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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.

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.

Currently, information technology is used in all the life domains. Many devices and equipment produce data and transfer them across the network. These transfers are not always secured and can contain new menaces and attacks invisible by the current security tools. Moreover, the large amount and variety of the exchanged data make the identification of the intrusions more difficult in terms of detection time. To solve these issues, we suggest in this paper, a new approach based on storing the large amount and variety of network traffic data employing big data techniques, and analyzing these data with machine learning algorithms, in a distributed and parallel way, in order to detect new hidden intrusions with less processing time. According to the results of the experiments, the detection accuracy of the machine learning methods reaches up to 99.9%, and their processing time has been reduced considerably by applying them in a parallel and distributed way, which proves that our proposed model is very effective for the detection of new hidden intrusions.

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 treeClusts 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 treeClusts existing parallel processing method does not work on the Naval Postgraduate Schools 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.

This report summarizes the progress, accomplishments, and status of our effort on the study of optical methods for parallel access optical memory. The main objectives of this project are to develop important optical amplification techniques to increase optical energy efficiency and novel optical interconnection and/or processing techniques to increase the versatility of new parallel access optical memory concepts. The goal is to relieve the electronic cache bottleneck and help realize high net data transfer rates.

The objective of this research is to provide a design methodology for connecting heterogeneous hardware modules that have inherently different functional and timing behavior. With the constraints dictated by the system- level interaction, we need to adopt a modular design approach without compromising the global performance. The main task of this effort will be the development of the theory for optimal interface circuits synthesis from high- level specification, with emphasis on testability and performance. In July-Dec 1991 we have concentrated on the gate-level synthesis of asynchronous control circuits and introducing timing information into the synthesis process to increase circuit performance.

This Memorandum describes work done in mapping image processing algorithms on to transputer arrays. Object classification based on the autocorrelation function has been applied to infrared images. Using the OCCAM programming language, parallel processing techniques have been developed to make use of the concurrency available for communication and calculation. The results for this specific problem are assessed and conclusions drawn on the use of transputers and OCCAM for parallel processing.

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)

During this quarter we have finalized the low to intermediate level interfac hardward design for the IUA. We have also been examining various network topologies for communications within the intermediate level of the IUA. At the minimum we will support a mesh with an ouverlayed hypercube However, we are looking into a fully programable network topology that will allow us to experiment with different processor interconnection schemes. Keywords: Image understanding algorithm; Computer architecture; Hough transform; Convex hull; Voronoi diagram; 3-D models.

This study developed a set of low level image processing on a parallel computer that allows concurrent processing of images in order to support development of systems that use multiple images to gather information. The parallel computer used is a collection of powerful processors connected in a hypercube topology. The software developed simplifies the interface between the parallel computer and the applications developer by providing a library of functions in the C programming language. These functions are used to control 'image processor programs' that run independently in the parallel computer and perform the image processing operations. The complexities of the parallel processor are hidden and replaced with a flexible structure specifically designed for image processing. This structure provides a simplified interface, but also acts as a framework to which additional image processing operations can be added. Keywords: Image processing, Convolution, Parallel processing.

The increase in dynamic average path length (DAPL- with network size is moderate while it is significantly less than log(2)N, the number of stages needed in a MIN. The best performance in the case of no fault and single fault, is obtained for the modified Omega and the Zeta SSINs. Keywords include: Interconnection networks, Switches, and Parallel processing networks.

This report proposes to use generalized stochastic Petri nets (GSPN) in reliability, maintainability, and availability studies. The novelty of this modeling approach lies on the ground of the following distinctive reasons: (1) The GSPN offers a precise description of system activities and conditions while involves less complexity, comparing to other modeling techniques. Specifically, it is an inherently effective bookkeeping for conditions and activities; (2) it provides a clairvoyant insight of the key parameters that effect RMA analysis. Causes and results of events can be easily tracked by executing the GSPN; and (3) it takes the advantage of the existence of concurrency and timing of events, thus describes accurately the sequence of events.

