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Results for “Parallel Algorithms”

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PubMed1989

To link, to infer, to understand.

A model of linkage in text processing is proposed: An external proposition and an inference belonging to one frame are superimposed to constitute understanding. A text containing four academic subjects was presented orally to students who recalled it in writing. After transforming the recalls into propositions they are entered into a nonmetric multidimensional scaling to yield a text space. The subjects' interest choices among items of the four aspects are scaled to render an interest space. The decomposition ob both as subspaces of a common space yields an angle as their overall similarity and indicates the degree of predictability from interests. As the aggregate of inferences shows directedness, correlated with volitional-motivational orientation, and inference base is assumed to intervene. It is concluded that recipients try to superimpose and thereby construct a primary stage of processing. This allows for a very general algorithm of parallel information processing (holographic thesis), perhaps constructing the properties we are used to. Motivated perception, knowledge influence, schema-directedness and contribution to coherence are rivalled out as an explanation of this process.

Adult↗

PubMed1992

Searching for distantly related protein sequences in large databases by parallel processing on a transputer machine.

AliMac is an implementation of a sensitive sequence alignment algorithm on a parallel computer. The method achieves reliable alignments for very distantly related sequences from a combined use of amino acid exchange weights and physicochemical characteristics. The algorithm is computing intensive and its usage on conventional computers is limited to a relatively small number of sequences. The parallel implementation uses a Macintosh IIcx host computer and 21 transputers and achieves 22 times the speed of a VAX 8650 at a fraction of the cost. This paper describes the AliMac hardware and software and discusses problems and peculiarities of parallel implementations, especially with transputers. Finally, several popular sequence alignment algorithms are compared in their ability to detect distantly related sequences in searching large databases.

Algorithms↗

PubMed1990

A review of the CLIP system for the quantitative analysis of two-dimensional electrophoresis gels.

This paper reviews the CLIP image processing system for the complete analysis of two-dimensional electrophoresis images. The analysis problem for two-dimensional gel images can be broken down into three issues: segmentation of individual gel images, alignment and comparison of pairs of gel images, and information storage and retrieval. This paper describes these problems and reviews how the CLIP system handles each of them. Segmentation is the location and isolation of each protein spot on an individual gel image and also the extraction of individual spot data such as position, area and volume. There are three basic stages: background field correction, noise filtering, spot detection and information extraction. Alignment and comparison of gel images involves matching protein spots between two gels. This can be quite difficult because there is not a simple relationship which can transform one gel image onto another. The database issues concern storing all the information which has been obtained from the above operations such that retrieval of this information can be readily performed. The advantage of the CLIP system over others is speed of processing. CLIP series computers use one processor for every pixel of the camera image such that image processing algorithms run in parallel. The main disadvantage is in the cost of these machines. With the declining trend in the cost of parallel processors, these machines will become more and more viable alternatives. This papers reviews the algorithms for the analysis of two-dimensional gels. It is shown that CLIP is flexible enough to perform more than one type of algorithm for a particular operation.

Algorithms↗

PubMed1991

A parallel computing interface to facilitate development of physiologic signal processing algorithms.

We have created a system to aid in the development of algorithms related to the blood pressure waveform. The system performs all of the basic analyses of the waveform, and allows the user to utilize the results of those analyses for the algorithm under development. We have used a parallel programming architecture which keeps the different tasks, or processes, within the system independent of each other. Additionally, we have developed a graphics interface to enable the user to visualize the waveform, the system analysis, and the internal workings of the algorithm under development.

Algorithms↗

PubMed1992

[Single photon emission computed tomography by using fan beam collimator].

A multislice fan beam collimator which has parallel collimation along the cephalic-caudal axis of a patient and converging collimation within planes that are perpendicular to that axis was designed for a SPECT system with a rotating scintillation camera, and it was constructed by the lead casting method which was developed in recent years. A reconstruction algorithm for fan beam SPECT was formed originally by combining the reconstruction algorithm of the parallel beam SPECT with that of the fan beam X-ray CT. The algorithm for fan beam SPECT was confirmed by means of computer simulation and a head phantom filled with diluted radionuclide. Not only 99mTc but also 131I was used as a radionuclide. A SPECT image with the fan beam collimator was compared with that of a parallel hole, low energy, high resolution collimator which was routinely used for clinical and research SPECT studies. Both system resolution and sensitivity of the fan beam collimator were approximately 20% better than those of the parallel hole collimator. Comparing SPECT images obtained from fan beam collimator with those of parallel hole collimator, the SPECT images using fan beam collimator had far better resolution. A fan beam collimator is a useful implement for the SPECT study.

Algorithms↗

PubMed1992

Three-dimensional SPECT reconstruction of combined cone beam and parallel beam data.

Single photon emission computed tomography (SPECT) using cone beam (CB) collimation exhibits increased sensitivity compared with acquisition geometries using parallel (P) hole collimation. However, CB collimation has a smaller field-of-view which may result in truncated projections and image artifacts. A primary objective of this work is to investigate maximum likelihood-expectation maximization (ML-EM) methods to reconstruct simultaneously acquired parallel and cone beam (P&CB) SPECT data. Simultaneous P&CB acquisition can be performed with commercially available triple camera systems by using two cone-beam collimators and a single parallel-hole collimator. The loss in overall sensitivity (relative to the use of three CB collimators) is about 15 to 20%. We have developed three methods to combine P&CB data using modified ML-EM algorithms. The first method consists of using both data sets to reconstruct a single intermediate image after each iteration using the ML-EM algorithm. The other two iterative algorithms combine intermediate parallel beam (PB) and CB source estimates to enhance image quality. For these methods, a PB estimate and a CB estimate are obtained for the first iteration. The second method consists of summing the PB and CB estimates for each subsequent iteration to obtain new PB and CB estimates. The third method is similar to the second method, with the exception that the new PB estimate simply is set equal to the PB estimate after each iteration. The combined source estimate is used in each subsequent iteration step of the EM algorithm. These algorithms are evaluated using projection data simulated using a Monte Carlo SPECT model. The P&CB SPECT images demonstrate marked improvement as compared with the CB-only reconstruction, particularly when the projections are truncated.

Algorithms↗

PubMed1992

A parallel implementation of the backward error propagation neural network training algorithm: experiments in event identification.

An artificial neural-network-based (ANN) event detection and alarm generation system has been developed to aid clinicians in the identification of critical events commonly occurring in the anesthesia breathing circuit. To detect breathing circuit problems, the system monitored CO2 gas concentration, gas flow, and airway pressure. Various parameters were extracted from each of these input waveforms and fed into an artificial neural network. To develop truly robust ANNs, investigators are required to train their networks on large training data sets, requiring enormous computing power. We implemented a parallel version of the backward error propagation neural network training algorithm in the widely portable parallel programming language C-Linda. A maximum speedup of 4.06 was obtained with six processors. This speedup represents a reduction in total run-time from 6.4 to 1.5 h. By reducing the total run time of the computation through parallelism, we were able to optimize many of the neural network's initial parameters. We conclude that use of the master-worker model of parallel computation is an excellent method for speeding up the backward error propagation neural network training algorithm.

Algorithms↗

PubMed1992

A parallel implementation of a multi-state Kalman filtering algorithm to detect ECG arrhythmias.

Detecting arrhythmias from the electrocardiogram (ECG) is of great importance for the continued development of intelligent cardiovascular monitors (ICM). An ICM's main goal is to present to the clinician a 'high-level' analysis of the patient's condition (e.g., the patient is slightly hypovolemic) based upon 'low-level' physiologic signals (e.g., blood pressure, heart rate, etc.). This paper reports on a parallel implementation of a multi-state Kalman filtering algorithm, within a prototype ICM, to help detect ECG arrhythmias. Preliminary test results show that the parallel, multi-state implementation performed exactly as the original sequential version. Several different rhythm disturbances were correctly identified after 3-5 beats. We conclude that our parallel implementation of the multi-state Kalman filter provides a faster and still reliable means of accurately detecting ECG arrhythmias in real-time.

Algorithms↗

PubMed2026

MethylModes: computationally efficient detection of multimodal distributions in DNA methylation data.

SUMMARY: MethylModes is an R package and Shiny application to identify multimodal distributions in human DNA methylation at individual CpG sites. Multimodal distributions, which can be the result of nearby genetic variation, environmental exposures, or assay artifacts, are susceptible to confounding and important to identify for methylation analysis. MethylModes is easily incorporated into existing quality control pipelines of array-based DNA methylation data. The underlying algorithm uses kernel smoothing of probe-level data to locate the number and location of peaks. The algorithm can be parallelized across probes for efficient implementation at genome-scale. We provide a case study implementation of MethylModes in the Health and Retirement Study as well as the Airwave Health Monitoring Study. AVAILABILITY AND IMPLEMENTATION: MethylModes is available on GitHub at https://github.com/lutiffan/methylModes as an R package wrapping an R Shiny application. We include a toy dataset to validate installation. The codebase is also published on Zenodo at https://doi.org/10.5281/zenodo.17448517.

