courant‐Friedrichs‐Lewy (CFL) stability condition and sub‐step computations, is efficient when dealing with fine grid problems. However, solution of the numerous tridiagonal systems still imposes a great computational burden and makes the method hard to execute in parallel. In this paper, we proposed an efficient graphic processing unit (GPU)‐based parallel implementation of the one‐step leapfrog ADI‐FDTD for the far‐field EM scattering simulation of objects, in which we present and analyze the manners of calculation area division and thread allocation and a data layout transformation of z components is proposed to achieve better memory access mode, which is a key factor affecting GPU execution efficiency. The simulation experiment is carried out to verify the accuracy and efficiency of the GPU‐based implementation. The simulation results show that there is a good agreement between the proposed one‐step leapfrog ADI‐FDTD method and Yee's FDTD in solving the far‐field scattering problem and huge benefits in performance were encountered when the method was accelerated using GPU technology.     

Copernicus,a hybrid dataflow and peer-to-peer scientific computing platform for efficient large-scale ensemble sampling

Compute-intensive applications have gradually changed focus from massively parallel supercomputers to capacity as a resource obtained on-demand. This is particularly true for the large-scale adoption of cloud computing and MapReduce in industry, while it has been difficult for traditional high-performance computing (HPC) usage in scientific and engineering computing to exploit this type of resources. However, with the strong trend of increasing parallelism rather than faster processors, a growing number of applications target parallelism already on the algorithm level with loosely coupled approaches based on sampling and ensembles. While these cannot trivially be formulated as MapReduce, they are highly amenable to throughput computing. There are many general and powerful frameworks, but in particular for sampling-based algorithms in scientific computing there are some clear advantages from having a platform and scheduler that are highly aware of the underlying physical problem. Here, we present how these challenges are addressed with combinations of dataflow programming, peer-to-peer techniques and peer-to-peer networks in the Copernicus platform. This allows automation of sampling-focused workflows, task generation, dependency tracking, and not least distributing these to a diverse set of compute resources ranging from supercomputers to clouds and distributed computing (across firewalls and fragile networks). Workflows are defined from modules using existing programs, which makes them reusable without programming requirements. The system achieves resiliency by handling node failures transparently with minimal loss of computing time due to checkpointing, and a single server can manage hundreds of thousands of cores e.g. for computational chemistry applications.     

A holistic approach to decentralized structural damage localization using wireless sensor networks

Wireless sensor networks (WSNs) have become an increasingly compelling platform for Structural Health Monitoring (SHM) applications, since they can be installed relatively inexpensively onto existing infrastructure. Existing approaches to SHM in WSNs typically address computing system issues or structural engineering techniques, but not both in conjunction. In this paper, we propose a holistic approach to SHM that integrates a decentralized computing architecture with the Damage Localization Assurance Criterion algorithm. In contrast to centralized approaches that require transporting large amounts of sensor data to a base station, our system pushes the execution of portions of the damage localization algorithm onto the sensor nodes, reducing communication costs by two orders of magnitude in exchange for moderate additional processing on each sensor. We present a prototype implementation of this system built using the TinyOS operating system running on the Intel Imote2 sensor network platform. Experiments conducted using two different physical structures demonstrate our system’s ability to accurately localize structural damage. We also demonstrate that our decentralized approach reduces latency by 65.5% and energy consumption by 64.0% compared to a typical centralized solution.     

Experiences with component‐oriented technologies in nuclear power plant simulators

This paper proposes the application of modern component‐oriented technologies to the development of nuclear power plant simulators. On the one hand, as a significant improvement on previous simulators, the new kernel is based on the Common Component Architecture (CCA). The use of such a high‐performance computing oriented component technology, together with a novel algorithm to automatically resolve simulation data dependencies, allows the efficient execution of both parallel and sequential simulation models. On the other hand, RT‐CORBA is employed in the development of the rest of the applications that comprise the simulator. This real‐time communication middleware not only makes the management of communications easier, but also provides the applications with real‐time capabilities. Software components used in these two ways, simulation models integrating the kernel and distributed applications from which the simulator is comprised, improve the evolution and maintenance of the entire system, as well as promoting code reusability in other projects. Copyright © 2006 John Wiley & Sons, Ltd.     

