Single-processor Computer Research Articles

ОПТИМІЗАЦІЯ ОДНОПРОЦЕСОРНОГО ОБРОБЛЕННЯ МУЛЬТИЗАПИТІВ

Background. The effectiveness of multi-query execution in single-processor computer system databases is considered. One of the methods of increasing the performance of databases of computer systems is the simultaneous execution of several queries that form a multi-query. Methods. Methods of analysis and criterial optimization are used in the paper. Results. The paper analyzes the processing of a conjunctive multi-query (queries are formed by the conjunction of elementary queries, from which a number of elementary queries are repeatedly included in the queries). With the growing importance of operational analytical data processing, the technique of more complex query optimizations has become crucial. In order to be effective, optimizers must adapt to new operators, changes in cost estimation methods, etc. The query execution time is usually used as a criterion for multi-query optimization, while subdividing the time spent working with data in RAM, buffer, and external memory. An optimization plan for conjunctive multi-query execution time when accessing the database based on the ordering of elementary queries is proposed. Two methods of performing a conjunctive multiquery are considered: independently of each other and jointly for unordered and ordered data. It is shown that the execution time of a conjunctive multi-query with joint processing is not always less than with independent processing. The conditions under which the joint processing of a conjunctive multi-query ensures no longer execution time in relation to independent processing are proved. Сonclusions. Implementation of the proposed optimization procedure allows one to increase efficiency of single-processor computer systems that support data bases.

Variable Selection Using aModified Gibbs Sampler Algorithm with Application on Rock Strength Dataset

Variable selection is an essential and necessary task in the statistical modeling field. Several studies have triedto develop and standardize the process of variable selection, but it isdifficultto do so. The first question a researcher needs to ask himself/herself what are the most significant variables that should be used to describe a given dataset’s response. In thispaper, a new method for variable selection using Gibbs sampler techniqueshas beendeveloped.First, the model is defined, and the posterior distributions for all the parameters are derived.The new variable selection methodis tested usingfour simulation datasets. The new approachiscompared with some existingtechniques: Ordinary Least Squared (OLS), Least Absolute Shrinkage and Selection Operator (Lasso), and Tikhonov Regularization (Ridge). The simulation studiesshow that the performance of our method is better than the othersaccording to the error and the time complexity. Thesemethodsare applied to a real dataset, which is called Rock StrengthDataset.The new approach implemented using the Gibbs sampler is more powerful and effective than other approaches.All the statistical computations conducted for this paper are done using R version 4.0.3 on a single processor computer.

Enhancing Parallel Scheduling of Grid Jobs in a Multicored Environment

The computing Grid has emerged as a platform to solve the complex and ever-increasing processing need of man and advances in computing technology have birthed the multicore era aimed for high throughput and efficient parallel computing. However, most systems still rely on the underlying hardware for parallelism despite the hard evidence that sequential algorithms do not optimally exploit parallel systems. This research seeks to harness the benefits of multicore systems using job and machine grouping methods to enhance parallelism in the scheduling of Grid jobs. The paper presents the result of two separate experiments on a method that parallelize scheduling algorithm on two multicore platforms. An arbitrary method was employed to group machines; a summation of the total processing power of machines in each group was made. To ensure load balancing, jobs were allocated to machine groups based on the ratio of the total processing power of the machines in each group. The MinMin Grid scheduling algorithm was implemented independently within the groups using a range of threads varied in powers of two. Also, the numbers of groups were varied between 2, 4, and 8. The same experiment was executed on a single processor computer; a duocore machine and a quadcore machine. A performance improvement of 16% to 85% was recorded by the group method against the best ordinary MinMin results and an improvement of 50% to 84% was recorded by the group method against the ordinary MinMin on corresponding machines. We prove that an increase in the number of groups results in improved performance on corresponding machines (approximately 2 times using 2 groups, approximately 3 times using four groups, and approximately 6 times using 8 groups). And most importantly, we established that as the number of processors increases, the grouping method makes more significant improvements over the ordinary MinMin scheduling algorithm executed on the multicore systems.