DNA Methylation↗

PubMed1991

Recognition of the folding consensus in RNA secondary structures by the topological-filtering method.

Functionally homologous RNA sequences can substantially diverge in their primary sequences but it can be reasonably assumed that they are related in their higher-degree structures. The problem to find such structures and simultaneously satisfy as far as possible the free-energy-minimization criterion, is considered here in two aspects. Firstly a quantitative measure of the folding consensus among secondary structures is defined, translating each structure into a linear representation and using the correlation theorem to compare them. Secondly an algorithm for the parallel search for secondary structures according to the free-energy-minimization criterion, but with a filtering action on the basis of the folding consensus measure is presented. The method is tested on groups of RNA sequences different in origin and in functions, for which proposals of homologous secondary structures based on experimental data exist. A comparison of the results with a blank consisting of a search on the basis of the free energy minimization alone is always performed. In these tests the method shows its ability in obtaining, from different sequences, secondary structures characterized by a high-folding consensus measure also when lower free energy but not homologous structures are possible. Two applications are also shown. The first demonstrates the transfer of experimental data available for one sequence, to a functionally related and therefore homologous one. The second application is the possibility of using a topological probe in the search for precise structural motifs.

Algorithms↗

PubMed1991

Evaluation of a parallel implementation of the learning portion of the backward error propagation neural network: experiments in artifact identification.

Various methods have been proposed in an attempt to solve problems in artifact and/or alarm identification including expert systems, statistical signal processing techniques, and artificial neural networks (ANN). ANNs consist of a large number of simple processing units connected by weighted links. To develop truly robust ANNs, investigators are required to train their networks on huge training data sets, requiring enormous computing power. We implemented a parallel version of the backward error propagation neural network training algorithm in the widely portable parallel programming language C-Linda. A maximum speedup of 4.06 was obtained with six processors. This speedup represents a reduction in total run-time from approximately 6.4 hours to 1.5 hours. We conclude that use of the master-worker model of parallel computation is an excellent method for obtaining speedups in the backward error propagation neural network training algorithm.

Algorithms↗

PubMed1992

Molecular dynamics simulation on a network of workstations using a machine-independent parallel programming language.

Molecular dynamics simulations investigate local and global motion in molecules. Several parallel computing approaches have been taken to attack the most computationally expensive phase of molecular simulations, the evaluation of long range interactions. This paper reviews these approaches and develops a straightforward but effective algorithm using the machine-independent parallel programming language, Linda. The algorithm was run both on a shared memory parallel computer and on a network of high performance Unix workstations. Performance benchmarks were performed on both systems using two proteins. This algorithm offers a portable cost-effective alternative for molecular dynamics simulations. In view of the increasing numbers of networked workstations, this approach could help make molecular dynamics simulations more easily accessible to the research community.

Algorithms↗

PubMedDate not supplied

Structure analysis and classification of cervical cells using a processing system based on TV.

This paper presents preliminary results of a cell classification experiment using a new approach for feature extraction. The algorithm takes into account the special requirements of a fast parallel processing system (processor-oriented algorithms). A cell image is described by several hundred features derived from the nucleus only. The most significant features with respect to classification are determined by statistical analysis. Applying principal axis transform, a new feature set is computed, reduced considerably in dimensions. The data base (1,925 cell images of Papanicolaou-stained cervical specimens) was divided into a training set (963 images) and a test set (962 images). The classification results of the test set show that the recognition rate for the two-class problem (normal, suspicious) is better than 91%, using only ten morphologic features.

Cervix Mucus↗

PubMed1992

Preverbal and verbal counting and computation.

We describe the preverbal system of counting and arithmetic reasoning revealed by experiments on numerical representations in animals. In this system, numerosities are represented by magnitudes, which are rapidly but inaccurately generated by the Meck and Church (1983) preverbal counting mechanism. We suggest the following. (1) The preverbal counting mechanism is the source of the implicit principles that guide the acquisition of verbal counting. (2) The preverbal system of arithmetic computation provides the framework for the assimilation of the verbal system. (3) Learning to count involves, in part, learning a mapping from the preverbal numerical magnitudes to the verbal and written number symbols and the inverse mappings from these symbols to the preverbal magnitudes. (4) Subitizing is the use of the preverbal counting process and the mapping from the resulting magnitudes to number words in order to generate rapidly the number words for small numerosities. (5) The retrieval of the number facts, which plays a central role in verbal computation, is mediated via the inverse mappings from verbal and written numbers to the preverbal magnitudes and the use of these magnitudes to find the appropriate cells in tabular arrangements of the answers. (6) This model of the fact retrieval process accounts for the salient features of the reaction time differences and error patterns revealed by experiments on mental arithmetic. (7) The application of verbal and written computational algorithms goes on in parallel with, and is to some extent guided by, preverbal computations, both in the child and in the adult.

