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Matrix Distributed Processing (MDP) is a C++ library for fast development of efficient parallel algorithms. It constitues the core of FermiQCD. MDP enables programmers to focus on algorithms, while parallelization is dealt with automatically and transparently. Here we present a brief overview of MDP and examples of applications in Computer Science (Cellular Automata), Engineering (PDE Solver) and Physics (Ising Model).

Matrix Distributed Processing (MDP) is a C++ library for fast development of efficient parallel algorithms. It constitues the core of FermiQCD. MDP enables programmers to focus on algorithms, while parallelization is dealt with automatically and transparently. Here we present a brief overview of MDP and examples of applications in Computer Science (Cellular Automata), Engineering (PDE Solver) and Physics (Ising Model).

We present a set of programming tools (classes and functions written in C++ and based on Message Passing Interface) for fast development of generic parallel (and non-parallel) lattice simulations. They are collectively called MDP 1.2. These programming tools include classes and algorithms for matrices, random number generators, distributed lattices (with arbitrary topology), fields and parallel iterations. No previous knowledge of MPI is required in order to use them. Some applications in electromagnetism, electronics, condensed matter and lattice QCD are presented.

The concept of using Parallel Distributed Processing (PDP) to enhance automated experiment monitoring and control is explored. Recent very large scale integration (VLSI) advances have made such applications an achievable goal. The PDP machine has demonstrated the ability to automatically organize stored information, handle unfamiliar and contradictory input data and perform the actions necessary. The PDP machine has demonstrated that it can perform inference and knowledge operations with greater speed and flexibility and at lower cost than traditional architectures. In applications where the rule set governing an expert system's decisions is difficult to formulate, PDP can be used to extract rules by associating the information an expert receives with the actions taken.

The concept of using Parallel Distributed Processing (PDP) to enhance automated experiment monitoring and control is explored. Recent very large scale integration (VLSI) advances have made such applications an achievable goal. The PDP machine has demonstrated the ability to automatically organize stored information, handle unfamiliar and contradictory input data and perform the actions necessary. The PDP machine has demonstrated that it can perform inference and knowledge operations with greater speed and flexibility and at lower cost than traditional architectures. In applications where the rule set governing an expert system's decisions is difficult to formulate, PDP can be used to extract rules by associating the information an expert receives with the actions taken.

The concept of using Parallel Distributed Processing (PDP) to enhance automated experiment monitoring and control is explored. Recent very large scale integration (VLSI) advances have made such applications an achievable goal. The PDP machine has demonstrated the ability to automatically organize stored information, handle unfamiliar and contradictory input data and perform the actions necessary. The PDP machine has demonstrated that it can perform inference and knowledge operations with greater speed and flexibility and at lower cost than traditional architectures. In applications where the rule set governing an expert system's decisions is difficult to formulate, PDP can be used to extract rules by associating the information an expert receives with the actions taken.

Talk by Marcin Cieslik, University of Virginia. Presented at the Python for Scientific Computing Conference (Scipy 2009) held August 18-23, 2009 at CalTech in Pasadena CA. Information about the conference is at: http://conference.scipy.org

Talk by Marcin Cieslik, University of Virginia. Presented at the Python for Scientific Computing Conference (Scipy 2009) held August 18-23, 2009 at CalTech in Pasadena CA. Information about the conference is at: http://conference.scipy.org

Nowadays, the big data marketplace is rising rapidly. The big challenge is finding a system that can store and handle a huge size of data and then processing that huge data for mining the hidden knowledge. This paper proposed a compr ehensive system that is used for improving big data analysis performance. It contains a fast big data processing engine using Apache Spark and a big data storage environment using Apache Hadoop. The system tests about 11 Gigabytes of text data which are co llected from multiple sources for sentiment analysis. Three different machine learning (ML) algorithms are used in this system which is already supported by the Spark ML package. The system programs were written in Java and Scala programming languages and the constructed model consists of the classification algorithms as well as the pre - processing steps in a figure of ML pipeline . The proposed system was implemented in both central and distributed data processing. Moreover, some datasets manipulation manner s have been applied in the system tests to check which manner provides the best accuracy and time performance. The results showed that the system works efficiently for treating big data, it gains excellent accuracy with fast execution time especially in th e distributed data nodes.

