Communication and control in an integrated manufacturing system

Typically, components in a manufacturing system are all centrally controlled. Due to possible communication bottlenecking, unreliability, and inflexibility caused by using a centralized controller, a new concept of system integration called an Integrated Multi-Robot System (IMRS) was developed. The IMRS can be viewed as a distributed real time system. Some of the current research issues being examined to extend the framework of the IMRS to meet its performance goals are presented. These issues include the use of communication coprocessors to enhance performance, the distribution of tasks and the methods of providing fault tolerance in the IMRS. An application example of real time collision detection, as it relates to the IMRS concept, is also presented and discussed

Parallel processing for scientific computations

The main contribution of the effort in the last two years is the introduction of the MOPPS system. After doing extensive literature search, we introduced the system which is described next. MOPPS employs a new solution to the problem of managing programs which solve scientific and engineering applications on a distributed processing environment. Autonomous computers cooperate efficiently in solving large scientific problems with this solution. MOPPS has the advantage of not assuming the presence of any particular network topology or configuration, computer architecture, or operating system. It imposes little overhead on network and processor resources while efficiently managing programs concurrently. The core of MOPPS is an intelligent program manager that builds a knowledge base of the execution performance of the parallel programs it is managing under various conditions. The manager applies this knowledge to improve the performance of future runs. The program manager learns from experience

A Domain Specific Approach to High Performance Heterogeneous Computing

Users of heterogeneous computing systems face two problems: firstly, in understanding the trade-off relationships between the observable characteristics of their applications, such as latency and quality of the result, and secondly, how to exploit knowledge of these characteristics to allocate work to distributed computing platforms efficiently. A domain specific approach addresses both of these problems. By considering a subset of operations or functions, models of the observable characteristics or domain metrics may be formulated in advance, and populated at run-time for task instances. These metric models can then be used to express the allocation of work as a constrained integer program, which can be solved using heuristics, machine learning or Mixed Integer Linear Programming (MILP) frameworks. These claims are illustrated using the example domain of derivatives pricing in computational finance, with the domain metrics of workload latency or makespan and pricing accuracy. For a large, varied workload of 128 Black-Scholes and Heston model-based option pricing tasks, running upon a diverse array of 16 Multicore CPUs, GPUs and FPGAs platforms, predictions made by models of both the makespan and accuracy are generally within 10% of the run-time performance. When these models are used as inputs to machine learning and MILP-based workload allocation approaches, a latency improvement of up to 24 and 270 times over the heuristic approach is seen.Comment: 14 pages, preprint draft, minor revisio

A support for remote process execution in a load-balanced distributed system

Load distribution and balancing in a workstation-based network includes a number of intricate tasks. Among them, transparent remote process execution is an essential one. This work describes the main problems to be considered when implementing remote process execution and propose a design for an alternative system attempting to solve these problems.Eje: Sistemas distribuidosRed de Universidades con Carreras en Informática (RedUNCI

Parallel discrete event simulation: A shared memory approach

With traditional event list techniques, evaluating a detailed discrete event simulation model can often require hours or even days of computation time. Parallel simulation mimics the interacting servers and queues of a real system by assigning each simulated entity to a processor. By eliminating the event list and maintaining only sufficient synchronization to insure causality, parallel simulation can potentially provide speedups that are linear in the number of processors. A set of shared memory experiments is presented using the Chandy-Misra distributed simulation algorithm to simulate networks of queues. Parameters include queueing network topology and routing probabilities, number of processors, and assignment of network nodes to processors. These experiments show that Chandy-Misra distributed simulation is a questionable alternative to sequential simulation of most queueing network models

Parallel Load Balancing Strategies for Ensembles of Stochastic Biochemical Simulations