Model calibration using a parallel differential evolution algorithm in computational neuroscience: Simulation of stretch induced nerve deficit

Neuronal damage, in the form of both brain and spinal cord injuries, is one of the major causes of disability and death in young adults worldwide. One way to assess the direct damage occurring after a mechanical insult is the simulation of the neuronal cells functional deficits following the mechanical event. In this study, we use a coupled electrophysiological-mechanical model with several free parameters that are required to be calibrated against experimental results. The calibration is carried out by means of an evolutionary algorithm (differential evolution, DE) that needs to evaluate each configuration of parameters on six different damage cases, each of them taking several minutes to compute. To minimise the simulation time of the parameter tuning for the DE, the stretch of one unique fixed-diameter axon with a simplified triggering process is used to speed up the calculations. The model is then leveraged for the parameter optimization of the more realistic bundle of independent axons, an impractical configuration to run on a single processor computer. To this end, we have developed a parallel implementation based on OpenMP that runs on a multi-processor taking advantage of all the available computational power. The parallel DE algorithm obtains good results, outperforming the best effort achieved by published manual calibration, in a fraction of the time. While not being able to fully capture the experimental results, the resulting nerve model provides a complex averaging framework for nerve damage simulation able to simulate gradual axonal functional alteration in a bundle.

A java application to characterize biomolecules and nanomaterials in electrolyte aqueous solutions

The electrostatic, entropic and surface interactions between a macroion (nanoparticle or biomolecule), surrounding ions and water molecules play a fundamental role in the behavior and function of colloidal systems. However, the molecular mechanisms governing these phenomena are still poorly understood. One of the major limitations in procuring this understanding is the lack of appropriate computational tools. Additionally, only experts in the field with an extensive background in programming, who are trained in statistical mechanics, and have access to supercomputers are able to study these systems. To overcome these limitations, in this article, we present a free, multi-platform, portable Java software, which provides experts and non-experts in the field an easy and efficient way to obtain an accurate molecular characterization of electrical and structural properties of aqueous electrolyte mixture solutions around both cylindrical- and spherical-like rigid macroions under multiple conditions. These properties include the normalized ions and water density profile distributions, the mean electrostatic potential, the integrated charge, the zeta potential, the electrostatic potential energy, the particle crowding entropy energy, the ion–ion electrostatic direct correlation energy, and the ionic potential of mean force. The Java software does not require outstanding skills and comes with detailed user-guide documentation. The application is based on the so-called Classical Density Functional Theory Solver (CSDFTS), which was successfully applied to a variety of rod-like biopolymers, rigid-like globular proteins, nanoparticles, and nano-rods. CSDFTS implements several electrolyte and macroion models, uses different levels of approximation and takes advantage of high performance Fortran90 routines and optimized libraries. These features enable the software to run on single processor computers at low-to-moderate computational cost depending on the computer performance, the grid resolution, and the characterization of the macroion and the electrolyte solution, among other factors. As a unique feature, the software comes with a graphical user interface (GUI) that allows users to take advantage of the visually guided setup of the required input data to properly characterize the system and configure the solver. Several examples on nanomaterials and biomolecules are provided to illustrate the use of the GUI and the solver performance. Program summaryProgram Title: CSDFTSProgram Files doi:http://dx.doi.org/10.17632/4t8ybf93d5.1Licensing provisions: GPLv2.Programming language: Java 1.8.External routines/libraries: GNU gawk installation is required if the user wants to calculate the protein volume using the 3v application.Nature of problem: A rich and complex, yet not fully understood, electrical double layer (EDL) formation arises when a nanoparticle or biomolecule is immersed in a liquid solution.Solution method: The Java application is based on the Classical Density Functional Theory Solver (CSDFTS).Additional comments including unusual features: The software incorporates a graphical user interface which eliminates the arduous and error-prone manual entry of data, and substantially reduces the time spent on the setup of the information required to characterize the macroion and the electrolyte solution. Additionally, each GUI screen provides helpful information about how to fill out the input data by simply holding the mouse pointer over the corresponding text or blank box. the GUI tests all the input data before running the CSDFTS to avoid the incorrect use of the software and prevent meaningless results.The GUI requires a variety of the user’s computer applications, which are a part of the basic operating systems. The GUI uses “Activity Monitor”, “procexp.exe”, and “gnome-system-monitor” (or “top”) applications for Mac, Windows and Linux users, respectively, to monitor the user’s computer performance. It also uses “Terminal.app”, “cmd.exe” and “gnome-terminal” (or “xterm”) for Mac, Windows, and Linux users, respectively, to display the CSDFT calculations.