Nowadays, the big data marketplace is rising rapidly. The big challenge is finding a system that can store and handle a huge size of data and then processing that huge data for mining the hidden knowledge. This paper proposed a compr ehensive system that is used for improving big data analysis performance. It contains a fast big data processing engine using Apache Spark and a big data storage environment using Apache Hadoop. The system tests about 11 Gigabytes of text data which are co llected from multiple sources for sentiment analysis. Three different machine learning (ML) algorithms are used in this system which is already supported by the Spark ML package. The system programs were written in Java and Scala programming languages and the constructed model consists of the classification algorithms as well as the pre - processing steps in a figure of ML pipeline . The proposed system was implemented in both central and distributed data processing. Moreover, some datasets manipulation manner s have been applied in the system tests to check which manner provides the best accuracy and time performance. The results showed that the system works efficiently for treating big data, it gains excellent accuracy with fast execution time especially in th e distributed data nodes.

Bell System Technical Journal, 62: 10. December 1983 pp 3107-3137. Parallel Fault Simulation Using Distributed Processing . (Levendel, Y.H.; Menon, P.R.; Patel, S.H.)

Bell System Technical Journal, 62: 10. December 1983 pp 3107-3137. Parallel Fault Simulation Using Distributed Processing . (Levendel, Y.H.; Menon, P.R.; Patel, S.H.)

Bell System Technical Journal, 62: 10. December 1983 pp 3107-3137. Parallel Fault Simulation Using Distributed Processing . (Levendel, Y.H.; Menon, P.R.; Patel, S.H.)

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.

Research in the area of distributed computing systems for digital signal processing applications is described. The work involves the modeling of asynchronous parallel processes and computer systems for executing these processes. The objective of the work is to develop techniques by which the compatibility of an architecture and an algorithm can be evaluated. The three part effort addresses: 1. Modeling of asynchronous parallel computer system architectures; 2. Modeling of asynchronous parallel computational processes; 3. Evaluation of alternative architectures relative to classes of computational the approach to the modeling of parallel processes and architectures is to examined the parallelism in a variety of one- and two-dimensional signal processing tasks. This includes a study of the ways in which different types of digital signal processing tasks can be executed on different types of architectures. The goal is to develop one set of features by which processes can be characterized, and another set of features by which parallel architectures can be characterized: and to use these features to obtain measures for the evaluation of process/architecture compatibility. This research will contribute to the understanding both of how distributed computer systems can be designed for the execution of a class of tasks, and of how signal processing tasks can be decomposed for execution on a distributed computing system. (Author)

Research in the area of distributed computing systems for digital signal processing applications is described. The work involves the modeling of asynchronous parallel processes and computer systems for executing these processes. The objective of the work is to develop techniques by which the compatibility of an architecture and an algorithm can be evaluated. The three part effort addresses: 1. Modeling of asynchronous parallel computer system architectures; 2. Modeling of asynchronous parallel computational processes; 3. Evaluation of alternative architectures relative to classes of computational the approach to the modeling of parallel processes and architectures is to examined the parallelism in a variety of one- and two-dimensional signal processing tasks. This includes a study of the ways in which different types of digital signal processing tasks can be executed on different types of architectures. The goal is to develop one set of features by which processes can be characterized, and another set of features by which parallel architectures can be characterized: and to use these features to obtain measures for the evaluation of process/architecture compatibility. This research will contribute to the understanding both of how distributed computer systems can be designed for the execution of a class of tasks, and of how signal processing tasks can be decomposed for execution on a distributed computing system. (Author)

Research in the area of distributed computing systems for digital signal processing applications is described. The work involves the modeling of asynchronous parallel processes and computer systems for executing these processes. The objective of the work is to develop techniques by which the compatibility of an architecture and an algorithm can be evaluated. The three part effort addresses: 1. Modeling of asynchronous parallel computer system architectures; 2. Modeling of asynchronous parallel computational processes; 3. Evaluation of alternative architectures relative to classes of computational the approach to the modeling of parallel processes and architectures is to examined the parallelism in a variety of one- and two-dimensional signal processing tasks. This includes a study of the ways in which different types of digital signal processing tasks can be executed on different types of architectures. The goal is to develop one set of features by which processes can be characterized, and another set of features by which parallel architectures can be characterized: and to use these features to obtain measures for the evaluation of process/architecture compatibility. This research will contribute to the understanding both of how distributed computer systems can be designed for the execution of a class of tasks, and of how signal processing tasks can be decomposed for execution on a distributed computing system. (Author)

Multidisciplinary design optimization (MDO) for large-scale engineering problems poses many challenges (e.g., the design of an efficient concurrent paradigm for global optimization based on disciplinary analyses, expensive computations over vast data sets, etc.) This work focuses on the application of distributed schemes for massively parallel architectures to MDO problems, as a tool for reducing computation time and solving larger problems. The specific problem considered here is configuration optimization of a high speed civil transport (HSCT), and the efficient parallelization of the embedded paradigm for reasonable design space identification. Two distributed dynamic load balancing techniques (random polling and global round robin with message combining) and two necessary termination detection schemes (global task count and token passing) were implemented and evaluated in terms of effectiveness and scalability to large problem sizes and a thousand processors. The effect of certain parameters on execution time was also inspected. Empirical results demonstrated stable performance and effectiveness for all schemes, and the parametric study showed that the selected algorithmic parameters have a negligible effect on performance.

The concept of using Parallel Distributed Processing (PDP) to enhance automated experiment monitoring and control is explored. Recent very large scale integration (VLSI) advances have made such applications an achievable goal. The PDP machine has demonstrated the ability to automatically organize stored information, handle unfamiliar and contradictory input data and perform the actions necessary. The PDP machine has demonstrated that it can perform inference and knowledge operations with greater speed and flexibility and at lower cost than traditional architectures. In applications where the rule set governing an expert system's decisions is difficult to formulate, PDP can be used to extract rules by associating the information an expert receives with the actions taken.

The concept of using Parallel Distributed Processing (PDP) to enhance automated experiment monitoring and control is explored. Recent very large scale integration (VLSI) advances have made such applications an achievable goal. The PDP machine has demonstrated the ability to automatically organize stored information, handle unfamiliar and contradictory input data and perform the actions necessary. The PDP machine has demonstrated that it can perform inference and knowledge operations with greater speed and flexibility and at lower cost than traditional architectures. In applications where the rule set governing an expert system's decisions is difficult to formulate, PDP can be used to extract rules by associating the information an expert receives with the actions taken.

The concept of using Parallel Distributed Processing (PDP) to enhance automated experiment monitoring and control is explored. Recent very large scale integration (VLSI) advances have made such applications an achievable goal. The PDP machine has demonstrated the ability to automatically organize stored information, handle unfamiliar and contradictory input data and perform the actions necessary. The PDP machine has demonstrated that it can perform inference and knowledge operations with greater speed and flexibility and at lower cost than traditional architectures. In applications where the rule set governing an expert system's decisions is difficult to formulate, PDP can be used to extract rules by associating the information an expert receives with the actions taken.

The concept of using Parallel Distributed Processing (PDP) to enhance automated experiment monitoring and control is explored. Recent very large scale integration (VLSI) advances have made such applications an achievable goal. The PDP machine has demonstrated the ability to automatically organize stored information, handle unfamiliar and contradictory input data and perform the actions necessary. The PDP machine has demonstrated that it can perform inference and knowledge operations with greater speed and flexibility and at lower cost than traditional architectures. In applications where the rule set governing an expert system's decisions is difficult to formulate, PDP can be used to extract rules by associating the information an expert receives with the actions taken.

The concept of using Parallel Distributed Processing (PDP) to enhance automated experiment monitoring and control is explored. Recent very large scale integration (VLSI) advances have made such applications an achievable goal. The PDP machine has demonstrated the ability to automatically organize stored information, handle unfamiliar and contradictory input data and perform the actions necessary. The PDP machine has demonstrated that it can perform inference and knowledge operations with greater speed and flexibility and at lower cost than traditional architectures. In applications where the rule set governing an expert system's decisions is difficult to formulate, PDP can be used to extract rules by associating the information an expert receives with the actions taken.

The concept of using Parallel Distributed Processing (PDP) to enhance automated experiment monitoring and control is explored. Recent very large scale integration (VLSI) advances have made such applications an achievable goal. The PDP machine has demonstrated the ability to automatically organize stored information, handle unfamiliar and contradictory input data and perform the actions necessary. The PDP machine has demonstrated that it can perform inference and knowledge operations with greater speed and flexibility and at lower cost than traditional architectures. In applications where the rule set governing an expert system's decisions is difficult to formulate, PDP can be used to extract rules by associating the information an expert receives with the actions taken.

The concept of using Parallel Distributed Processing (PDP) to enhance automated experiment monitoring and control is explored. Recent very large scale integration (VLSI) advances have made such applications an achievable goal. The PDP machine has demonstrated the ability to automatically organize stored information, handle unfamiliar and contradictory input data and perform the actions necessary. The PDP machine has demonstrated that it can perform inference and knowledge operations with greater speed and flexibility and at lower cost than traditional architectures. In applications where the rule set governing an expert system's decisions is difficult to formulate, PDP can be used to extract rules by associating the information an expert receives with the actions taken.

Multidisciplinary design optimization (MDO) for large-scale engineering problems poses many challenges (e.g., the design of an efficient concurrent paradigm for global optimization based on disciplinary analyses, expensive computations over vast data sets, etc.) This work focuses on the application of distributed schemes for massively parallel architectures to MDO problems, as a tool for reducing computation time and solving larger problems. The specific problem considered here is configuration optimization of a high speed civil transport (HSCT), and the efficient parallelization of the embedded paradigm for reasonable design space identification. Two distributed dynamic load balancing techniques (random polling and global round robin with message combining) and two necessary termination detection schemes (global task count and token passing) were implemented and evaluated in terms of effectiveness and scalability to large problem sizes and a thousand processors. The effect of certain parameters on execution time was also inspected. Empirical results demonstrated stable performance and effectiveness for all schemes, and the parametric study showed that the selected algorithmic parameters have a negligible effect on performance.

Mind and brain provide two quite different perspectives for viewing cognition. Yet both perspectives are informed by the study of parallel distributed processing. This duality creates a certain ambiguity about the interpretation of a particular PDP model of a cognitive process: Is each processing unit to be interpreted as a neuron? Is the model supposed to relate to the neural implementation of the process in some less direct way? A closely related set of questions arises when it is observed that PDP models of cognitive processing divide broadly into two classes. In local models, the activity of a single unit represents the degree of participation in the processing of a known conceptual entity a word, a word sense, a phoneme, a motor program. In distributed models, the strength of patterns of activity over many units determine the degree of participation of these conceptual entities. In some models, these patterns are chosen in a deliberately arbitrary way, so that the activity of a single unit has no apparent meaning whatever -- no discernible relation to the conceptual entities involved in the cognitive process. On the surface, at least, these two types of models seem quite different. Are they as different as they seem? How are they related? This chapter begins with a brief consideration of the neural interpretation of PDP models of cognition. These considerations serve mostly to lay out a certain perspective on the PDP modeling world, to make some distinctions I have found to be valuable, to introduce some terminology and to lead into the main question of this chapter: How are distributed and local PDP models related? The chapter ends with a discussion of how, using the framework of PDP models, we might forge a mathematical relationship between the principles of mind and brain.

Mind and brain provide two quite different perspectives for viewing cognition. Yet both perspectives are informed by the study of parallel distributed processing. This duality creates a certain ambiguity about the interpretation of a particular PDP model of a cognitive process: Is each processing unit to be interpreted as a neuron? Is the model supposed to relate to the neural implementation of the process in some less direct way? A closely related set of questions arises when it is observed that PDP models of cognitive processing divide broadly into two classes. In local models, the activity of a single unit represents the degree of participation in the processing of a known conceptual entity a word, a word sense, a phoneme, a motor program. In distributed models, the strength of patterns of activity over many units determine the degree of participation of these conceptual entities. In some models, these patterns are chosen in a deliberately arbitrary way, so that the activity of a single unit has no apparent meaning whatever -- no discernible relation to the conceptual entities involved in the cognitive process. On the surface, at least, these two types of models seem quite different. Are they as different as they seem? How are they related? This chapter begins with a brief consideration of the neural interpretation of PDP models of cognition. These considerations serve mostly to lay out a certain perspective on the PDP modeling world, to make some distinctions I have found to be valuable, to introduce some terminology and to lead into the main question of this chapter: How are distributed and local PDP models related? The chapter ends with a discussion of how, using the framework of PDP models, we might forge a mathematical relationship between the principles of mind and brain.

Mind and brain provide two quite different perspectives for viewing cognition. Yet both perspectives are informed by the study of parallel distributed processing. This duality creates a certain ambiguity about the interpretation of a particular PDP model of a cognitive process: Is each processing unit to be interpreted as a neuron? Is the model supposed to relate to the neural implementation of the process in some less direct way? A closely related set of questions arises when it is observed that PDP models of cognitive processing divide broadly into two classes. In local models, the activity of a single unit represents the degree of participation in the processing of a known conceptual entity a word, a word sense, a phoneme, a motor program. In distributed models, the strength of patterns of activity over many units determine the degree of participation of these conceptual entities. In some models, these patterns are chosen in a deliberately arbitrary way, so that the activity of a single unit has no apparent meaning whatever -- no discernible relation to the conceptual entities involved in the cognitive process. On the surface, at least, these two types of models seem quite different. Are they as different as they seem? How are they related? This chapter begins with a brief consideration of the neural interpretation of PDP models of cognition. These considerations serve mostly to lay out a certain perspective on the PDP modeling world, to make some distinctions I have found to be valuable, to introduce some terminology and to lead into the main question of this chapter: How are distributed and local PDP models related? The chapter ends with a discussion of how, using the framework of PDP models, we might forge a mathematical relationship between the principles of mind and brain.

Mind and brain provide two quite different perspectives for viewing cognition. Yet both perspectives are informed by the study of parallel distributed processing. This duality creates a certain ambiguity about the interpretation of a particular PDP model of a cognitive process: Is each processing unit to be interpreted as a neuron? Is the model supposed to relate to the neural implementation of the process in some less direct way? A closely related set of questions arises when it is observed that PDP models of cognitive processing divide broadly into two classes. In local models, the activity of a single unit represents the degree of participation in the processing of a known conceptual entity a word, a word sense, a phoneme, a motor program. In distributed models, the strength of patterns of activity over many units determine the degree of participation of these conceptual entities. In some models, these patterns are chosen in a deliberately arbitrary way, so that the activity of a single unit has no apparent meaning whatever -- no discernible relation to the conceptual entities involved in the cognitive process. On the surface, at least, these two types of models seem quite different. Are they as different as they seem? How are they related? This chapter begins with a brief consideration of the neural interpretation of PDP models of cognition. These considerations serve mostly to lay out a certain perspective on the PDP modeling world, to make some distinctions I have found to be valuable, to introduce some terminology and to lead into the main question of this chapter: How are distributed and local PDP models related? The chapter ends with a discussion of how, using the framework of PDP models, we might forge a mathematical relationship between the principles of mind and brain.

Mind and brain provide two quite different perspectives for viewing cognition. Yet both perspectives are informed by the study of parallel distributed processing. This duality creates a certain ambiguity about the interpretation of a particular PDP model of a cognitive process: Is each processing unit to be interpreted as a neuron? Is the model supposed to relate to the neural implementation of the process in some less direct way? A closely related set of questions arises when it is observed that PDP models of cognitive processing divide broadly into two classes. In local models, the activity of a single unit represents the degree of participation in the processing of a known conceptual entity a word, a word sense, a phoneme, a motor program. In distributed models, the strength of patterns of activity over many units determine the degree of participation of these conceptual entities. In some models, these patterns are chosen in a deliberately arbitrary way, so that the activity of a single unit has no apparent meaning whatever -- no discernible relation to the conceptual entities involved in the cognitive process. On the surface, at least, these two types of models seem quite different. Are they as different as they seem? How are they related? This chapter begins with a brief consideration of the neural interpretation of PDP models of cognition. These considerations serve mostly to lay out a certain perspective on the PDP modeling world, to make some distinctions I have found to be valuable, to introduce some terminology and to lead into the main question of this chapter: How are distributed and local PDP models related? The chapter ends with a discussion of how, using the framework of PDP models, we might forge a mathematical relationship between the principles of mind and brain.

Mind and brain provide two quite different perspectives for viewing cognition. Yet both perspectives are informed by the study of parallel distributed processing. This duality creates a certain ambiguity about the interpretation of a particular PDP model of a cognitive process: Is each processing unit to be interpreted as a neuron? Is the model supposed to relate to the neural implementation of the process in some less direct way? A closely related set of questions arises when it is observed that PDP models of cognitive processing divide broadly into two classes. In local models, the activity of a single unit represents the degree of participation in the processing of a known conceptual entity a word, a word sense, a phoneme, a motor program. In distributed models, the strength of patterns of activity over many units determine the degree of participation of these conceptual entities. In some models, these patterns are chosen in a deliberately arbitrary way, so that the activity of a single unit has no apparent meaning whatever -- no discernible relation to the conceptual entities involved in the cognitive process. On the surface, at least, these two types of models seem quite different. Are they as different as they seem? How are they related? This chapter begins with a brief consideration of the neural interpretation of PDP models of cognition. These considerations serve mostly to lay out a certain perspective on the PDP modeling world, to make some distinctions I have found to be valuable, to introduce some terminology and to lead into the main question of this chapter: How are distributed and local PDP models related? The chapter ends with a discussion of how, using the framework of PDP models, we might forge a mathematical relationship between the principles of mind and brain.

Mind and brain provide two quite different perspectives for viewing cognition. Yet both perspectives are informed by the study of parallel distributed processing. This duality creates a certain ambiguity about the interpretation of a particular PDP model of a cognitive process: Is each processing unit to be interpreted as a neuron? Is the model supposed to relate to the neural implementation of the process in some less direct way? A closely related set of questions arises when it is observed that PDP models of cognitive processing divide broadly into two classes. In local models, the activity of a single unit represents the degree of participation in the processing of a known conceptual entity a word, a word sense, a phoneme, a motor program. In distributed models, the strength of patterns of activity over many units determine the degree of participation of these conceptual entities. In some models, these patterns are chosen in a deliberately arbitrary way, so that the activity of a single unit has no apparent meaning whatever -- no discernible relation to the conceptual entities involved in the cognitive process. On the surface, at least, these two types of models seem quite different. Are they as different as they seem? How are they related? This chapter begins with a brief consideration of the neural interpretation of PDP models of cognition. These considerations serve mostly to lay out a certain perspective on the PDP modeling world, to make some distinctions I have found to be valuable, to introduce some terminology and to lead into the main question of this chapter: How are distributed and local PDP models related? The chapter ends with a discussion of how, using the framework of PDP models, we might forge a mathematical relationship between the principles of mind and brain.

Mind and brain provide two quite different perspectives for viewing cognition. Yet both perspectives are informed by the study of parallel distributed processing. This duality creates a certain ambiguity about the interpretation of a particular PDP model of a cognitive process: Is each processing unit to be interpreted as a neuron? Is the model supposed to relate to the neural implementation of the process in some less direct way? A closely related set of questions arises when it is observed that PDP models of cognitive processing divide broadly into two classes. In local models, the activity of a single unit represents the degree of participation in the processing of a known conceptual entity a word, a word sense, a phoneme, a motor program. In distributed models, the strength of patterns of activity over many units determine the degree of participation of these conceptual entities. In some models, these patterns are chosen in a deliberately arbitrary way, so that the activity of a single unit has no apparent meaning whatever -- no discernible relation to the conceptual entities involved in the cognitive process. On the surface, at least, these two types of models seem quite different. Are they as different as they seem? How are they related? This chapter begins with a brief consideration of the neural interpretation of PDP models of cognition. These considerations serve mostly to lay out a certain perspective on the PDP modeling world, to make some distinctions I have found to be valuable, to introduce some terminology and to lead into the main question of this chapter: How are distributed and local PDP models related? The chapter ends with a discussion of how, using the framework of PDP models, we might forge a mathematical relationship between the principles of mind and brain.

Matched-field processing (MFP) localizes sources more accurately than plane-wave beamforming by employing full-wave acoustic propagation models for the cluttered ocean environment. The minimum variance distortionless response MFP (MVDR-MFP) algorithm incorporates the MVDR technique into the MFP algorithm to enhance beamforming performance. Such an adaptive MFP algorithm involves intensive computational and memory requirements due to its complex acoustic model and environmental adaptation. The real-time implementation of adaptive MFP algorithms for large surveillance areas presents a serious computational challenge where high-performance embedded computing and parallel processing may be required to meet real-time constraints. In this paper, three parallel algorithms based on domain decomposition techniques are presented for the MVDR-MFP algorithm on distributed array systems. The parallel performance factors in terms of execution times, communication times, parallel efficiencies, and memory capacities are examined on three potential distributed systems including two types of digital signal processor arrays and a cluster of personal computers. The performance results demonstrate that these parallel algorithms provide a feasible solution for real-time, scalable, and cost-effective adaptive beamforming on embedded, distributed array systems.

This report compiles the results of army research in the area of modeling asynchronous parallel architectures and computation for applications in the areas of digital image and signal processing. The work can be broadly divided into three areas: (1) Case studies of parallel image processing algorithms and tasks, the objective of which is to study the interaction of parallel processes and parallel architectures; (2) Modeling of interconnection networks and (3) Aspects of the problem of modeling parallel processes and parallel architectures.

Two methods are used to speed up the execution of a computational task. One is new technology development and the other is the exploitation of parallelism in the computation. To take an advantage of the parallelism in a task requires the utilization of parallel computer architectures. At a certain high level of abstraction a parallel computer system is represented as a graph where the nodes represent processors, memories, or other devices, and the edges represent the communication links. In this thesis the following problems of parallel processing are studied. First is a theoretical study of topological properties of interconnection networks. Second is a case study of a network design for a real-time system. Lastly, the use of SIMD(Single Instruction Stream Multiple Data Stream) networks for performing 'shuffles'. A general model that can be used to describe networks and systems with arbitrary topologies is developed. Based upon the of morphism of groups, the concept of morphism of systems is developed. The morphism of systems is called quasimorphism and allows a method of comparison between topologically arbitrary parallel computer systems. The quasimorphism is used to study the emulation of one system by another.

Two methods are used to speed up the execution of a computational task. One is new technology development and the other is the exploitation of parallelism in the computation. To take an advantage of the parallelism in a task requires the utilization of parallel computer architectures. At a certain high level of abstraction a parallel computer system is represented as a graph where the nodes represent processors, memories, or other devices, and the edges represent the communication links. In this thesis the following problems of parallel processing are studied. First is a theoretical study of topological properties of interconnection networks. Second is a case study of a network design for a real-time system. Lastly, the use of SIMD(Single Instruction Stream Multiple Data Stream) networks for performing 'shuffles'. A general model that can be used to describe networks and systems with arbitrary topologies is developed. Based upon the of morphism of groups, the concept of morphism of systems is developed. The morphism of systems is called quasimorphism and allows a method of comparison between topologically arbitrary parallel computer systems. The quasimorphism is used to study the emulation of one system by another.

A growing body of evidence suggests that traditional views of automaticity are in need of revision. For example, automaticity has often been treated as an all-or-none phenomenon, and traditional theories have held that automatic processes are independent of attention. Yet recent empirical data suggest that automatic processes are continuous, and furthermore are subject to attentional control. In this paper we present a model of attention which addresses these issues. Using a parallel distributed processing framework we propose that the attributes of automaticity depend upon the strength of a processing pathway and that strength increases with training. Using the Stroop effect as an example, we show how automatic processes are continuous and emerge gradually with practice. Specifically, we present a computational model of the Stroop task which simulates the time course of processing as well as the effects of learning. This was accomplished by combining the cascade mechanism described by McClelland (1979) with the back propagation learning algorithm (Rumelhart, Hinton, & Williams, 1986). The model is able to simulate performance in the standard Stroop task, as well as aspects of performance in variants of this task which manipulate SOA, response set, and degree of practice. In the discussion we contrast our model with other models, and indicate how it relates to many of the central issues in the literature on attention, automaticity, and interference. (Author) (kr)

Typical desktop workstations can be a severe bottleneck in the viewing and enhancement of imagery data. Due to the nature of many image processing algorithms, an effective method for alleviating this problem is through parallelism. Parallel hardware can come in many forms, from small clusters of workstations and workstations with many processors to dedicated hardware containing 10's, 100's and 1000's of processing nodes. One of the challenges is developing a portable parallel image processing library in such a potentially diverse environment. These issues led to the development of a Parallel Image Processing Toolkit (PIPT). The toolkit hides the detail of parallelization from the users of the PIPT and provides a uniform programming interface. In developing the toolkit the issues of advanced data handling, load, balancing and parallel visualization were addressed. In addition a specific computationally expensive High Resolution Video Stills algorithm was implemented within the PIPT. The important contributions that resulted from this work transcend the individual tasks that were undertaken. First, the general approach that was taken to implement the PIPT illustrates several important principles for the design and implementation of general purpose parallel libraries. in this regard, the PIPT design can serve as a design pattern for an extensible parallel library. Second, the design pattern of the current implementation of the PIPT contains some notable attempts at programming image processing tasks in a generic fashion.

A growing body of evidence suggests that traditional views of automaticity are in need of revision. For example, automaticity has often been treated as an all-or-none phenomenon, and traditional theories have held that automatic processes are independent of attention. Yet recent empirial data suggests that automatic processes are continuous, and furthermore are subject to attentional control. In this paper we present a model of attention which addresses these issues. Using a parallel distributed processing framework we propose that the attributes of automaticity depend upon the strength of a process and that strength increases with training. Using the Stroop effect as an example, we show how automatic processes are continuous and emerge gradually with practice. Specifically, we present a computational model of the Stroop task which simulates the time course of processing as well as the effects of learning.

This paper provides a brief overview of the connectionist or parallel distributed processing framework for modeling cognitive processes, and considers the application of the connectionist framework to problems of cognitive development. Several aspects of cognitive development might result from the process of learning as it occurs in multi-layer networks. This learning process has the characteristic that it reduces the discrepancy between expected and observed events. As it does this, representations develop on hidden units which dramatically change both the way in which the network represents the environment from which it learns and the expectations that the network generates about environmental events. The learning process exhibits relatively abrupt transitions corresponding to stage shifts in cognitive development. These points are illustrated using a network that learns to anticipate which side of a balance beam will go down, based on the number of weights on each side of the fulcrum and their distance from the fulcrum on each side of the beam. The network is trained in an environment in which weight more frequently governs which side will go down. It recapitulates the states of development seen in children, as well as the stage transitions, as it learns to represent weight and distance information. Keywords: Parallel processing; Data processing.

Many modern command and control applications are driven by imagery, and imagery, in general, may originate from diverse geographically distributed sources. A rapid increase in image sizes and processing capabilities has resulted from improved hardware for collection and display. Moreover, techniques for parallel processing are keeping pace with military requirements by harnessing powerful parallel processors to facilitate the analysis of images. In addition, tools for distributed processing are under development that can be used to coordinate the processing effort across a network of parallel processing systems. This paper describes a system that can be used to request image analysis over distributed parallel processing facilities, and integrate the results into command and control systems.

The supervised relaxation operator combines the information from multiple ancillary data sources with the information from multispectral remote sensing image data and spatial context. Iterative calculation integrate information from the various sources, reaching a balance in consistency between these sources of information. The supervised relaxation operator is shown to produce substantial improvements in classification accuracy compared to the accuracy produced by the conventional maximum likelihood classifier using spectral data only. The convergence property of the supervised relaxation algorithm is also described. Improvement in classification accuracy by means of supervised relaxation comes at a high price in terms of computation. In order to overcome the computation-intensive problem, a distributed/parallel implementation is adopted to take advantage of a high degree of inherent parallelism in the algorithm.

Basic aspects are reviewed of conventional approaches to sentence comprehension and point out are some of the difficulties faced by models that take these approaches. An alternative approach is described, based on the principles of parallel distributed processing, and shown how it offers different answers to basic questions about the nature of the language processing mechanism. An illustrative simulation model captures the key characteristics of the approach, and illustrates how it can cope with the difficulties faced by conventional models. Alternative ways of conceptualizing basic aspects of language processing within the framework of this approach will consider how it can address several arguments that might be brought to bear against it, and suggest avenues for future development. Keywords: Neural networks; Connectionist models; Language comprehension; Language acquisition.

Continuous innovations in adaptive matched-field processing (MFP) algorithms have presented significant increases in computational complexity and resource requirements that make development and use of advanced parallel processing techniques imperative. In real-time sonar systems operating in severe underwater environments, there is a high likelihood of some part of systems exhibiting defective behavior, resulting in loss of critical network, processor, and sensor elements, and degradation in beam power pattern. Such real-time sonar systems require high reliability to overcome these challenging problems. In this paper, efficient fault-tolerant parallel algorithms based on coarse-grained domain decomposition methods are developed in order to meet real-time and reliability requirements on distributed array systems in the presence of processor and sensor element failures. The performance of the fault-tolerant parallel algorithms is experimentally analyzed in terms of beamforming performance, computation time, speedup, and parallel efficiency on a distributed testbed. The performance results demonstrate that these fault-tolerant parallel algorithms can provide real-time, scalable, lightweight, and fault-tolerant implementations for adaptive MFP algorithms on distributed array systems.

Schizophrenia is marked by a variety of cognitive and biological abnormalities. In the first part of this paper we describe schizophrenic cognitive deficits in three experimental tasks which tap attention and language processing abilities. We also review biological disturbances that have been reported involving the frontal lobes and the mesocortical dopamine system. In the second part of the paper we present three computer models, each of which simulates normal performance in one of the cognitive tasks described initially. These models were developed within the connectionist (or parallel distributed processing) framework. At the behavioral level, the models suggest that a disturbance in the processing of context can account for schizophrenic patterns of performance in both the attention and language-related tasks. At the same time, the models incorporate features of biological computation that address the biological processes underlying cognitive deficits. All three models incorporate a mechanism for processing context that can be identified with frontal lobe function, and a parameter that corresponds to the effects of dopamine on frontal cortex. A disturbance in this parameter is sufficient to account for schizophrenic patterns of performance in all three of the cognitive tasks simulated. Thus, the models offer an explanatory mechanism linking performance deficits to a disturbance in the processing of context which, in turn, is attributed to a reduction of dopaminergic activity in prefrontal cortex. In the General Discussion, we consider the implications of these models for our understanding of both normal and schizophrenic cognition. We conclude with a discussion of some of the general issues surrounding the modelling endeavor itself.