: A distributed real-time logic language

This paper presents a new language that integrates the real-time and distributed paradigms within the framework of a concurrent logic language. Concurrent logic languages (CLLs) are capable of expressing concurrence, communication and nondeterminism in a natural way. That is, the intrinsic parallel semantics of the concurrent logic languages makes them well-suited for distributed programming. The proposed language is particularly suitable for loosely coupled systems and it contains mechanisms for distributed and real-time process control. A new execution model for concurrent logic languages is presented, which enables efficient distributed execution and real-time control. The model is introduced by giving an operational semantics for the language and the new model's implementation is discussed, including the definition of a new abstract machine and its implementation on a network of Unix workstations. Although the sequential core is not optimized, some previous results are discussed, showing the feasibility of the language's execution model for distributed real-time systems. The language is currently being used as the kernel language for a distributed simulation and validation tool for communication protocols.     

: A distributed real-time logic language

This paper presents a new language that integrates the real-time and distributed paradigms within the framework of a concurrent logic language. Concurrent logic languages (CLLs) are capable of expressing concurrence, communication and nondeterminism in a natural way. That is, the intrinsic parallel semantics of the concurrent logic languages makes them well-suited for distributed programming. The proposed language is particularly suitable for loosely coupled systems and it contains mechanisms for distributed and real-time process control. A new execution model for concurrent logic languages is presented, which enables efficient distributed execution and real-time control. The model is introduced by giving an operational semantics for the language and the new model's implementation is discussed, including the definition of a new abstract machine and its implementation on a network of Unix workstations. Although the sequential core is not optimized, some previous results are discussed, showing the feasibility of the language's execution model for distributed real-time systems. The language is currently being used as the kernel language for a distributed simulation and validation tool for communication protocols.     

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.     

Conservative distributed discrete-event simulation on the Amazon EC2 cloud: An evaluation of time synchronization protocol performance and cost efficiency

Distributed execution of simulation models comes into play when memory limitations of a single computational resource prohibit their execution. In addition, the potential for parallel execution of a model on a distributed platform through the integration of multiple computational cores, can potentially reduce the execution time of a simulation. However, such gains can be voided by the overhead that time synchronization protocols for parallel and distributed simulation induce. This overhead is determined by the protocol used, the characteristics of the simulation model, as well as the architectural and performance characteristics of the hardware platform used. Recently, Infrastructure-as-a-Service offerings in the cloud computing domain have introduced flexibility in acquiring access to virtualized hardware platforms on a pay-as-you-go basis. At present, it is however unclear to what extent these offerings are suited for the distributed execution of discrete-event simulations, and how the characteristics of different resource types impact the performance of distributed simulation under different time synchronization protocols. Likewise, it is unclear which type of resources are most cost-efficient for this type of workload. To our knowledge, this paper is the first to investigate these aspects through an assessment of the performance and cost efficiency of different conservative time synchronization protocols on a range of cloud resource types that are currently available on Amazon EC2. Our analysis shows that performance levels comparable to those realized on commodity hardware based-clusters are attainable, and that the relative performance of different synchronization protocols is retained on high-end IaaS resources. In terms of cost-efficiency, we find that IaaS products tailored to traditional cluster workloads do not necessarily constitute the optimal choice, and we assess the impact of different packing configurations for logical processes in this regard.     

New trends in parallel and distributed simulation: From many-cores to Cloud Computing