The evolution of biochemical systems where some chemical species are present with only a small number of molecules, is strongly influenced by discrete and stochastic effects that cannot be accurately captured by continuous and deterministic models. The budding yeast cell cycle provides an excellent example of the need to account for stochastic effects in biochemical reactions. To obtain statistics of the cell cycle progression, a stochastic simulation algorithm must be run thousands of times with different initial conditions and parameter values. In order to manage the computational expense involved, the large ensemble of runs needs to be executed in parallel. The CPU time for each individual task is unknown before execution, so a simple strategy of assigning an equal number of tasks per processor can lead to considerable work imbalances and loss of parallel efficiency. Moreover, deterministic analysis approaches are ill suited for assessing the effectiveness of load balancing algorithms in this context. Biological models often require stochastic simulation. Since generating an ensemble of simulation results is computationally intensive, it is important to make efficient use of computer resources. This paper presents a new probabilistic framework to analyze the performance of dynamic load balancing algorithms when applied to large ensembles of stochastic biochemical simulations. Two particular load balancing strategies (point-to-point and all-redistribution) are discussed in detail. Simulation results with a stochastic budding yeast cell cycle model confirm the theoretical analysis. While this work is motivated by cell cycle modeling, the proposed analysis framework is general and can be directly applied to any ensemble simulation of biological systems where many tasks are mapped onto each processor, and where the individual compute times vary considerably among tasks

An efficient adaptative predictive load balancing method for distributed systems

When allocating processors to processes in a distributed system, load balancing is a main concern of designers. By its implementation, system performance can be enhanced by equally distributing the dynamically changing workload and consequently user expectation are improved through an additional reduction on mean response time. In this way, through process migration, a rational and equitable use of the system computational power is achieved, preventing degradation of system performance due to unbalanced work of processors. This article presents an Adaptative Predictive Load Balancing Strategy (APLBS), a variation of Predictive Load Balancing Strategy (PLBS) reported elsewhere [1]. As PLBS, APLBS is a sender initiated, prediction-based strategy for load balancing. The predictive approach is based on estimates given by a weighted exponential average [12] of the load condition of each node in the system. The new approach tries to minimise traffic en the network selecting the most suitable subset of candidates to request migration and the novel aspect is that the size of this subset is adaptative with respect to the system workload. APLBS was contrasted against Random (R), PLBS and Flexible Load Sharing (FLS) [7] strategies on diverse scenarios where the load can be characterised as static or dynamic. A comparative analysis of mean response time, acceptance hit ratio and number of migration failures under each strategy is reported.Sistemas Distribuidos - Redes ConcurrenciaRed de Universidades con Carreras en Informática (RedUNCI

Statistical methodologies for the control of dynamic remapping

Following an initial mapping of a problem onto a multiprocessor machine or computer network, system performance often deteriorates with time. In order to maintain high performance, it may be necessary to remap the problem. The decision to remap must take into account measurements of performance deterioration, the cost of remapping, and the estimated benefits achieved by remapping. We examine the tradeoff between the costs and the benefits of remapping two qualitatively different kinds of problems. One problem assumes that performance deteriorates gradually, the other assumes that performance deteriorates suddenly. We consider a variety of policies for governing when to remap. In order to evaluate these policies, statistical models of problem behaviors are developed. Simulation results are presented which compare simple policies with computationally expensive optimal decision policies; these results demonstrate that for each problem type, the proposed simple policies are effective and robust

Towards a predictive load balancing method based on multiples resources

Processors load unbalance in distributed systems is one of the main problems, because ít involves system performance degradation. Load balance algorithms try to improve the system global performance through migration of processes, but they present also an additional problem, known as instability: lt happens when processes spend an excessive amount of time migrating among different system nodes. In arder to diminish this cost without affecting the mean system response time, load balancíng algoríthms based on dífferent strategíes have been proposed. Multiple Resources Predictíve Load Balance Strategy (MRPLBS), ís a new predíctive, dynamic and nonpreemptive strategy for balancing multiple resources. The predictive approach is based on estimations computed as weighed exponential averages of the load of each node in the system. This paper presents MRPLBS' system architecture and its performance and system a comparison on different scenarios against Random Load Balancing. The number of requirements, the mean response time, the number of failed migratíons and the percentage of acceptance are shownI Workshop de Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI