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In this grant, we have proposed a three-year research effort focused on developing High Performance Computation and Communication (HPCC) methodologies for structural analysis on parallel processors and clusters of workstations, with emphasis on reducing the structural design cycle time. Besides consolidating and further improving the FETI solver technology to address plate and shell structures, we have proposed to tackle the following design related issues: (a) parallel coupling and assembly of independently designed and analyzed three-dimensional substructures with non-matching interfaces, (b) fast and smart parallel re-analysis of a given structure after it has undergone design modifications, (c) parallel evaluation of sensitivity operators (derivatives) for design optimization, and (d) fast parallel analysis of mildly nonlinear structures. While our proposal was accepted, support was provided only for one year.

This report summarizes the progress and results of the first of a three-year study whose goal is the use of fault tolerant distributed and parallel processing techniques to decrease the cost and improve the performance and reliability of large, disposable sonar arrays. In this first phase, tasks have concentrated on the study, design, and analysis of the fundamental components in the design and analysis of parallel and distributed computing architectures and algorithms for fault tolerant sonar arrays as well as models for their baseline counterparts. An interactive investigation of the interdependent areas of topology, architecture, protocol, and algorithms has been conducted. The results of this first phase have helped to identify the optimum candidate network topology, architecture, hardware components, and interface protocols for the system architecture and a set of fundamental decomposition techniques and parallel algorithms for a representative set of time domain and frequency domain beamforming methods.

The first methods for tolerating more than a small number of worst- case faults in commonly-Lised networks such as the butterfly, the mesh of trees, and other hypercubic networks. Previously, work on the fault-tolerance properties of these networks was limited to showing how to overcome a single fault by adding an extra stage of hardware to the networks.

The first methods for tolerating more than a small number of worst- case faults in commonly-Lised networks such as the butterfly, the mesh of trees, and other hypercubic networks. Previously, work on the fault-tolerance properties of these networks was limited to showing how to overcome a single fault by adding an extra stage of hardware to the networks.

The major thrust of our effort was focused on the theory and practice of responsive (fault-tolerant, real-time) computing in parallel and distributed processing environments. New efficient methods of system testing have been developed which shorten a multiprocessor testing time by orders of magnitude and, therefore, can be used at system booting (previous techniques were prohibitively long. A new design framework for responsive computing was designed and is being implemented for validation. This framework for responsive computing was designed and is being implemented for validation. This framework is based on consensus which can be used to provide synchronization, reliable communication, fault diagnosis, checkpointing and even scheduling in multiprocessor environments. We have formalized and quantified the space-time tradeoff for efficient fault recovery. The system model is a graph, and we were especially successful in analysis of meshes and hypercubes. We developed a new method called naturally redundant algorithms which allows efficient implementation of application-specific techniques

The major thrust of our effort was focused on the theory and practice of responsive (fault-tolerant, real-time) computing in parallel and distributed processing environments. New efficient methods of system testing have been developed which shorten a multiprocessor testing time by orders of magnitude and, therefore, can be used at system booting (previous techniques were prohibitively long. A new design framework for responsive computing was designed and is being implemented for validation. This framework for responsive computing was designed and is being implemented for validation. This framework is based on consensus which can be used to provide synchronization, reliable communication, fault diagnosis, checkpointing and even scheduling in multiprocessor environments. We have formalized and quantified the space-time tradeoff for efficient fault recovery. The system model is a graph, and we were especially successful in analysis of meshes and hypercubes. We developed a new method called naturally redundant algorithms which allows efficient implementation of application-specific techniques

The major thrust of our effort was focused on the theory and practice of responsive (fault-tolerant, real-time) computing in parallel and distributed processing environments. New efficient methods of system testing have been developed which shorten a multiprocessor testing time by orders of magnitude and, therefore, can be used at system booting (previous techniques were prohibitively long. A new design framework for responsive computing was designed and is being implemented for validation. This framework for responsive computing was designed and is being implemented for validation. This framework is based on consensus which can be used to provide synchronization, reliable communication, fault diagnosis, checkpointing and even scheduling in multiprocessor environments. We have formalized and quantified the space-time tradeoff for efficient fault recovery. The system model is a graph, and we were especially successful in analysis of meshes and hypercubes. We developed a new method called naturally redundant algorithms which allows efficient implementation of application-specific techniques

Includes bibliographical references and index

The problem of optimally assigning the modules of a parallel program over the processors of a multiple computer system is addressed. A Sum-Bottleneck path algorithm is developed that permits the efficient solution of many variants of this problem under some constraints on the structure of the partitions. In particular, the following problems are solved optimally for a single-host, multiple satellite system: partitioning multiple chain structured parallel programs, multiple arbitrarily structured serial programs and single tree structured parallel programs. In addition, the problems of partitioning chain structured parallel programs across chain connected systems and across shared memory (or shared bus) systems are also solved under certain constraints. All solutions for parallel programs are equally applicable to pipelined programs. These results extend prior research in this area by explicitly taking concurrency into account and permit the efficient utilization of multiple computer architectures for a wide range of problems of practical interest.

Trimester 2 2016/2017

The research efforts reported here have centered in the areas of parallel and distributed computing, network architecture, combinatorial algorithms, and complexity theory. Significant progress has been made on the development of efficient sorting circuits, network management protocols for high-speed networks, distributed graph algorithms and data structures, improved algorithms for packet routing and sorting in parallel machines, algorithms for reconfiguring networks around faults, improved approximation algorithms for a variety of NP-hard optimization problems, VLSI design, and algorithms for combinatorial problems such as multicommodity flow.

The research efforts reported here have centered in the areas of parallel and distributed computing, network architecture, combinatorial algorithms, and complexity theory. Significant progress has been made on the development of efficient sorting circuits, network management protocols for high-speed networks, distributed graph algorithms and data structures, improved algorithms for packet routing and sorting in parallel machines, algorithms for reconfiguring networks around faults, improved approximation algorithms for a variety of NP-hard optimization problems, VLSI design, and algorithms for combinatorial problems such as multicommodity flow.

The research efforts reported here have centered in the areas of parallel and distributed computing, network architecture, combinatorial algorithms, and complexity theory. Significant progress has been made on the development of efficient sorting circuits, network management protocols for high-speed networks, distributed graph algorithms and data structures, improved algorithms for packet routing and sorting in parallel machines, algorithms for reconfiguring networks around faults, improved approximation algorithms for a variety of NP-hard optimization problems, VLSI design, and algorithms for combinatorial problems such as multicommodity flow.

The research efforts reported here have centered in the areas of parallel and distributed computing, network architecture, combinatorial algorithms, and complexity theory. Significant progress has been made on the development of efficient sorting circuits, network management protocols for high-speed networks, distributed graph algorithms and data structures, improved algorithms for packet routing and sorting in parallel machines, algorithms for reconfiguring networks around faults, improved approximation algorithms for a variety of NP-hard optimization problems, VLSI design, and algorithms for combinatorial problems such as multicommodity flow.

The research efforts reported here have centered in the areas of parallel and distributed computing, network architecture, combinatorial algorithms, and complexity theory. Significant progress has been made on the development of efficient sorting circuits, network management protocols for high-speed networks, distributed graph algorithms and data structures, improved algorithms for packet routing and sorting in parallel machines, algorithms for reconfiguring networks around faults, improved approximation algorithms for a variety of NP-hard optimization problems, VLSI design, and algorithms for combinatorial problems such as multicommodity flow.

788 p. ; 27 cm

The demand for very high speed data processing coupled with falling hardware costs has made large-scale parallel and distributed computer systems both desirable and feasible. Two modes of parallel processing are single instruction stream-multiple data stream (SIMD) and multiple instruction stream - multiple data stream (MIMD). PASM, a partitionable SIMD/MIMD system, is a reconfigurable multimicroprocessor system being designed for image processing and pattern recognition. An important component of these systems is the interconnection network, the mechanism for communication among the computation nodes and memories. Assuring high reliability for such complex systems is a significant task. Thus, a crucial practical aspect of an interconnection network is fault tolerance. In answer to this need, the Extra Stage Cube (ESC), a fault-tolerant, multistage cube-type interconnection network, is defined. The fault tolerance of the ESC is explored for both single and multiple faults, routing tags are defined, and consideration is given to permuting data and partitioning the ESC in the presence of faults. The ESC is compared with other fault-tolerant multistage networks. Finally, reliability of the ESC and an enhanced version of it are investigated. Keywords: Theses.

Trimester 2 2017/2018

This grant has been used to support several graduate students and researchers in the area of modeling and simulation. The final report lists these individuals and summarizes their research. Keywords: Robotics, Motion planning, Solid modeling systems, Algorithms, Modeling, and simulation.