Efficient Techniques for Solution of Complex Computational Tasks in Building Physics

Various simplification or optimization techniques are sought that reduce demands of computational modeling on time or computing power while keeping a sufficient level of accuracy. In this paper, determination of hygrothermal performance of a brick block is presented using two homogenization techniques based on different principles. While the computational homogenization technique uses a multiscale method realized on the master/slave computer system, the materials homogenization comes out from the effective media theory (EMT), and after the determination of effective material properties, the whole isotropic problem can be transformed to one dimension. Contrary to most applications of EMT, free parameters of mixing formulas are not determined based on an experimental measurement of a single material property but on a complex hygrothermal performance of the element where the distribution of moisture and temperature over a reference year is taken into account. The calculated results from both techniques are compared with results obtained by high‐performance computing without any computational simplifications. For materials homogenization, the best results are achieved when k = 0.9 in Lichtenecker’s mixing rule is assumed, which corresponds to a nearly parallel arrangement of the block. The root mean square error (RMSE) of relative humidity (RH) and temperature distribution is only 0.992% and 0.566°C, respectively. This is even better than the results of computational homogenization (RMSE: 1.502% of RH and 0.629°C). Besides obtaining sufficiently precise results, a significant time‐saving is achieved, accounting for more than 99%, while being solved on a single‐processor computer.

The Generation and Forecast of Extreme Winds during the Origin and Progression of the 2017 Tubbs Fire

On 8–9 October 2017, fourteen wildfires developed rapidly during a strong Diablo wind event in northern California including the Tubbs Fire, which travelled over 19 km in 3.25 h. Here, we applied the CAWFE® coupled numerical weather prediction-fire modeling system to investigate the airflow regime and extreme wind peaks underlying the extreme fire behavior using simulations that refine from a 10 km to a 185 m horizontal grid spacing. We found that as Diablo winds travelled south down the Sacramento Valley and fanned out southwestward over the Wine Country, their strength waxed and waned and their direction wavered, creating varying locations near fire origins where wind overrunning topography reached 30–40 m/s, along with streaks and bursts of strong winds in the lee of some topographic features and stagnation downstream of others. Despite a statically stable layer in the lowest 1.5 km, the high Froude number flow sometimes resembled a hydraulic jump. Elsewhere, the flow behaved similarly to neutrally-stratified flow over small hills, creating wind extrema that exceeded 40 m/s at the crest of some lesser hills including near the Tubbs fire ignition, but which shed bursts of high speed winds that travel downstream at approximately 5–7-min intervals. Nonetheless, simulated fire growth lagged satellite detection of fire arrival in Santa Rosa by up to 1 h, although whether the data detect fire line or spotting is ambiguous. A forecast simulation with a 370 m horizontal grid spacing produced an on-time fire line arrival in Santa Rosa, with calculations executed 4 times faster than real time on a single computer processor.

Development of a framework for parallel reservoir simulation

The development of a parallel reservoir simulator is a more complex task than nonparallel simulators. Problems related to parallel implementation such as parallel communication, model division among processors, and the management of data distributed among processors, and other issues should be addressed and solved in addition to the already complex task of reservoir simulator development. Hence, the development of parallel reservoir simulators is more time intensive than the traditional development on single processor computers. In this work, a framework was developed to aid the development of parallel reservoir simulators. The framework developed in this work implements and handles the parallel processing complexity and provides easy-to-use programming interfaces to accelerate the development of new parallel reservoir simulators or the parallelization of existing ones. Additionally, The University of Texas Compositional Simulator was used in conjunction with the framework developed in this work to create a parallel reservoir simulator. Case studies are used to verify accuracy and to test parallel performance of our new parallel reservoir simulator.

Method for a Parallel Solution of a Combined Economic Emission Dispatch Problem

The power system simulation software tools are traditionally designed for serial codes and optimized using single-processor computers. They are inadequate in terms of computational efficiency and execution time for the ever-increasing complexity of the power grid. Due to the above-mentioned sequential computing demerits, this paper used MATLAB data parallelism message passing interface software to execute the Lagrange's and Particle Swarm Optimization (PSO) algorithms in parallel with multiple processor units with different and large data sets for the solution of the Combined Economic Emission Dispatch (CEED) problem. The two important advantages of using parallel computing approach to solve the power system economic dispatch problem are 1) to increase the efficiency (solution quality) and 2) to reduce the execution time (speed-up) of the parallelization process for the CEED problem solution. The comparison between the Lagrange's and PSO data-parallel solution quality and execution time is presented for the CEED problem for Institute of Electrical and Electronic Engineers (IEEE) 30 bus and IEEE 118 bus systems. The paper contributes to the on-line real-time market analyses of the deregulated power system, which need improved solution quality and a fast computation process to solve the power system energy management (CEED) problems for proper discussion and decision making at the control center level.