Recent advances in computing architectures and networking are bringing parallel computing systems to the masses so increasing the number of potential users of these kinds of systems. In particular, two important technological evolutions are happening at the ends of the computing spectrum: at the “small” scale, processors now include an increasing number of independent execution units (cores), at the point that a mere CPU can be considered a parallel shared-memory computer; at the “large” scale, the Cloud Computing paradigm allows applications to scale by offering resources from a large pool on a pay-as-you-go model. Multi-core processors and Clouds both require applications to be suitably modified to take advantage of the features they provide. Despite laying at the extreme of the computing architecture spectrum – multi-core processors being at the small scale, and Clouds being at the large scale – they share an important common trait: both are specific forms of parallel/distributed architectures. As such, they present to the developers well known problems of synchronization, communication, workload distribution, and so on. Is parallel and distributed simulation ready for these challenges? In this paper, we analyze the state of the art of parallel and distributed simulation techniques, and assess their applicability to multi-core architectures or Clouds. It turns out that most of the current approaches exhibit limitations in terms of usability and adaptivity which may hinder their application to these new computing architectures. We propose an adaptive simulation mechanism, based on the multi-agent system paradigm, to partially address some of those limitations. While it is unlikely that a single approach will work well on both settings above, we argue that the proposed adaptive mechanism has useful features which make it attractive both in a multi-core processor and in a Cloud system. These features include the ability to reduce communication costs by migrating simulation components, and the support for adding (or removing) nodes to the execution architecture at runtime. We will also show that, with the help of an additional support layer, parallel and distributed simulations can be executed on top of unreliable resources.     

The performance of a distributed combat simulation with the time warp operating system

This paper analyzes the performance of a discrete-event combat simulation executed on a parallel processor under control of the Time Warp Operating System. Time Warp is in a class of distributed simulation methods called Optimistic methods which have proven to be useful over a wide range of simulations. The combat simulation used for this performance study, called STB88, is a division-corps model incorporating a number of different types of computations. The speed-up for three versions of this model on the Caltech/JPL Mark III Hypercube and the BBN Butterfly parallel processors was measured relative to an efficient sequential execution of the same model on the same hardware. The results indicate that STB88 version 1 achieves a speed-up of 28.6 on 60 Mark III processors, while STB88 version 2 achieves a speed-up of 36.8 on 100 Butterfly processors. Version 3 of STB88 achieved a speed-up of 38.5 on 128 Mark III processors. The versions differed only in their interface to Time Warp. On the Butterfly, the sequential execution completed in 2 hours, while the 100 processor execution completed in 3.2 minutes.     

Asynchronous parallel simulation of parallel programs

Parallel simulation of parallel programs for large datasets has been shown to offer significant reduction in the execution time of many discrete event models. The paper describes the design and implementation of MPI-SIM, a library for the execution driven parallel simulation of task and data parallel programs. MPI-SIM can be used to predict the performance of existing programs written using MPI for message passing, or written in UC, a data parallel language, compiled to use message passing. The simulation models can be executed sequentially or in parallel. Parallel execution of the models are synchronized using a set of asynchronous conservative protocols. The paper demonstrates how protocol performance is improved by the use of application-level, runtime analysis. The analysis targets the communication patterns of the application. We show the application-level analysis for message passing and data parallel languages. We present the validation and performance results for the simulator for a set of applications that include the NAS Parallel Benchmark suite. The application-level optimization described in the paper yielded significant performance improvements in the simulation of parallel programs, and in some cases completely eliminated the synchronizations in the parallel execution of the simulation model     

Agent scheduling model for adaptive dynamic load balancing in agent-based distributed simulations

Agent-based distributed simulations are confronted with load imbalance problem, which significantly affects simulation performance. Dynamic load balancing can be effective in decreasing simulation execution time and improving simulation performance. The characteristics of multi-agent systems and time synchronization mechanisms make the traditional dynamic load balancing approaches not suitable for dynamic load balancing in agent-based distributed simulations. In this paper, an adaptive dynamic load balancing model in agent-based distributed simulations is proposed. Due to the complexity and huge time consuming for solving the model, a distributed approximate optimized scheduling algorithm with partial information (DAOSAPI) is proposed. It integrates the distributed mode, approximate optimization and agent set scheduling approach. Finally, experiments are conducted to verify the efficiency of the proposed algorithm and the simulation performance under dynamic agent scheduling. The experiments indicate that DAOSPI has the advantage of short execution time in large-scale agent scheduling, and the distributed simulation performance under this dynamic agent scheduling outperforms that under static random agent distribution.     