Prediction and computer control of aeroelastic phenomena is a complex multi-disciplinary problem due to the interaction of aerodynamic, elastic and inertia forces on the aircraft structure. Lack of integration among computer programs that serve different disciplines, such as aerodynamics and structures, has posed a major obstacle to accurate aeroelastic analysis. The Multi-Disciplinary Computing Environment (MDICE) has been developed to help solve this problem. MDICE enables engineering analysis codes to perform coupled multi-disciplinary analysis in a distributed computing environment. A unique feature is that existing engineering analysis codes are being used with a high level of interoperability and interchangeability.

Prediction and computer control of aeroelastic phenomena is a complex multi-disciplinary problem due to the interaction of aerodynamic, elastic and inertia forces on the aircraft structure. Lack of integration among computer programs that serve different disciplines, such as aerodynamics and structures, has posed a major obstacle to accurate aeroelastic analysis. The Multi-Disciplinary Computing Environment (MDICE) has been developed to help solve this problem. MDICE enables engineering analysis codes to perform coupled multi-disciplinary analysis in a distributed computing environment. A unique feature is that existing engineering analysis codes are being used with a high level of interoperability and interchangeability.

As the capabilities of computing machinery grow, so does the diverse variety of their applications. The feasibility of many approaches to these applications depends solely upon the existence of computing machinery capable of performing these tasks within a given time constraint. Because the majority of the available computing machinery is general purpose in nature, tasks that do not require purpose facilities, but that do require high throughput, are condemned to execution on expensive general purpose hardware. This research describes several tasks that require fast computing machinery. These tasks do not require general purpose facilities in the sense that the computing machinery used will only perform a fixed set of tasks. Some of the tasks are simple in nature, but are required to execute on very large data sets. Other tasks are computationally intensive in addition to possibly involving large data sets. Both simple and complex algorithms are considered. The discussion includes a description of the tasks. All of the above tasks are useful; however, their value is determined in part by the time required to perform them. This work discusses three architectures for performing remote sensing tasks. These architectures can execute the described tasks more quickly than conventionally available hardware.

A workshop on the complexity of parallel and distributed computation in May 1986. It has 21 speakers and 141 participants; their interests ranged from practical questions about the architecture of parallel and distributed systems to highly theoretical questions about the complexity of parallel computation. A panel discussion was held on the topic 'Bridging the Gap Between the Theory and Practice of Parallel and Distributed Computing'. The main focus of the workshop was a set of mathematical and algorithmic issues that underlie the efficient use of the massively parallel computers that are just beginning to come into use. Several of the lectures were concerned with efficient algorithms for such computers. Other lectures were concerned with the problems of synchronization, load-sharing, and communication between processors in such systems. A third major theme was the reliable operation of such systems in the presence of faulty processors.

A workshop on the complexity of parallel and distributed computation in May 1986. It has 21 speakers and 141 participants; their interests ranged from practical questions about the architecture of parallel and distributed systems to highly theoretical questions about the complexity of parallel computation. A panel discussion was held on the topic 'Bridging the Gap Between the Theory and Practice of Parallel and Distributed Computing'. The main focus of the workshop was a set of mathematical and algorithmic issues that underlie the efficient use of the massively parallel computers that are just beginning to come into use. Several of the lectures were concerned with efficient algorithms for such computers. Other lectures were concerned with the problems of synchronization, load-sharing, and communication between processors in such systems. A third major theme was the reliable operation of such systems in the presence of faulty processors.

This report summarizes the progress and results of the second of a three-year study whose goal is the use of fault-tolerant distributed and parallel processing techniques to decrease the cost and improve the performance and reliability of large, disposable sonar arrays. In this second phase, tasks have concentrated on the design, development, analysis and evaluation of conventional and basic split-aperture beamforming algorithms. A broad assortment of parallel algorithms and programs for FFT beamformers have been completed, and their performance evaluated via a cluster testbed and via rapid virtual prototyping capabilities derived from new network architecture models. These models include a wide variety of network protocols centered around unidirectional, ring, and bidirectional topologies. In addition, new emphasis has begun with split-aperture conventional beamforming and initial results indicate a significant potential for performance improvement through parallel processing. Finally, the architecture for the hardware prototype has been developed, and work has begun on its construction and that of its software system.

