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Drawing form the classic priming work (Marcel 1980) and recent advances in semantic priming (Vikbladh and Burgess et al 2023) we investigate the notion that parallel processing of semantic primes is possible when those primes are subliminal but not when conscious. Vikbladh, O., & Burgess, N. (2023, October 31). Revisiting Dichoptic Pattern Masking for Subliminal Semantic Priming. Retrieved from osf.io/kmp8h

Drawing form the classic priming work (Marcel 1980) and recent advances in semantic priming (Vikbladh and Burgess et al 2023) we investigate the notion that parallel processing of semantic primes is possible when those primes are subliminal but not when conscious. Vikbladh, O., & Burgess, N. (2023, October 31). Revisiting Dichoptic Pattern Masking for Subliminal Semantic Priming. Retrieved from osf.io/kmp8h

Drawing form the classic priming work (Marcel 1980) and recent advances in semantic priming (Vikbladh and Burgess et al 2023) we investigate the notion that parallel processing of semantic primes is possible when those primes are subliminal but not when conscious. Vikbladh, O., & Burgess, N. (2023, October 31). Revisiting Dichoptic Pattern Masking for Subliminal Semantic Priming. Retrieved from osf.io/kmp8h

The focus of this research has been to determine the effectiveness of applying parallel processing techniques to a sizable real-world problem, the simulation of the dynamics associated with a tether which connects two objects in low earth orbit, and to explore the degree to which the parallelization process can be automated through the creation of new software tools. The goal has been to utilize this specific application problem as a base to develop more generally applicable techniques.

Many scientific applications involve grids that lack a uniform underlying structure. These applications are often also dynamic in nature in that the grid structure significantly changes between successive phases of execution. In parallel computing environments, mesh adaptation of unstructured grids through selective refinement/coarsening has proven to be an effective approach. However, achieving load balance while minimizing interprocessor communication and redistribution costs is a difficult problem. Traditional dynamic load balancers are mostly inadequate because they lack a global view of system loads across processors. In this paper, we propose a novel and general-purpose load balancer that utilizes symmetric broadcast networks (SBN) as the underlying communication topology, and compare its performance with a successful global load balancing environment, called PLUM, specifically created to handle adaptive unstructured applications. Our experimental results on an IBM SP2 demonstrate that the SBN-based load balancer achieves lower redistribution costs than that under PLUM by overlapping processing and data migration.

The development of numerical methods and software tools for parallel processors can be aided through the use of a hardware test-bed. The test-bed architecture must be flexible enough to support investigations into architecture-algorithm interactions. One way to implement a test-bed is to use a commercial parallel processor. Unfortunately, most commercial parallel processors are fixed in their interconnection and/or processor architecture. In this paper, we describe a modified n cube architecture, called the hypercluster, which is a superset of many other processor and interconnection architectures. The hypercluster is intended to support research into parallel processing of computational fluid and structural mechanics problems which may require a number of different architectural configurations. An example of how a typical partial differential equation solution algorithm maps on to the hypercluster is given.

Are there limits on the speed of computers and their CPUs? A review of new processor architectures to increase computing speed. Guests: Dr. Howard Resnikoff, MIT; Eugene Brooks, Lawrence Livermore; David Rodgers, Sequent; Dr. H.T. Kung, Carnegie Mellon Univ; Craig Mundie, Alliant; Jeff Canin, Hambrecht & Quist; Gary Kildall, Digital Research; Peter Appleton-Jones, ELXSI Products/Demos:

This article is from Bioinformatics , volume 30 . Abstract Motivation: Using high-throughput sequencing, researchers are now generating hundreds of whole-genome assays to measure various features such as transcription factor binding, histone marks, DNA methylation or RNA transcription. Displaying so much data generally leads to a confusing accumulation of plots. We describe here a multithreaded library that computes statistics on large numbers of datasets (Wiggle, BigWig, Bed, BigBed and BAM), generating statistical summaries within minutes with limited memory requirements, whether on the whole genome or on selected regions.Availability and Implementation: The code is freely available under Apache 2.0 license at www.github.com/Ensembl/WiggletoolsContact:[email protected] or [email protected]