Animals↗

PubMed1991

A parallel computing approach to genetic sequence comparison: the master-worker paradigm with interworker communication.

We have implemented a parallel version of a dynamic programming biological sequence comparison algorithm to study the potential applicability of using parallel computers for genetic sequence comparisons. Our parallel program is built using C-Linda, a machine-independent parallel programming language, and was tested on both a 10 CPU Sequent Symmetry and a 64 CPU Intel Hypercube. C-Linda implements a shared associative memory model, "tuple space," through which multiple processes can communicate and coordinate control. In our master-worker (MW) parallel implementation, a master process creates several worker processes, extracts a test sequence and multiple library sequences from a database and stores them in tuple space. Each worker reads the test sequence and then repeatedly extracts library strings from tuple space, performs pairwise sequence comparison using a local comparison algorithm to generate a similarity score, and returns the similarity scores to tuple space. The master collects the scores from tuple space and identifies the best match over all library sequences. We also implemented a method of global interworker communication to reduce the total search time by stopping those string comparisons that had no chance of improving on the current best match. Comparisons of the total run time, speedup, and efficiency were made for parallel and sequential versions of a basic MW implementation as well as versions with the global abort threshold.

Algorithms↗

PubMed1979

Utilization of cross-plane rays for three-dimensional reconstruction by filtered back-projection.

Present popular computed tomography (CT) algorithms reconstruct an object from the ray measurements lying on a set of parallel planes. This paper presents an algorithm that can also utilize "cross-plane" rays (i.e., rays that cross through many planes) to reconstruct the object. In this reconstruction algorithm, the ray measurements are grouped into two-dimensional projections, filtered, and stored. The filtered projections can then be back-projected onto a three-dimensional matrix or any plane through the three-dimensional volume. General theoretical aspects are presented and then applied to the special case in which ray measurements have been made in all directions. The algorithm is tested using computer-generated data. Expressions for the noise power spectrum and the variance in the reconstruction are derived. It is shown that the noise-to-signal ratio per detected photon for this reconstruction method is close to a theoretical limit, as it also is for normal CT. The ability to use ray measurements that cross many planes is especially useful in emission CT, where order-of-magnitude improvements in image quality per unit dose can be achieved.

Computers↗

PubMed1991

Fast space-filling molecular graphics using dynamic partitioning among parallel processors.

We present a novel algorithm for the efficient generation of high-quality space-filling molecular graphics that is particularly appropriate for the creation of the large number of images needed in the animation of molecular dynamics. Each atom of the molecule is represented by a sphere of an appropriate radius, and the image of the sphere is constructed pixel-by-pixel using a generalization of the lighting model proposed by Porter (Comp. Graphics 1978, 12, 282). The edges of the spheres are antialiased, and intersections between spheres are handled through a simple blending algorithm that provides very smooth edges. We have implemented this algorithm on a multiprocessor computer using a procedure that dynamically repartitions the effort among the processors based on the CPU time used by each processor to create the previous image. This dynamic reallocation among processors automatically maximizes efficiency in the face of both the changing nature of the image from frame to frame and the shifting demands of the other programs running simultaneously on the same processors. We present data showing the efficiency of this multiprocessing algorithm as the number of processors is increased. The combination of the graphics and multiprocessor algorithms allows the fast generation of many high-quality images.

Algorithms↗

PubMed1991

Molecular dynamics simulation on a network of workstations using a machine-independent parallel programming language.

Molecular dynamics simulations investigate local and global motion in molecules. Several parallel computing approaches have been taken to attack the most computationally expensive phase of molecular simulations, the evaluation of long range interactions. This paper develops a straightforward but effective algorithm for molecular dynamics simulations using the machine-independent parallel programming language, Linda. The algorithm was run both on a shared memory parallel computer and on a network of high performance Unix workstations. Performance benchmarks were performed on both systems using two proteins. This algorithm offers a portable cost-effective alternative for molecular dynamics simulations. In view of the increasing numbers of networked workstations, this approach could help make molecular dynamics simulations more easily accessible to the research community.

Algorithms↗