The objective of this research is to provide an interconnect synthesis methodology which facilitates a modular design approach without compromising the global performance. The main tasks of this effort will be the development of the theory for optimal interconnect circuit synthesis from a high-level specification, with emphasis on testability and fault-tolerance asynchronous interface among concurrently computing hardware, and the application of this design methodology to physical implementations of parallel processing systems. Our major research effort has been directed toward the testability of asynchronous circuits. Testability has become a major design consideration in integrated circuit industry and the question that whether asynchronous circuits would simplify the testing tasks is to be answered. Asynchronous combinational circuits had been shown to be fully testable with single-stuck-at-faults (SSAFs) if precharged circuits were used for implementation; however, the testability of asynchronous control circuits (sequential circuits with environmental constraints) had not been addressed. Level-sensitive scan-path design can be used to aid testing in asynchronous sequential circuits, but scan-path design usually requires too long a testing day. We are most interested in incorporating testability directly into circuit synthesis. We considered the problem of testability for two areas of asynchronous design: speed-independent circuits and self-timed circuits.

In recent years, significant progress has been made towards achieving autonomous roadway navigation using video images. None of these systems take full advantage of all the information in the 512x512 pixel frame, 30 frame/ second color image sequence. This can be attributed to the large amount of data which is present in the color video image stream (22.5 Mbytes/sec) as well as the limited amount of computing resources available to the systems. We have increased the available computing power by using a parallel computer. Specifically, a single instruction, multiple data (SIMD) machine was used to develop simple and efficient parallel algorithms, largely based on connectionist techniques, which can process every pixel in the incoming 30 frame/second, color video image stream. The system uses substantially larger frames and processes them at faster rates than other color road following systems. This is achievable through the use of algorithms specifically designed for a fine-grained parallel machine as opposed to ones ported from existing systems to parallel architectures. The algorithms presented here were tested on 4k and 16k processor MasPar MP-1 and on 4K, 8K, and 16K processor MasPar MP-2 parallel machines and were used to drive Carnegie Mellon's testbed vehicle, the Navlab I, on paved roads near campus.

PSRDP: A Parallel Processing Method for Pulsar Baseband Data 作者： Ya-Zhou Zhang, Hai-Long Zhang, Jie Wang, Xin-Chen Ye, Shuang-Qiang Wang, Xu Du, Han Wu, Ting Zhang, Shao-Cong Guo and Meng Zhang 1 作者单位： 1. Ya-Zhou Zhang, Hai-Long Zhang, Jie Wang, Xin-Chen Ye, Shuang-Qiang Wang, Xu Du, Han Wu, Ting Zhang, Shao-Cong Guo and Meng Zhang 提交时间： 2024-02-01 14:50:09 摘要: To address the problem of real-time processing of ultra-wide bandwidth pulsar baseband data, we designed and implemented a pulsar baseband data processing algorithm (PSRDP) based on GPU parallel computing technology. PSRDP can perform operations such as baseband data unpacking, channel separation, coherent dedispersion, Stokes detection, phase and folding period prediction, and folding integration in GPU clusters. We tested the algorithm using the J0437-4715 pulsar baseband data generated by the CASPSR and Medusa backends of the Parkes, and the J0332+5434 pulsar baseband data generated by the self-developed backend of the NanShan Radio Telescope. We obtained the pulse profiles of each baseband data. Through experimental analysis, we have found that the pulse profiles generated by the PSRDP algorithm in this paper are essentially consistent with the processing results of Digital Signal Processing Software for Pulsar Astronomy (DSPSR), which verified the effectiveness of the PSRDP algorithm. Furthermore, using the same baseband data, we compared the processing speed of PSRDP with DSPSR, and the results showed that PSRDP was not slower than DSPSR in terms of speed. The theoretical and technical experience gained from the PSRDP algorithm research in this article lays a technical foundation for the real-time processing of QTT (Qi Tai radio Telescope) ultra-wide bandwidth pulsar baseband data. 期刊： Research in Astronomy and Astrophysics 分类： 物理学 >> 地球物理学、天文学和天体物理学 投稿状态： 已在期刊出版 引用： ChinaXiv:202402.00040 (或此版本 ChinaXiv:202402.00040V1 ) DOI:https://doi.org/10.1088/1674-4527/ad0e99 CSTR:32003.36.ChinaXiv.202402.00040.V1 推荐引用方式： Ya-Zhou Zhang, Hai-Long Zhang, Jie Wang, Xin-Chen Ye, Shuang-Qiang Wang, Xu Du, Han Wu, Ting Zhang, Shao-Cong Guo and Meng Zhang.(2024).PSRDP: A Parallel Processing Method for Pulsar Baseband Data.Research in Astronomy and Astrophysics.doi:https://doi.org/10.1088/1674-4527/ad0e99 版本历史 [V1] 2024-02-01 14:50:09 ChinaXiv:202402.00040V1 下载全文