Trimester 1 2016/2017

Trimester 1 2016/2017

Trimester 1 2016/2017

The Berkeley Mathematical Sciences Research Institute (MSRI) featured a program in computational complexity during 1985-86. A substantial part of the program was devoted to parallel and distributed computing. Support for this part of the program was obtained from the present Army contract and a similar grant from the Air Force. Personnel supported on this grant were Leslie Valiant of Harvard University and postdoctoral fellows David Shmoys and Umesh Vazirani. A workshop on parallel and distributed computing was held from May 19 to May 23, 1986 and drew 141 participants. Keywords: Mathematical programming; Protocols; Randomized algorithms. (Author)

The Berkeley Mathematical Sciences Research Institute (MSRI) featured a program in computational complexity during 1985-86. A substantial part of the program was devoted to parallel and distributed computing. Support for this part of the program was obtained from the present Army contract and a similar grant from the Air Force. Personnel supported on this grant were Leslie Valiant of Harvard University and postdoctoral fellows David Shmoys and Umesh Vazirani. A workshop on parallel and distributed computing was held from May 19 to May 23, 1986 and drew 141 participants. Keywords: Mathematical programming; Protocols; Randomized algorithms. (Author)

The Berkeley Mathematical Sciences Research Institute (MSRI) featured a program in computational complexity during 1985-86. A substantial part of the program was devoted to parallel and distributed computing. Support for this part of the program was obtained from the present Army contract and a similar grant from the Air Force. Personnel supported on this grant were Leslie Valiant of Harvard University and postdoctoral fellows David Shmoys and Umesh Vazirani. A workshop on parallel and distributed computing was held from May 19 to May 23, 1986 and drew 141 participants. Keywords: Mathematical programming; Protocols; Randomized algorithms. (Author)

The Berkeley Mathematical Sciences Research Institute (MSRI) featured a program in computational complexity during 1985-86. A substantial part of the program was devoted to parallel and distributed computing. Support for this part of the program was obtained from the present Army contract and a similar grant from the Air Force. Personnel supported on this grant were Leslie Valiant of Harvard University and postdoctoral fellows David Shmoys and Umesh Vazirani. A workshop on parallel and distributed computing was held from May 19 to May 23, 1986 and drew 141 participants. Keywords: Mathematical programming; Protocols; Randomized algorithms. (Author)

This report summarizes the progress and results of the third year of a three-year study whose goal is the use of fault-tolerant distributed and parallel processing techniques to decrease the cost and improve the performance and reliability of large, battery-powered, autonomous sonar arrays. In FY98, work has been completed for single-aperture conventional parallel beamforming (CBF), recently completed for split-aperture parallel beamforming (SA-CBF), and initiated for more advanced adaptive or optimal processing algorithms. A comprehensive set of performance experiments has been conducted with the new CBF and SA-CBF parallel algorithms on both clustered workstation testbeds and simulated array architectures, and the results have illuminated the strengths and weaknesses of the algorithms. Several of the new CBF and SA-CBF parallel algorithms developed in this project have been shown to have promising performance and scalability characteristics in terms of processing and memory capacity. In addition, to support sensitivity studies for the mapping of parallel algorithms to array architectures, a new rapid virtual prototyping tool for the design and analysis of distributed parallel sonar arrays has been completed and demonstrated. Finally, an 8-node distributed parallel array prototype has been designed, developed, fabricated, and tested, and promising preliminary results have been attained.

This report summarizes the progress and results of the third year of a three-year study whose goal is the use of fault-tolerant distributed and parallel processing techniques to decrease the cost and improve the performance and reliability of large, battery-powered, autonomous sonar arrays. In FY98, work has been completed for single-aperture conventional parallel beamforming (CBF), recently completed for split-aperture parallel beamforming (SA-CBF), and initiated for more advanced adaptive or optimal processing algorithms. A comprehensive set of performance experiments has been conducted with the new CBF and SA-CBF parallel algorithms on both clustered workstation testbeds and simulated array architectures, and the results have illuminated the strengths and weaknesses of the algorithms. Several of the new CBF and SA-CBF parallel algorithms developed in this project have been shown to have promising performance and scalability characteristics in terms of processing and memory capacity. In addition, to support sensitivity studies for the mapping of parallel algorithms to array architectures, a new rapid virtual prototyping tool for the design and analysis of distributed parallel sonar arrays has been completed and demonstrated. Finally, an 8-node distributed parallel array prototype has been designed, developed, fabricated, and tested, and promising preliminary results have been attained.

xviii,292p. ; 23 cm

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. 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.