In this paper, parallel processing is used to analyze the mixing, and combustion behavior of hypersonic flow. Preliminary work for a sonic transverse hydrogen jet injected from a slot into a Mach 4 airstream in a two-dimensional duct combustor has been completed [Moon and Chung, 1996]. Our aim is to extend this work to three-dimensional domain using multithreaded domain decomposition parallel processing based on the flowfield-dependent variation theory. Numerical simulations of chemically reacting flows are difficult because of the strong interactions between the turbulent hydrodynamic and chemical processes. The algorithm must provide an accurate representation of the flowfield, since unphysical flowfield calculations will lead to the faulty loss or creation of species mass fraction, or even premature ignition, which in turn alters the flowfield information. Another difficulty arises from the disparity in time scales between the flowfield and chemical reactions, which may require the use of finite rate chemistry. The situations are more complex when there is a disparity in length scales involved in turbulence. In order to cope with these complicated physical phenomena, it is our plan to utilize the flowfield-dependent variation theory mentioned above, facilitated by large eddy simulation. Undoubtedly, the proposed computation requires the most sophisticated computational strategies. The multithreaded domain decomposition parallel processing will be necessary in order to reduce both computational time and storage. Without special treatments involved in computer engineering, our attempt to analyze the airbreathing combustion appears to be difficult, if not impossible.

In this paper, parallel processing is used to analyze the mixing, and combustion behavior of hypersonic flow. Preliminary work for a sonic transverse hydrogen jet injected from a slot into a Mach 4 airstream in a two-dimensional duct combustor has been completed [Moon and Chung, 1996]. Our aim is to extend this work to three-dimensional domain using multithreaded domain decomposition parallel processing based on the flowfield-dependent variation theory. Numerical simulations of chemically reacting flows are difficult because of the strong interactions between the turbulent hydrodynamic and chemical processes. The algorithm must provide an accurate representation of the flowfield, since unphysical flowfield calculations will lead to the faulty loss or creation of species mass fraction, or even premature ignition, which in turn alters the flowfield information. Another difficulty arises from the disparity in time scales between the flowfield and chemical reactions, which may require the use of finite rate chemistry. The situations are more complex when there is a disparity in length scales involved in turbulence. In order to cope with these complicated physical phenomena, it is our plan to utilize the flowfield-dependent variation theory mentioned above, facilitated by large eddy simulation. Undoubtedly, the proposed computation requires the most sophisticated computational strategies. The multithreaded domain decomposition parallel processing will be necessary in order to reduce both computational time and storage. Without special treatments involved in computer engineering, our attempt to analyze the airbreathing combustion appears to be difficult, if not impossible.

Clustering data is one of the most common statistical and machine learning techniques for analyzing big data. Clustering can be particularly difficult when the data sets include categorical, missing, or noise variables. The tree clustering algorithm developed by Samuel Buttrey and Lyn Whitaker, as described in the December 2015 issue of The R Journal, seems to provide a solution to these problems, but it requires a large set of overhead computations. This issue is intensified when working with high-dimensional data because the extent of treeClust’s overhead computations are based on the dimensions of the data. High performance computing (HPC) and parallel processing present a solution to this overhead computation burden, but treeClust’s existing parallel processing method does not work on the Naval Postgraduate School’s HPC, the Hamming Supercomputer (HSC). Furthermore, correctly determining what HPC resources to use can be a difficult task. In this thesis, we present a new HSC-specific method for parallel processing data using the treeClust R package developed by Buttrey and Whitaker. Based on the results of our experiments, our method approximates the optimal resource HPC request, so that users realize the best run time when using treeClust on the HSC.

Clustering data is one of the most common statistical and machine learning techniques for analyzing big data. Clustering can be particularly difficult when the data sets include categorical, missing, or noise variables. The tree clustering algorithm developed by Samuel Buttrey and Lyn Whitaker, as described in the December 2015 issue of The R Journal, seems to provide a solution to these problems, but it requires a large set of overhead computations. This issue is intensified when working with high-dimensional data because the extent of treeClust’s overhead computations are based on the dimensions of the data. High performance computing (HPC) and parallel processing present a solution to this overhead computation burden, but treeClust’s existing parallel processing method does not work on the Naval Postgraduate School’s HPC, the Hamming Supercomputer (HSC). Furthermore, correctly determining what HPC resources to use can be a difficult task. In this thesis, we present a new HSC-specific method for parallel processing data using the treeClust R package developed by Buttrey and Whitaker. Based on the results of our experiments, our method approximates the optimal resource HPC request, so that users realize the best run time when using treeClust on the HSC.

Clustering data is one of the most common statistical and machine learning techniques for analyzing big data. Clustering can be particularly difficult when the data sets include categorical, missing, or noise variables. The tree clustering algorithm developed by Samuel Buttrey and Lyn Whitaker, as described in the December 2015 issue of The R Journal, seems to provide a solution to these problems, but it requires a large set of overhead computations. This issue is intensified when working with high-dimensional data because the extent of treeClust’s overhead computations are based on the dimensions of the data. High performance computing (HPC) and parallel processing present a solution to this overhead computation burden, but treeClust’s existing parallel processing method does not work on the Naval Postgraduate School’s HPC, the Hamming Supercomputer (HSC). Furthermore, correctly determining what HPC resources to use can be a difficult task. In this thesis, we present a new HSC-specific method for parallel processing data using the treeClust R package developed by Buttrey and Whitaker. Based on the results of our experiments, our method approximates the optimal resource HPC request, so that users realize the best run time when using treeClust on the HSC.

Tri 2 2015/2016

Tri 2 2015/2016

Available from National Technical Information Service, Springfield, Va

We introduce a model for communication costs in parallel processing environments, called the 'hyperbolic model,' which generalizes two-parameter dedicated-link models in an analytically simple way. Dedicated interprocessor links parameterized by a latency and a transfer rate that are independent of load are assumed by many existing communication models; such models are unrealistic for workstation networks. The communication system is modeled as a directed communication graph in which terminal nodes represent the application processes that initiate the sending and receiving of the information and in which internal nodes, called communication blocks (CBs), reflect the layered structure of the underlying communication architecture. The direction of graph edges specifies the flow of the information carried through messages. Each CB is characterized by a two-parameter hyperbolic function of the message size that represents the service time needed for processing the message.

During the last 3 years we have developed a mathematical theory of algorithms and implementation strategies for DSP computations on RISC and DSP chips and parallel architectures ranging from scalable multinode boards to massively parallel multinode computers as typified by the Intel's touchstone systems.

Many operations on strings of length n can be speeded up by a factor of p using p processors. String operations can also be speeded up, even when a single processor is used, by compactly encoding the strings, e.g. using run length code. This paper shows how to combine these two approaches by using p processors to process compactly encoded strings. The author studies various representations of bit strings and parallel algorithms to process these representations using a multiprocessor system. Section 2 describes the parallel processing model used. Sections 3 and 4 discuss various compact representations of strings, and their conversions to each other. Section 5 presents algorithms to process run length coded strings. Section 6 briefly discusses the extension of this work to representations of two-dimensional objects. Additional keywords: message processing, computations, binary arithmetic, coding, coordinates, value.

A single instruction stream - multiple data stream (SIMD) computer performs one algorithm (single instruction stream) on vectors of data of a control unit (CU), processing elements (PEs), and an interconnection network. The CU broadcasts instructions to the N Pes (where N is a power of two). The interconnection network is the mechanism that allows PEs to pass data among themselves. Four types of interconnection networks are discussed in this work: the Shuffle-Exchange network, the Cube network, the ILLIAC network, and the Plus-Minus 2 (PM2I) network. Each type has been discussed in the literature and used in an existing or proposed machine design. For each of these four network types, different hardware structures are considered. A recirculating network consists of one stage of switches that is reused until the data reach their final destinations. A combinational logic multistage network consists of several stages of switches and, usually, data is transferred in one pass through the network. In pipelined multistage networks, which are introduced, registers are inserted after each stage of a combinational logic multistage network. The data are divided into segments, and these segments are passed in a parallel-pipelined manner. Hardware implementations for recirculating, combinational logic multistage, and pipelined multistage networks are presented and analyzed.

The Naval Research Laboratory's (NRL) Code 7440 Production Enhancement Team at the Stennis Space Center has been tasked to develop ways to speedup hydrographic data processing at the Naval Oceanographic Office (NAVOCEANO) [Depn02]. This paper presents the final development of a parallelized version of the Pfm_loader application customized to run on a Beowulf cluster.

Parallel programming techniques have been used for years to develop processing software that does the same work in only a fraction of the time. The research in this paper focuses on the I/O problem associated with a parallel application writing to a single physical disk. Included in our research are the original ideas that led to the first version of the parallel software, subsequent versions of the software derived from lessons learned from benchmark results, and speedup results of each version. The platform used was a custom built Linux Beowulf cluster running a standard Linux kernel and the MPICH parallel message-passing library. The underlying purpose of this software is to process hydrographic data having a complicated, multi-tiered format. The data processing involves reading tens to hundreds of files containing raw data, filtering out extraneous data values, and writing the filtered data to a single file used in additional processing. The problem is not computationally intensive, but bound by the system's file writing capability. Subsequent versions of the parallel software developed exploit the strengths of the system's hardware to write the output file in the most time efficient manner. Each software version uses advanced software architecture schemes to achieve better results. Results show that the more responsible the software was for organizing the data before writing, the better the speedup. The critical factor for writing data efficiently involved the limitation of writing data over a single I/O controller. Our parallel software has fantastic utility where system specifications do not allow for the use of parallel file systems, or writing data over multiple I/O controllers.

45 p. 28 cm

Least-squares (LS) estimations and spectral decomposition algorithms constitute the heart of modern signal processing and communication problems. Implementations of recursive LS and spectral decomposition algorithms onto parallel processing architectures such as systolic arrays with efficient fault-tolerant schemes are the major concerns of this dissertation. There are four major results in this dissertation. First, we propose the systolic block Householder transformation with application to the recursive least-squares minimization. It is successfully implemented on a systolic array with a two-level pipelined implementation at the vector level as well as at the word level. Second, a real-time algorithm-based concurrent error detection scheme based on the residual method is proposed for the QRD RLS systolic array. The fault diagnosis, order degraded reconfiguration, and performance analysis are also considered. Third, the dynamic range, stability, error detection capability under finite-precision implementation, order degraded performance, and residual estimation under faulty situations for the QRD RLS systolic array are studied in details. Finally, we propose the use of multi-phase systolic algorithms for spectral decomposition based on the QR algorithm. Two systolic architectures, one based on triangular array and another based on rectangular array, are presented for the multiphase operations with fault-tolerant considerations. Eigenvectors and singular vectors can be easily obtained by using the multi-pase operations. Performance issues are also considered.

Least-squares (LS) estimations and spectral decomposition algorithms constitute the heart of modern signal processing and communication problems. Implementations of recursive LS and spectral decomposition algorithms onto parallel processing architectures such as systolic arrays with efficient fault-tolerant schemes are the major concerns of this dissertation. There are four major results in this dissertation. First, we propose the systolic block Householder transformation with application to the recursive least-squares minimization. It is successfully implemented on a systolic array with a two-level pipelined implementation at the vector level as well as at the word level. Second, a real-time algorithm-based concurrent error detection scheme based on the residual method is proposed for the QRD RLS systolic array. The fault diagnosis, order degraded reconfiguration, and performance analysis are also considered. Third, the dynamic range, stability, error detection capability under finite-precision implementation, order degraded performance, and residual estimation under faulty situations for the QRD RLS systolic array are studied in details. Finally, we propose the use of multi-phase systolic algorithms for spectral decomposition based on the QR algorithm. Two systolic architectures, one based on triangular array and another based on rectangular array, are presented for the multiphase operations with fault-tolerant considerations. Eigenvectors and singular vectors can be easily obtained by using the multi-pase operations. Performance issues are also considered.

Least-squares (LS) estimations and spectral decomposition algorithms constitute the heart of modern signal processing and communication problems. Implementations of recursive LS and spectral decomposition algorithms onto parallel processing architectures such as systolic arrays with efficient fault-tolerant schemes are the major concerns of this dissertation. There are four major results in this dissertation. First, we propose the systolic block Householder transformation with application to the recursive least-squares minimization. It is successfully implemented on a systolic array with a two-level pipelined implementation at the vector level as well as at the word level. Second, a real-time algorithm-based concurrent error detection scheme based on the residual method is proposed for the QRD RLS systolic array. The fault diagnosis, order degraded reconfiguration, and performance analysis are also considered. Third, the dynamic range, stability, error detection capability under finite-precision implementation, order degraded performance, and residual estimation under faulty situations for the QRD RLS systolic array are studied in details. Finally, we propose the use of multi-phase systolic algorithms for spectral decomposition based on the QR algorithm. Two systolic architectures, one based on triangular array and another based on rectangular array, are presented for the multiphase operations with fault-tolerant considerations. Eigenvectors and singular vectors can be easily obtained by using the multi-pase operations. Performance issues are also considered.

Least-squares (LS) estimations and spectral decomposition algorithms constitute the heart of modern signal processing and communication problems. Implementations of recursive LS and spectral decomposition algorithms onto parallel processing architectures such as systolic arrays with efficient fault-tolerant schemes are the major concerns of this dissertation. There are four major results in this dissertation. First, we propose the systolic block Householder transformation with application to the recursive least-squares minimization. It is successfully implemented on a systolic array with a two-level pipelined implementation at the vector level as well as at the word level. Second, a real-time algorithm-based concurrent error detection scheme based on the residual method is proposed for the QRD RLS systolic array. The fault diagnosis, order degraded reconfiguration, and performance analysis are also considered. Third, the dynamic range, stability, error detection capability under finite-precision implementation, order degraded performance, and residual estimation under faulty situations for the QRD RLS systolic array are studied in details. Finally, we propose the use of multi-phase systolic algorithms for spectral decomposition based on the QR algorithm. Two systolic architectures, one based on triangular array and another based on rectangular array, are presented for the multiphase operations with fault-tolerant considerations. Eigenvectors and singular vectors can be easily obtained by using the multi-pase operations. Performance issues are also considered.

The report summarizes the work performed at the University of Notre Dame under a NASA grant from July 15, 1995 through July 14, 1996. Researchers involved in the work included the PI, Dr. Peter M. Kogge, and three graduate students under his direction in the Computer Science and Engineering Department: Stephen Dartt, Costin Iancu, and Lakshmi Narayanaswany. The organization of this report is as follows. Section 2 is a summary of the problem addressed by this work. Section 3 is a summary of the project's objectives and approach. Section 4 summarizes PIM technology briefly. Section 5 overviews the main results of the work. Section 6 then discusses the importance of the results and future directions. Also attached to this report are copies of several technical reports and publications whose contents directly reflect results developed during this study.

The report summarizes the work performed at the University of Notre Dame under a NASA grant from July 15, 1995 through July 14, 1996. Researchers involved in the work included the PI, Dr. Peter M. Kogge, and three graduate students under his direction in the Computer Science and Engineering Department: Stephen Dartt, Costin Iancu, and Lakshmi Narayanaswany. The organization of this report is as follows. Section 2 is a summary of the problem addressed by this work. Section 3 is a summary of the project's objectives and approach. Section 4 summarizes PIM technology briefly. Section 5 overviews the main results of the work. Section 6 then discusses the importance of the results and future directions. Also attached to this report are copies of several technical reports and publications whose contents directly reflect results developed during this study.

EASY-FLOW, a very high-level data flow language, is introduced for the purpose of adapting programs written in a conventional high-level language to a parallel environment. The level of parallelism provided is of the large-grained variety in which parallel activities take place between subprograms or processes. A program written in EASY-FLOW is a set of subprogram calls as units, structured by iteration, branching, and distribution constructs. A data flow graph may be deduced from an EASY-FLOW program.

EASY-FLOW, a very high-level data flow language, is introduced for the purpose of adapting programs written in a conventional high-level language to a parallel environment. The level of parallelism provided is of the large-grained variety in which parallel activities take place between subprograms or processes. A program written in EASY-FLOW is a set of subprogram calls as units, structured by iteration, branching, and distribution constructs. A data flow graph may be deduced from an EASY-FLOW program.

EASY-FLOW, a very high-level data flow language, is introduced for the purpose of adapting programs written in a conventional high-level language to a parallel environment. The level of parallelism provided is of the large-grained variety in which parallel activities take place between subprograms or processes. A program written in EASY-FLOW is a set of subprogram calls as units, structured by iteration, branching, and distribution constructs. A data flow graph may be deduced from an EASY-FLOW program.

Reproducibility review completed, adding a registration for proper archival and snapshot.

TRI 2 2018/2019

REAPER (DAW) instructional video: Parallel Processing

This article is from Nature Communications , volume 4 . Abstract The increasing demands on information processing require novel computational concepts and true parallelism. Nevertheless, hardware realizations of unconventional computing approaches never exceeded a marginal existence. While the application of optics in super-computing receives reawakened interest, new concepts, partly neuro-inspired, are being considered and developed. Here we experimentally demonstrate the potential of a simple photonic architecture to process information at unprecedented data rates, implementing a learning-based approach. A semiconductor laser subject to delayed self-feedback and optical data injection is employed to solve computationally hard tasks. We demonstrate simultaneous spoken digit and speaker recognition and chaotic time-series prediction at data rates beyond 1 Gbyte/s. We identify all digits with very low classification errors and perform chaotic time-series prediction with 10% error. Our approach bridges the areas of photonic information processing, cognitive and information science.

This article is from Nature Communications , volume 4 . Abstract The increasing demands on information processing require novel computational concepts and true parallelism. Nevertheless, hardware realizations of unconventional computing approaches never exceeded a marginal existence. While the application of optics in super-computing receives reawakened interest, new concepts, partly neuro-inspired, are being considered and developed. Here we experimentally demonstrate the potential of a simple photonic architecture to process information at unprecedented data rates, implementing a learning-based approach. A semiconductor laser subject to delayed self-feedback and optical data injection is employed to solve computationally hard tasks. We demonstrate simultaneous spoken digit and speaker recognition and chaotic time-series prediction at data rates beyond 1 Gbyte/s. We identify all digits with very low classification errors and perform chaotic time-series prediction with 10% error. Our approach bridges the areas of photonic information processing, cognitive and information science.

This article is from Nature Communications , volume 4 . Abstract The increasing demands on information processing require novel computational concepts and true parallelism. Nevertheless, hardware realizations of unconventional computing approaches never exceeded a marginal existence. While the application of optics in super-computing receives reawakened interest, new concepts, partly neuro-inspired, are being considered and developed. Here we experimentally demonstrate the potential of a simple photonic architecture to process information at unprecedented data rates, implementing a learning-based approach. A semiconductor laser subject to delayed self-feedback and optical data injection is employed to solve computationally hard tasks. We demonstrate simultaneous spoken digit and speaker recognition and chaotic time-series prediction at data rates beyond 1 Gbyte/s. We identify all digits with very low classification errors and perform chaotic time-series prediction with 10% error. Our approach bridges the areas of photonic information processing, cognitive and information science.

This article is from Nature Communications , volume 4 . Abstract The increasing demands on information processing require novel computational concepts and true parallelism. Nevertheless, hardware realizations of unconventional computing approaches never exceeded a marginal existence. While the application of optics in super-computing receives reawakened interest, new concepts, partly neuro-inspired, are being considered and developed. Here we experimentally demonstrate the potential of a simple photonic architecture to process information at unprecedented data rates, implementing a learning-based approach. A semiconductor laser subject to delayed self-feedback and optical data injection is employed to solve computationally hard tasks. We demonstrate simultaneous spoken digit and speaker recognition and chaotic time-series prediction at data rates beyond 1 Gbyte/s. We identify all digits with very low classification errors and perform chaotic time-series prediction with 10% error. Our approach bridges the areas of photonic information processing, cognitive and information science.

Several lines of evidence support a 'parallel processing' view for short- and long- term memory in Aplysia: (1) The cellular analog of long-term memory, long-term synaptic facilitation (LTF), can be induced by repeated applications of the neuromodulator serotonin (5HT) in the complete absence of the analog of short-term memory, short-term facilitation (STF). (2) STF and LTF can be dissociated by their temporal dynamics: STF decays within 2 hours, whereas LTF does not begin to be expressed until 10-15 hours. (3) A novel stage of intermediate-term facilitation (lTF) seems to be a precondition to triggering the long-term process (LTF). (4) It has long been known that spaced behavioral training typically produces superior memory compared to massed training. We observe a related phonemenon at the synaptic level: 5 spaced applications of 511T induces STF, ITF and E!?F, whereas comparable exposure to 5HT at one time induces only STF and ITF; no LTF is induced. Finally, at a behavioral level, we find that long-term memory for sensitization can be induced in the absence of short-term memory.

An example of the calculation of mixing flows in a solution crystal growth system is shown here. Solution crystal growth is an important means of producing a wide variety of organic and inorganic crystals. In these systems, a single crystal is grown from a supersaturated solution in which the material to be crystallized is dissolved as a solute. Macroscopic transport of solute molecules from the liquid solution phase to the faces of the growing crystal is almost wholly determined by fluid mechanics. Until recently, the extreme challenges posed by modeling flows within the 3D geometry of such systems has limited progress in understanding transport effects; the calculations shown here represent one of the first efforts to rigorously model flows within a solution crystal growth system.

The purpose of this study was to evaluate the parallel processing impact of MIMD (Multiple-Instruction Multiple-Data Path) computers on Flight Simulation Software. Basic mathematical functions and arithmetic expressions from typical Flight Simulation Software were selected and run on a MIMD computer to evaluate the improvement in execution time that results from the parallel architecture of this type of computer. Recommendations as to the types of tasks which are optimally suitable for this computer architecture are made, together with the improvement in execution speed to be expected. (Author)

The purpose of this study was to evaluate the parallel processing impact of MIMD (Multiple-Instruction Multiple-Data Path) computers on Flight Simulation Software. Basic mathematical functions and arithmetic expressions from typical Flight Simulation Software were selected and run on a MIMD computer to evaluate the improvement in execution time that results from the parallel architecture of this type of computer. Recommendations as to the types of tasks which are optimally suitable for this computer architecture are made, together with the improvement in execution speed to be expected. (Author)

The purpose of this study was to evaluate the parallel processing impact of MIMD (Multiple-Instruction Multiple-Data Path) computers on Flight Simulation Software. Basic mathematical functions and arithmetic expressions from typical Flight Simulation Software were selected and run on a MIMD computer to evaluate the improvement in execution time that results from the parallel architecture of this type of computer. Recommendations as to the types of tasks which are optimally suitable for this computer architecture are made, together with the improvement in execution speed to be expected. (Author)

13 p. 28 cm

13 p. 28 cm

A set of computer aided software engineering (CASE) tools for the software development framework for parallel processing systems based on the Parallel Object Oriented Functional computation model (PROOF) toward the automation of object oriented software development for parallel processing systems is presented. An object oriented analysis tool has been developed to identify and express parallelism in the problem statement. We have developed a communication estimation tool to estimate the communication among objects, and a clustering tool to partition the objects in groups so that the intergroup communication is reduced and concurrency with specified user requirements is realized. The parallelism analysis tool further explores potential parallelism by analyzing the invocation relations and data flow among objects. The PROOF/L back end translator is extended to support clustering and dynamic allocation features in order to achieve better performance on a workstation cluster. The software effort using our approach can be greatly reduced due to implicit synchronization and communication, the user friendly graphical interfaces of the CASE tools, the automated object oriented analysis and parallelism analysis. The performance of software developed using our approach can be improved due to the integration of the object clustering algorithm, the parallelism analysis tool and the extended back end translator.

The study undertook to examine the utility of cellular automata in physical science calculations, especially in fluid dynamics. The scope of the study was expanded to include several related topics which would be of some interest to our sponsors. These include: (1) a comparison of cellular automata (CA) techniques to conventional methods of solving the partial differential equations of fluid dynamics or other physical situations; and (2) the utility and status of using parallel or concurrent processing machines for doing either CA or conventional fluids calculations. Keywords: Impressiable fluid algorithmns; Octogonal quasilattice.

Even with ever changing technology, industry is always looking for ways to improve performance. Scientists are continually finding innovative ways to speed up the processing power of computers. Still, we need faster and more effective ways to accomplish a task. Now that advancements in technology are reaching their limits, industry must look for a new way to keep up with the demands. There is the old adage that two minds are greater than one. This theory can be applied to computer processing. With two processors, not only can more tasks be accomplished, but also tasks can be accomplished faster.

This effort produced an approach for developing software for parallel processing systems based on the parallel object-oriented functional computational model (PROOF). A benefit of this approach is that it allows the software development to be separated from architecture-dependent issues to a great degree. For example, the programmers do not need to be concerned with issues such as synchronization, parallelization, or the topology of the parallel processing system when they address objects in the system. The object-oriented paradigm reflects the parallel structure of the problem space and can represent inherently concurrent behavior. This approach is suitable for MIMD machines and should result in software that is more understandable, modifiable and portable over a variety of parallel computer architectures.