Advances in device and packaging technologies are producing incremental gains in the performance of computer systems. However, these gains are being more than offset by new applications having a need to process large data sets, a need real-time computation, or other requirements which make them prohibitively expensive to perform on conventional computer systems. This has forced computer architects to consider parallel/distributed computer designs. In Part I of this thesis, the strengths and weaknesses of a variety of existing and proposed parallel computer designs are examined. These characteristics are used to motivate and support the design decisions made for a particular parallel computer architecture, PASM. It is PASM that is the primary focus of the work in this thesis.

Given the massively parallel nature of the brain an obvious question is why are so many information processing functions serial? In particular, this paper addresses the issue of the comparison process. Behavioral data show that in perceptual matching tasks (such as memory scanning and visual search) performance is systematically affected by stimulus load, in that required processing time increases with each additional comparison item. It is arguable whether this indicates a processing system that performs serial comparisons, or a system for which comparisons are done in parallel but reaction time is affected by load because of other system limitations. In this simulation we show that in a modular connectionist system vector transmission is possible in parallel, but the comparison process within a module must be done serially unless accuracy is sacrificed.

One approach to parallel digital signal processing decomposes a high bandwidth signal into multiple lower bandwidth (rate) signals by an analysis bank. After processing, the subband signals are recombined into a fullband output signal by a synthesis bank. This paper describes an implementation of the analysis and synthesis banks using (Field Programmable Gate Arrays) FPGAs.

Recently, networked and cluster computation have become very popular for both signal processing and system simulation. A new language is ideally suited for parallel signal processing applications and system simulation since it allows the programmer to explicitly express the computations that can be performed concurrently. In addition, the new C based parallel language (ace C) for architecture-adaptive programming allows programmers to implement algorithms and system simulation applications on parallel architectures by providing them with the assurance that future parallel architectures will be able to run their applications with a minimum of modification. In this paper, we will focus on some fundamental features of ace C and present a signal processing application (FFT).

This report presents the results of research on the allocation of binary cartesian product database files across several stores (such as disks) which are accessible in parallel so that the average time required to perform the retrieval (by using several parallel accesses) of all buckets specified by a partial-match query is objective of constructing efficient packings of Hamming spheres in hypercubes. The viewpoint of binary cartesian product file allocation thus produced allowed in our research the creation of allocations for two particular sizes of the set of stores such that the average processing time for partial-match queries was smaller than that of the best allocation technique found in the literature for these same two cases. It also provides a framework in which good allocations for other particular sizes of the set of stores, and possibly for general sets of stores, might be found.

This report presents the results of research on the allocation of binary cartesian product database files across several stores (such as disks) which are accessible in parallel so that the average time required to perform the retrieval (by using several parallel accesses) of all buckets specified by a partial-match query is objective of constructing efficient packings of Hamming spheres in hypercubes. The viewpoint of binary cartesian product file allocation thus produced allowed in our research the creation of allocations for two particular sizes of the set of stores such that the average processing time for partial-match queries was smaller than that of the best allocation technique found in the literature for these same two cases. It also provides a framework in which good allocations for other particular sizes of the set of stores, and possibly for general sets of stores, might be found.

Large industrial systems that combine services and applications, have become targets for cyber criminals and are challenging from the security, monitoring and auditing perspectives. Security log analysis is a key step for uncovering anomalies, detecting intrusion, and enabling incident response. The constant increase of link speeds, threats and users, produce large volumes of log data and become increasingly difficult to analyse on a Central Processing Unit (CPU). This paper presents a massively parallel Graphics Processing Unit (GPU) LOg Processing (GLoP) library and can also be used for Deep Packet Inspection (DPI), using a prefix matching technique, harvesting the full power of off-the-shelf technologies. GLoP implements two different algorithm using different GPU memory and is compared against CPU counterpart implementations. The library can be used for processing nodes with single or multiple GPUs as well as GPU cloud farms. The results show throughput of 20Gbps and demonstrate that modern GPUs can be utilised to increase the operational speed of large scale log processing scenarios, saving precious time before and after an intrusion has occurred.

Thesis advisor, Douglas J. Fouts

This project consists of two parts: applications of wavelet transforms in multiscale image processing, and parallel algorithms and architectures. We have studied issues in wavelet-based edge detection: antisymmetry of wavelet filters and their support size with respect to edge localization, and a non-orthogonal four-coefficient wavelet edge detector. Texture segmentation using a modulated Daubechies wavelet has been studied, providing both spatial frequency and orientation selectivity. Object segmentation has been studied by examining silhouettes at multiple resolutions in piecewise linear approximations, and has been explored on LADAR data for use in target recognition. Applications to biomedical image compression, image halftoning and artificial neural network structure have also been investigated. In parallel processing, we have studied embeddings of wavelet transform algorithms as well as other algorithms for 2D and 3D mesh architectures and systolic arrays, and their time complexities have been evaluated. A variety of issues are addressed in utilization of 3D meshes in parallel image processing on 2D and 3D images including: the embedding of 2D images into 3D meshes, 3D shrinking incorporating subfields methodology, 3D connected component labeling, and segmentation in magnetic resonance imaging. Some fundamental issues for parallel reduction and reduction-augmentation operators in 2D and 3D image spaces have been examined.

The software development process for parallel processors is investigated with a focus on real-time sensor processing applications. Concepts that are relevant to real-time parallel processing are introduced including: a definition of scalable dataflow graphs motivated by the need to meet a fixed throughput constraint for varying problem sizes, an algorithm classification that makes explicit the impact that data dependencies have in real-time implementations, and a real-time implementation strategy that decomposes the most problematic algorithms into compositions of more predictable constituents and then uses scalable dataflow graphs and parallel processing to recover timing predictability by mapping data dependent timing uncertainties into the spatial dimension (processors). Two case studies apply these ideas: an implementation of the Modified Gram-Schmidt (MGS) algorithm on a MasPar MP1 and an implementation of the joint probabilistic data association (JPDA) algorithm on a Thinking Machines CM-2. The JPDA case study includes a SISAL implementation to illustrate the advantages of functional programming for these applications. Parallel processors, Scalable dataflow graphs, Algorithms.

Simulations: Processing of chaos and memory storage. In view of our previously published findings showing that motor patterns represent adaptive behaviors may be generated by chaotic activity, we have used computer simulations to examine the ability of simple networks to learn to process chaotic signals and to perform complex operations on them. These studies have shown that even simple networks can be used to understand how networks store information, much of which information can not have been obtained from the more complex biological systems. As one example, an important and unexpected finding is that networks having trainable thresholds, in addition to trainable synapses, can performs computations that trainable synapses alone can not, regardless of the number synapses that may be included in the network. Another finding is that when networks must learn several tasks simultaneously, the effective size network is self-limiting, and probably does not require special algorithmic rules for limiting the size of successfully computing neural connections.

Direct numerical simulations of separated-reattaching and separated flows have been performed on massively parallel processing computers. Two basic geometrical configurations have been studied: the separated-reattaching flow past a normal flat plate with an attached downstream splitter plate and the separated flow past a flat plate held normal to a uniform stream. A high-order finite-difference formulation on collocated grids has been developed to perform unsteady fluid flow simulations in rectangular geometries. The numerical procedure is based on a fifth-order upwind-biased scheme for the convective terms and a fourth-order accurate stencil for the diffusive terms. A direct solver based on eigenvalue decomposition has been developed for the pressure-Poisson equation. A mixed Fourier spectral /finite difference formulation is used for the spanwise discretization, and a data-parallel algorithm has been developed for the CM-5. The performance of the algorithm has been evaluated on various grid sizes in model flow problems and for different partition sizes. The characteristics of the separated-reattaching flow have been investigated through two-dimensional simulations in the steady and unsteady regimes. The shedding mechanism is characterized by two major modes at Re = 250 and a single mode at Re = 375 and 500. Further, the instability of the separated shear layer is found to be consistent with inviscid theory. For the two-dimensional study of the separated flow past a normal flat plate, the time-mean flow quantities are observed to be over-estimated compared to the experiments. The time-mean drag coefficient is also over-predicted by a factor of up to 2. This is attributed to the high coherence of the vortices predicted by the two-dimensional simulations

Comparison of biological (DNA or protein) sequences provides insight into molecular structure, function, and homology, and is increasingly important as the available data bases become larger and more numerous. One method of increasing the speed of the calculations is to perform them in parallel. We present the results of initial investigations using the Intel iPSC hypercube and the Connection Machine for these comparisons. Since these machines have very different architectures, the issues and performance trade-offs discussed have a wide applicability for the parallel processing of biological sequence comparisons.

This research was conducted to obtain better methods for image processing. It focused on several aspects of this problem, including parallel algorithms for image processing, knowledge-based techniques for image understanding, and modeling images using shape and texture. Eighteen technical reports were produced which will also appear as published papers in journals. In the paper Holes and Genus of 3D images, it was shown that certain geometric invariants of a digital image (number of components, number of holes, and number of cavities) do not determine the topology (in the sense of connectivity) of the image refuting the commonly believed assumption that they do. This research lays the groundwork for research on digital and computational geometry of 3D images. In the paper Hough Transform Algorithms for Mesh-Connected for Mesh-Connected SIMD Parallel Processors, several methods of Hough transform computation are studied in terms of suitability for implementation on a parallel processor, providing a valuable tool for straight-line detection.

The Final Proceedings for VECPAR 983rd International Meeting on Vector and Parallel Processing, 21 June 1998 -23 June 1998 This is an interdisciplinary conference. Topics include parallel and distributed computing, image processing and synthesis. mal-time and embedded systems.

The Final Proceedings for VECPAR, 4th International Meeting on Vector and Parallel Processing, 21-23 June 2000, an interdisciplinary conference covering topics in all areas of vector, parallel and distributed computing applied to a broad range of research disciplines with a focus on engineering. The principal topics include: Cellular Automata, Computational Fluid Dynamics, Crash and Structural Analysis, Data Warehousing and Data Mining, Distributed Computing and Operating Systems, Fault Tolerant Systems, Imaging and Graphics, Interconnection Networks, Languages and Tools, Numerical Methods, Parallel and Distributed Algorithms, Real-time and Embedded Systems, Reconfigurable Systems, Linear Algebra Algorithms and Software for Large Scientific Problems, Computer Organization, Image Analysis and Synthesis, and Nonlinear Problems.