基于Java的网格计算框架及其实现

为了提高网格计算的可靠性,适应大规模计算的要求,采用了一种基于Java的分布式网格计算框架,它利用移动部署代理管理计算任务,可以最大限度地减轻中心主机的负担,并通过在Java运行环境中引入两个组件,解决了用Java构建网格计算时存在的安全和资源管理问题,是一种理想的基于Internet的网格应用项目,具有良好的实用性和推广价值。     

基于agent技术的并行构件组装及性能优化方法研究

为更好地组装并行构件程序和进行性能优化工作,设计和使用了不同的软件agent.构件连接agent负责构件接口的粘合和数据重分布.构件执行agent和资源管理agent相互协作,把构件部署在满足要求的计算节点上.定义了4种不同的构件自适应策略.不同的构件自适应agent、构件执行agent和资源管理agent相互合作,针对平台计算资源的不同情况,完成构件的自适应过程,提高了构件的性能.资源管理agent、负载探测agent和构件执行agent相互合作,完成负载均衡工作,提高了整个计算平台的性能和吞吐量.在异构计算机集群上的相关实验证明了所提出的基于agent技术的并行构件组装及性能优化方法的有效性.相比传统的性能优化方法,基于agent技术的方法使用灵活,并且具有性能上的优势.     

A scheduling algorithm for applications in a cloud computing system with communication changes

This paper proposes a scheduling algorithm to solve the problem of task scheduling in a cloud computing system with time‐varying communication conditions. This algorithm converts the scheduling problem with communication changes into a directed acyclic graph (DAG) scheduling problem for existing fuzzy communication task nodes, that is, the scheduling problem for a communication‐change DAG (CC‐DAG). The CC‐DAG contains both computation task nodes and communication task nodes. First, this paper proposes a weighted time‐series network bandwidth model to solve the indefinite processing time (cost) problem for a fuzzy communication task node. This model can accurately predict the processing time of a fuzzy communication task node. Second, to address the scheduling order problem for the computation task nodes, a dynamic pre‐scheduling search strategy (DPSS) is proposed. This strategy computes the essential paths for the pre‐scheduling of the computation task nodes based on the actual computation costs (times) of the computation task nodes and the predicted processing costs (times) of the fuzzy communication task nodes during the scheduling process. The computation task node with the longest essential path is scheduled first because its completion time directly influences the completion time of the task graph. Finally, we demonstrate the proposed algorithm via simulation experiments. The experimental results show that the proposed DPSS produced remarkable performance improvement rate on the total execution time that ranges between 11.5% and 21.2%. In view of the experimental results, the proposed algorithm provides better quality scheduling solution that is suitable for scientific application task execution in the cloud computing environment than HEFT, PEFT, and CEFT algorithms.     

Ontology based web simulation system for hydrodynamic modeling

Hydrodynamic models generally deal with large sets of data and utilize substantial computational resources. Powerful, robust servers with extensive storage capabilities are desirable for rapid execution. Unfortunately, it is not always possible to effort those kinds of facilities whereas a centralized computer system together with a user access interface can be a viable alternative for many clients. The simplest way a client can communicate with the central simulation server is by a web browser because it is available as a pre-installed application on most every computing platform purchased today. This type of environment is called web based simulation or WBS. In this study, the concepts necessary to design and develop a WBS for the simulation of hydrodynamic processes using legacy (FORTRAN) code are introduced. A formal specification of the simulation domain or an ontology has been developed that is the underlying concept to share, retrieve, and move the simulation data between the different components of the WBS. This ontology can also be used for future analysis and reuse of the simulation domain concepts and the associated data sets.