The Ariadne's Clew Algorithm

We present a new approach to path planning, called the "Ariadne's clew algorithm". It is designed to find paths in high-dimensional continuous spaces and applies to robots with many degrees of freedom in static, as well as dynamic environments - ones where obstacles may move. The Ariadne's clew algorithm comprises two sub-algorithms, called Search and Explore, applied in an interleaved manner. Explore builds a representation of the accessible space while Search looks for the target. Both are posed as optimization problems. We describe a real implementation of the algorithm to plan paths for a six degrees of freedom arm in a dynamic environment where another six degrees of freedom arm is used as a moving obstacle. Experimental results show that a path is found in about one second without any pre-processing

Performance-based control system design automation via evolutionary computing

This paper develops an evolutionary algorithm (EA) based methodology for computer-aided control system design (CACSD) automation in both the time and frequency domains under performance satisfactions. The approach is automated by efficient evolution from plant step response data, bypassing the system identification or linearization stage as required by conventional designs. Intelligently guided by the evolutionary optimization, control engineers are able to obtain a near-optimal ‘‘off-thecomputer’’ controller by feeding the developed CACSD system with plant I/O data and customer specifications without the need of a differentiable performance index. A speedup of near-linear pipelineability is also observed for the EA parallelism implemented on a network of transputers of Parsytec SuperCluster. Validation results against linear and nonlinear physical plants are convincing, with good closed-loop performance and robustness in the presence of practical constraints and perturbations

Efficient computation of aerodynamic influence coefficients for aeroelastic analysis on a transputer network

Aeroelastic analysis is multi-disciplinary and computationally expensive. Hence, it can greatly benefit from parallel processing. As part of an effort to develop an aeroelastic capability on a distributed memory transputer network, a parallel algorithm for the computation of aerodynamic influence coefficients is implemented on a network of 32 transputers. The aerodynamic influence coefficients are calculated using a 3-D unsteady aerodynamic model and a parallel discretization. Efficiencies up to 85 percent were demonstrated using 32 processors. The effect of subtask ordering, problem size, and network topology are presented. A comparison to results on a shared memory computer indicates that higher speedup is achieved on the distributed memory system

Programming Model to Develop Supercomputer Combinatorial Solvers

© 2017 IEEE. Novel architectures for massively parallel machines offer better scalability and the prospect of achieving linear speedup for sizable problems in many domains. The development of suitable programming models and accompanying software tools for these architectures remains one of the biggest challenges towards exploiting their full potential. We present a multi-layer software abstraction model to develop combinatorial solvers on massively-parallel machines with regular topologies. The model enables different challenges in the design and optimization of combinatorial solvers to be tackled independently (separation of concerns) while permitting problem-specific tuning and cross-layer optimization. In specific, the model decouples the issues of inter-node communication, n ode-level scheduling, problem mapping, mesh-level load balancing and expressing problem logic. We present an implementation of the model and use it to profile a Boolean satisfiability solver on simulated massively-parallel machines with different scales and topologies

Experimental comparison of parameter estimation methods in adaptive robot control

In the literature on adaptive robot control a large variety of parameter estimation methods have been proposed, ranging from tracking-error-driven gradient methods to combined tracking- and prediction-error-driven least-squares type adaptation methods. This paper presents experimental data from a comparative study between these adaptation methods, performed on a two-degrees-of-freedom robot manipulator. Our results show that the prediction error concept is sensitive to unavoidable model uncertainties. We also demonstrate empirically the fast convergence properties of least-squares adaptation relative to gradient approaches. However, in view of the noise sensitivity of the least-squares method, the marginal performance benefits, and the computational burden, we (cautiously) conclude that the tracking-error driven gradient method is preferred for parameter adaptation in robotic applications

An empirical evaluation of techniques for parallel simulation of message passing networks