Asynchronous parallel discrete event simulation

Complex models may have model components distributed over a network and generally require significant execution times. The field of parallel and distributed simulation has grown over the past fifteen years to accommodate the need of simulating the complex models using a distributed versus sequential method. In particular, asynchronous parallel discrete event simulation (PDES) has been widely studied, and yet we envision greater acceptance of this methodology as more readers are exposed to PDES introductions that carefully integrate real-world applications. With this in mind, we present two key methodologies (conservative and optimistic) which have been adopted as solutions to PDES systems. We discuss PDES terminology and methodology under the umbrella of the personal communications services application     

Modeling and distributed simulation of a broadband-ISDN network

A distributed approach to communication network simulation using a network of workstations configured as a loosely coupled parallel processor to model and simulate the broadband integrated services digital network (B-ISDN) is proposed. In a loosely coupled parallel processor system, a number of concurrently executable processors communicate asynchronously using explicit messages over high-speed links. Since this architecture is similar to that of B-ISDN networks, it constitutes a realistic testbed for their modeling and simulation. The authors describe an implementation of this approach on 50 Sun workstations at Brown University. Performance results, based on representative B-ISDN networks and realistic traffic models, indicate that the distributed approach is efficient and accurate     

Agent-based modeling and simulation of an autonomic manufacturing execution system

Production management systems must constantly deal with unplanned disruptive events and disturbances such as arrivals of rush orders, raw material shortage/delays or equipment breakdowns along with a multitude of interactions in the supply chain which constantly demand on-line task rescheduling and order execution control. For responsiveness and agility at the shop-floor, a distributed design for manufacturing execution systems is proposed based on autonomic units that fill the gap between production planning and shop-floor control. An interaction mechanism designed around the concept of order and resource agents implementing the monitor-analyze-plan-execution loop is described. Generative simulation modeling of an autonomic manufacturing execution system (@MES) is proposed in order to evaluate emerging behaviors and macroscopic dynamics in a multiproduct batch plant. Results obtained for an industrial case study using a simulation model of the proposed @MES are presented. The usefulness of agent-based modeling and simulation as a tool for distributed MESs design and to verify performance, stability and disturbance rejection capability of an interaction mechanism is highlighted.     

面向异构多核处理器的并行代价模型

现有的并行代价模型大多是面向共享存储或分布存储结构设计的,不完全适合异构多核处理器。为解决这个问题,提出了面向异构多核处理器的并行代价模型,通过定量刻画计算核心运算能力、存储访问延迟和数据传输开销对循环并行执行时间的影响,提高加速并行循环识别的准确性。实验结果表明,提出的并行代价模型能有效识别加速并行循环,将其识别结果作为后端生成并行代码的依据,可有效提高并行程序在异构多核处理器上的性能。     

The impact of task service time variability on gang scheduling performance in a two-cluster system

Gang scheduling is a common task scheduling policy for parallel and distributed systems which combines elements of space-sharing and time-sharing. In this paper we present a migration strategy which reduces the fragmentation in the schedule caused by gang scheduled jobs. We consider the existence of high priority jobs in the workload. These jobs need to be started immediately and they may interrupt a parallel job’s execution. A distributed system consisting of two homogeneous clusters is simulated to evaluate the performance for various workloads. We study the impact on performance of the variability in service time of the parallel tasks. Our simulation results indicate that the proposed strategy can result in a significant performance gain and that the performance improvement depends on the variability of gang tasks’ service time.     

Simulation of MPI applications with time‐independent traces

Analyzing and understanding the performance behavior of parallel applications on parallel computing platforms is a long‐standing concern in the High Performance Computing community. When the targeted platforms are not available, simulation is a reasonable approach to obtain objective performance indicators and explore various hypothetical scenarios. In the context of applications implemented with the Message Passing Interface, two simulation methods have been proposed, on‐line simulation and off‐line simulation, both with their own drawbacks and advantages. In this work, we present an off‐line simulation framework, that is, one that simulates the execution of an application based on event traces obtained from an actual execution. The main novelty of this work, when compared to previously proposed off‐line simulators, is that traces that drive the simulation can be acquired on large, distributed, heterogeneous, and non‐dedicated platforms. As a result, the scalability of trace acquisition is increased, which is achieved by enforcing that traces contain no time‐related information. Moreover, our framework is based on a state‐of‐the‐art scalable, fast, and validated simulation kernel. We introduce the notion of performing off‐line simulation from time‐independent traces, propose and evaluate several trace acquisition strategies, describe our simulation framework, and assess its quality in terms of trace acquisition scalability, simulation accuracy, and simulation time. Copyright © 2014 John Wiley & Sons, Ltd.     

基于模拟系统的并行程序运行时间推算

本文在并行系统模拟环境中，采集了一个迭代类并行程序实例的运行时间数据，据此，分析了影响程序运行时间的主要因素，建立了一个并行程序运行时间推算模型，从而可以在迭代次数，输入数据规模，以及并行系统的配置等三个方向上对程序运行时间进行预测，实验数据表明，该模型是相当精确的，可以为我们节省大量的模拟时间。     

Performance evaluation of fork and join synchronization primitives

Summary The paper presents a performance model of fork and join synchronization primitives. The primitives are used in parallel programs executed on distributed systems. Three variants of the execution of parallel programs with fork and join primitives are considered and queueing models are proposed to evaluate their performance on a finite number of processors. Synchronization delays incurred by the programs are represented by a state-dependent server with service rate depending on a particular synchronization scheme. Closed form results are presented for the two processor case and a numerical method is proposed for many processors. Fork-join queueing networks having more complex structure i.e., processors arranged in series and in parallel, are also analyzed in the same manner. The networks can model the execution of jobs with a general task precedence graph corresponding to a nested structure of the fork-join primitives. Some performance indices of the parallel execution of programs are studied. The results show that the speedup which can be obtained theoretically in a parallel system may be decreased significantly by synchronization constraints.This research we carried out while the author was visiting ISEM, Université de Paris-Sud, France     

Fast multi-core co-simulation of Cyber-Physical Systems: Application to internal combustion engines

The design process of complex Cyber-Physical Systems often relies on co-simulations of the system, involving the interaction of several simulated models of sub-systems. However, reaching real-time simulations is currently prevented by prohibitive CPU times using the single-threaded existing simulation tools. This paper investigates the problem of the efficient parallel co-simulation of hybrid dynamical systems. It introduces a finely-grained co-simulation method enabling numerical integration speed-ups. It is obtained using a partition across the model into loosely coupled sub-systems with sparse communication between modules. The proposed scheme leads to schedule a large number of operations with a wide range of execution times. A suitable off-line scheduling algorithm, based on the input/output dynamics of the models, is proposed to minimize the simulation errors induced by the parallel execution. This scheme is finally tested using the phenomenological model of a combustion engine issued from the Functional Mockup Interface framework. Compared with the sequential case, it shows significant speed-ups while keeping the numerical integration accuracy under control.     

Extending the DEVS formalism for massively parallel simulation

The use of multiprocessors for discrete event simulation is an active research area where work has focused on strategies for model execution with little regard for the underlying formalism in which models may be expressed. However, a formalism-based approach offers several advantages including the ability to migrate models from sequential to parallel platforms and the ability to calibrate simulation architectures to model structural properties. In this article, we extend the DEVS (discrete event system specification) formalism, originally developed for sequential simulation, to accommodate the full potential of parallel processing. The extension facilitates exploitation of both internal and external event parallelism manifested in hierarchical, modular DEVS models. After developing a mapping of the extended formalism to parallel architectures, we describe an implementation of the approach on a massively parallel architecture, the Connection Machine. Execution results are discussed for a class of models exhibiting high external and internal event parallelism, the so-called broadcast models. These verify the tenets of the underlying theory and demonstrate that significant reduction in execution time is possible compared to the same model executed in serial simulation.     

基于MapReduce模型的推测执行优化算法

作为数据中心大规模处理框架,MapReduce集群包含成百上千个节点,多采用推测执行的方法来有效解决并行计算中的掉队任务。针对集群中实时性需求较高并且任务量较小的目标作业,提出基于MapReduce模型的推测执行优化算法,其目的是在满足实时性需求的基础上尽量减少目标作业的完成时间。首先通过分析任务模型和时间模型,引入数学0-1规划模型,求得整体作业的完成时间最小;然后设计可以在多项式复杂度内完成的启发式算法,目的是在可用资源允许的范围内尽量逼近最优值;最后通过大量实验模拟验证算法的执行效果。