We analyze the effects of hardware faults on the performance of computer-implemented signal detectors, as measured by the probability of detection and the probability of false alarm. We then use these results to design fault-tolerant detectors using hardware redundancy. Keywords include. Fault tolerance, Signal Processing, and Parallel Architecture.

A simple ``brute-force'' parallelisation procedure for the computational implementation of high-order coupled cluster method (CCM) calculations is presented here. This approach is investigated and illustrated by an application of high-order CCM to the Heisenberg antiferromagnet on the Kagom\'e lattice with nearest- and next-nearest-neighbour bonds. Ferromagnetic next-nearest-neighbour bonds are used to stabilise a model state which contains three sublattices in which the spins make angles of 120$^{\circ}$ to each other. Ground-state results for up to approximately 10000 fundamental clusters are presented, and our best estimate for the ground-state energy per spin of the spin-half Kagom\'e lattice antiferromagnet with only nearest-neighbour bonds is $E_g/N = -0.43104$. We believe that further increases (of at least another order of magnitude) in the number of fundamental clusters might be possible in future by using parallel processing techniques. The extension of high-order CCM calculations in order to consider non-N\'eel (e.g., dimer solid) model states, simulation of excitation spectra, lattice boson and fermion models, and finite-sized systems is very briefly considered.

A simple ``brute-force'' parallelisation procedure for the computational implementation of high-order coupled cluster method (CCM) calculations is presented here. This approach is investigated and illustrated by an application of high-order CCM to the Heisenberg antiferromagnet on the Kagom\'e lattice with nearest- and next-nearest-neighbour bonds. Ferromagnetic next-nearest-neighbour bonds are used to stabilise a model state which contains three sublattices in which the spins make angles of 120$^{\circ}$ to each other. Ground-state results for up to approximately 10000 fundamental clusters are presented, and our best estimate for the ground-state energy per spin of the spin-half Kagom\'e lattice antiferromagnet with only nearest-neighbour bonds is $E_g/N = -0.43104$. We believe that further increases (of at least another order of magnitude) in the number of fundamental clusters might be possible in future by using parallel processing techniques. The extension of high-order CCM calculations in order to consider non-N\'eel (e.g., dimer solid) model states, simulation of excitation spectra, lattice boson and fermion models, and finite-sized systems is very briefly considered.

A simple ``brute-force'' parallelisation procedure for the computational implementation of high-order coupled cluster method (CCM) calculations is presented here. This approach is investigated and illustrated by an application of high-order CCM to the Heisenberg antiferromagnet on the Kagom\'e lattice with nearest- and next-nearest-neighbour bonds. Ferromagnetic next-nearest-neighbour bonds are used to stabilise a model state which contains three sublattices in which the spins make angles of 120$^{\circ}$ to each other. Ground-state results for up to approximately 10000 fundamental clusters are presented, and our best estimate for the ground-state energy per spin of the spin-half Kagom\'e lattice antiferromagnet with only nearest-neighbour bonds is $E_g/N = -0.43104$. We believe that further increases (of at least another order of magnitude) in the number of fundamental clusters might be possible in future by using parallel processing techniques. The extension of high-order CCM calculations in order to consider non-N\'eel (e.g., dimer solid) model states, simulation of excitation spectra, lattice boson and fermion models, and finite-sized systems is very briefly considered.

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]

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]

This article is from Sensors (Basel, Switzerland) , volume 9 . Abstract In this study, a parallel processing method using a PC cluster and a virtual grid is proposed for the fast processing of enormous amounts of airborne laser scanning (ALS) data. The method creates a raster digital surface model (DSM) by interpolating point data with inverse distance weighting (IDW), and produces a digital terrain model (DTM) by local minimum filtering of the DSM. To make a consistent comparison of performance between sequential and parallel processing approaches, the means of dealing with boundary data and of selecting interpolation centers were controlled for each processing node in parallel approach. To test the speedup, efficiency and linearity of the proposed algorithm, actual ALS data up to 134 million points were processed with a PC cluster consisting of one master node and eight slave nodes. The results showed that parallel processing provides better performance when the computational overhead, the number of processors, and the data size become large. It was verified that the proposed algorithm is a linear time operation and that the products obtained by parallel processing are identical to those produced by sequential processing.

This article is from Sensors (Basel, Switzerland) , volume 9 . Abstract In this study, a parallel processing method using a PC cluster and a virtual grid is proposed for the fast processing of enormous amounts of airborne laser scanning (ALS) data. The method creates a raster digital surface model (DSM) by interpolating point data with inverse distance weighting (IDW), and produces a digital terrain model (DTM) by local minimum filtering of the DSM. To make a consistent comparison of performance between sequential and parallel processing approaches, the means of dealing with boundary data and of selecting interpolation centers were controlled for each processing node in parallel approach. To test the speedup, efficiency and linearity of the proposed algorithm, actual ALS data up to 134 million points were processed with a PC cluster consisting of one master node and eight slave nodes. The results showed that parallel processing provides better performance when the computational overhead, the number of processors, and the data size become large. It was verified that the proposed algorithm is a linear time operation and that the products obtained by parallel processing are identical to those produced by sequential processing.

The intent of this thesis is to answer the question as to whether real time battlefield visualization, once requiring high speed UNIX workstations and specialized parallel processors, can now be now performed on relatively inexpensive off the shelf components. Alternative network topologies were implemented using 10 and 100 megabit-per-second Ethernet cards under the Linux operating system on Pentium based personal computer platforms. Network throughput, processor and video performance benchmark routines were developed to assess the hardware's potential for parallel application in a distributed environment. Code was first ported to the Linux environment. Benchmark routines were then developed and tested on various machines. Dual 200 MHz Pentium Pro processor performance exceeded the dual processor 50 MHz SUN and 40 MHz SGI UNIX workstations currently used for terrain generation by a factor of 30 using a simple ray trace algorithms as a basis for comparison. The Intel Pentium Pro personal computer proved to be a capable platform for generating six to ten frame-per-second terrain simulations. However, Fast Ethernet throughput averages only 2.5 megabytes-per-second, thereby limiting the usefulness of a distributed approach designed to increase performance by dividing workload across the network.

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.

Available from National Technical Information Service, Springfield, Va

Available from National Technical Information Service, Springfield, Va

In this paper, we study the communication complexity for the problem of computing a conjunctive query on a large database in a parallel setting with $p$ servers. In contrast to previous work, where upper and lower bounds on the communication were specified for particular structures of data (either data without skew, or data with specific types of skew), in this work we focus on worst-case analysis of the communication cost. The goal is to find worst-case optimal parallel algorithms, similar to the work of [18] for sequential algorithms. We first show that for a single round we can obtain an optimal worst-case algorithm. The optimal load for a conjunctive query $q$ when all relations have size equal to $M$ is $O(M/p^{1/\psi^*})$, where $\psi^*$ is a new query-related quantity called the edge quasi-packing number, which is different from both the edge packing number and edge cover number of the query hypergraph. For multiple rounds, we present algorithms that are optimal for several classes of queries. Finally, we show a surprising connection to the external memory model, which allows us to translate parallel algorithms to external memory algorithms. This technique allows us to recover (within a polylogarithmic factor) several recent results on the I/O complexity for computing join queries, and also obtain optimal algorithms for other classes of queries.

A study was made of the emerging research area of algorithmic fault tolerance. This topic is attracting lots of attention due to its low implementation overhead and to its suitability for very large scale integration multiprocessor arrays. A fundamental contribution was made in the area by providing a novel linear algebraic model for the technique. In addition, a new fault tolerance scheme was developed for recursive least squares minimization. Other results include new ways for generating weights for the checksum method. (kr)

Includes bibliographical references (p. 391-393) and indexes