209 p.[EN]In the field of computer design, simulation is an essential tool to validate and evaluate architectural proposals. Conventional simulation techniques, designed for their use in sequential computers, are too slow if the system to simulate is large or complex. The aim of this work is to search for techniques to accelerate simulations exploiting the parallelism available in current, commercial multicomputers, and to use these techniques to study a model of a message router. This router has been designed to constitute the communication infrastructure of a (hypothetical) massively parallel computer. Three parallel simulation techniques have been considered: synchronous, asynchronous-conservative and asynchronous-optimistic. These algorithms have been implemented in three multicomputers: a transputer-based Supernode, an Intel Paragon and a network of workstations. The influence that factors such as the characteristics of the simulated models, the organization of the simulators and the characteristics of the target multicomputers have in the performance of the simulations has been measured and characterized. It is concluded that optimistic parallel simulation techniques are not suitable for the considered kind of models, although they may provide good performance in other environments. A network of workstations is not the right platform for our experiments, because the communication demands of the parallel simulators surpass the abilities of local area networks—the granularity is too fine. Synchronous and conservative parallel simulation techniques perform very well in the Supernode and in the Paragon, specially if the model to simulate is complex or large—precisely the worst case for traditional, sequential simulators. This way, studies previously considered as unrealizable, due to their exceedingly high computational cost, can be performed in reasonable times. Additionally, the spectrum of possibilities of using multicomputers can be broadened to execute more than numeric applications.[ES]En el ámbito del diseño de computadores, la simulación es una herramienta imprescindible para la validación y evaluación de cualquier propuesta arquitectónica. Las ténicas convencionales de simulación, diseñadas para su utilización en computadores secuenciales, son demasiado lentas si el sistema a simular es grande o complejo. El objetivo de esta tesis es buscar técnicas para acelerar estas simulaciones, aprovechando el paralelismo disponible en multicomputadores comerciales, y usar esas técnicas para el estudio de un modelo de encaminador de mensajes. Este encaminador está diseñado para formar infraestructura de comunicaciones de un hipotético computador masivamente paralelo. En este trabajo se consideran tres técnicas de simulación paralela: síncrona, asíncrona-conservadora y asíncrona-optimista. Estos algoritmos se han implementado en tres multicomputadores: un Supernode basado en Transputers, un Intel Paragon y una red de estaciones de trabajo. Se caracteriza la influencia que tienen en las prestaciones de los simuladores aspectos tales como los parámetros del modelo simulado, la organización del simulador y las características del multicomputador utilizado. Se concluye que las técnicas de simulación paralela optimista no resultan adecuadas para trabajar con el modelo considerado, aunque pueden ofrecer un buen rendimiento en otros entornos. La red de estaciones de trabajo no resulta una plataforma apropiada para estas simulaciones, ya que una red local no reúne condiciones para la ejecución de aplicaciones paralelas de grano fino. Las técnicas de simulación paralela síncrona y conservadora dan muy buenos resultados en el Supernode y en el Paragon, especialmente si el modelo a simular es complejo o grande—precisamente el peor caso para los algoritmos secuenciales. De esta forma, estudios previamente considerados inviables, por ser demasiado costosos computacionalmente, pueden realizarse en tiempos razonables. Además, se amplía el espectro de posibilidades de los multicomputadores, utilizándolos para algo más que aplicaciones numéricas.Este trabajo ha sido parcialmente subvencionado por la Comisión Interministerial de Ciencia y Tecnología, bajo contrato TIC95-037

Solving large scale linear programming

The interior point method (IPM) is now well established as a competitive technique for solving very large scale linear programming problems. The leading variant of the interior point method is the primal dual - predictor corrector algorithm due to Mehrotra. The main computational steps of this algorithm are the repeated calculation and solution of a large sparse positive definite system of equations. We describe an implementation of the predictor corrector IPM algorithm on MasPar, a massively parallel SIMD computer. At the heart of the implemen-tation is a parallel Cholesky factorization algorithm for sparse matrices. Our implementation uses a new scheme of mapping the matrix onto the processor grid of the MasPar, that results in a more efficient Cholesky factorization than previously suggested schemes. The IPM implementation uses the parallel unit of MasPar to speed up the factorization and other computationally intensive parts of the IPM. An impor-tant part of this implementation is the judicious division of data and computation between the front-end computer, that runs the main IPM algorithm, and the par-allel unit. Performanc

Gaussian block algorithms for solving path problems

Path problems are a family of optimization and enumeration problems that reduce to determination or evaluation of paths in a directed graph. In this paper we give a convenient algebraic description of block algorithms for solving path problems. We also develop block versions of two Gaussian algorithms, which are counterparts of the conventional Jordan and escalator method respectively. The correctness of the two considered block algorithms is discussed, and their complexity is analyzed. A parallel implementation of the block Jordan algorithm on a transputer network is presented, and the obtained experimental results are listed

Solving large scale linear programming problems

The interior point method (IPM) is now well established as a computationaly com-petitive scheme for solving very large scale linear programming problems. The leading variant of the IPM is the primal dual predictor corrector algorithm due to Mehrotra. The main computational efforts in this algorithm are the repeated calculation and solution of a large sparse positive definite system of equations. We describe an implementation of this algorithm for vector processors. At the heart of the implementation is a vectorized matrix multiplication and Cholesky factorization for sparse matrices. We identify the parts where vectorization can be beneficial and discuss in details the merits of alternative vectorization techniques. We show that the best way to utilize a vector processor is by exploiting dense computation within the sparse framework and by unrolling loop operations. We further present an extended definition of supernodes, and describe an implementation based on this new approach. We show that although this approach requires more memory it can increase the scope of dense computation substantially with out adding extra operations. Performance results on standard industrial test problems and comparison between an algorithm that utilizes the extended supernodes and one that utilizes standard supernodes are presented and discussed

Parallel process placement

This thesis investigates methods of automatic allocation of processes to available processors in a given network configuration. The research described covers the investigation of various algorithms for optimal process allocation. Among those researched were an algorithm which used a branch and bound technique, an algorithm based on graph theory, and an heuristic algorithm involving cluster analysis. These have been implemented and tested in conjunction with the gathering of performance statistics during program execution, for use in improving subsequent allocations. The system has been implemented on a network of loosely-coupled microcomputers using multi-port serial communication links to simulate a transputer network. The concurrent programming language occam has been implemented, replacing the explicit process allocation constructs with an automatic placement algorithm. This enables the source code to be completely separated from hardware consideration