Hybrid parallel approach to homogenization of transport processes in masonry

Numerical simulations of transport processes, e.g., heat and moisture transport, in heterogeneous masonry structures performed on single processor computers are very time consuming. The classical description by the finite element method leads to enormous numbers of degrees of freedom. It is caused by the fact that there are relatively thin layers of mortar in contrary to large bricks or stones. The mortar and stones have very different material parameters and the finite element mesh has to be able to describe the temperature and moisture fields in the thin layers of mortar and in their vicinity. An application of a multi-scale approach in connection with parallel computing can be successfully exploited. The whole structure is described by a reasonably coarse finite element mesh, called the macro-scale problem, and the material parameters are obtained from the lower-level problems, called the meso-scale problem, by a homogenization procedure. In this procedure, the macro-problem is assigned to the master processor and the meso-scale problems belong to the slave processors. This proposed approach is called hybrid parallel method.

MPBEC, a Matlab Program for Biomolecular Electrostatic Calculations

One of the most used and efficient approaches to compute electrostatic properties of biological systems is to numerically solve the Poisson–Boltzmann (PB) equation. There are several software packages available that solve the PB equation for molecules in aqueous electrolyte solutions. Most of these software packages are useful for scientists with specialized training and expertise in computational biophysics. However, the user is usually required to manually take several important choices, depending on the complexity of the biological system, to successfully obtain the numerical solution of the PB equation. This may become an obstacle for researchers, experimentalists, even students with no special training in computational methodologies. Aiming to overcome this limitation, in this article we present MPBEC, a free, cross-platform, open-source software that provides non-experts in the field an easy and efficient way to perform biomolecular electrostatic calculations on single processor computers. MPBEC is a Matlab script based on the Adaptative Poisson–Boltzmann Solver, one of the most popular approaches used to solve the PB equation. MPBEC does not require any user programming, text editing or extensive statistical skills, and comes with detailed user-guide documentation. As a unique feature, MPBEC includes a useful graphical user interface (GUI) application which helps and guides users to configure and setup the optimal parameters and approximations to successfully perform the required biomolecular electrostatic calculations. The GUI also incorporates visualization tools to facilitate users pre- and post-analysis of structural and electrical properties of biomolecules. Program summaryProgram title: MPBECCatalogue identifier: AEXW_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEXW_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: UTSA licenseNo. of lines in distributed program, including test data, etc.: 1505214No. of bytes in distributed program, including test data, etc.: 115585507Distribution format: tar.gzProgramming language: Matlab R2010a and higher versions.Computer: Any computer at least with 2.5 GHz speed.Operating system: Windows, Linux Fedora, and Mac OSX.RAM: At least 2 GB of free memory.Classification: 3, 10.External routines: Java, Jmol, pdb2pqr, Propka.Nature of problem: Numerical solution of the linearized PB equation.Solution method: MPBEC uses sparse matrix calculations and iterative linear solvers (gmres, minres, and bicgstab) to solve the discretized (box method) linear PB equation. The stability and the convergence of these iterative solvers are improved by using the incomplete LU factorization which generates pre-conditioner matrices L (lower triangular matrix) and U (upper triangular matrix) at low computational cost.Unusual features: MPBEC incorporates a GUI to provide non-experts in computational biophysics a user-friendly and intuitive tool to obtain biomolecular electrostatic calculations. The GUI provides helpful information about how to fill out the input data by moving the mouse pointer over the corresponding text or blank box. The GUI tests all the input data before running MPBEC to make sure that the solver is properly configured and facilitates the user with the pre- and post-analysis of the biomolecular calculations.Additional comments: MPBEC checks available and required RAM memory before allocation to alert users when the available RAM memory is insufficient to perform the required calculations depending basically on the number of grid points and the size and number of atoms of the biomolecule(s). Therefore, MPBEC might be used with lower RAM memory and processor speed conditions than those mentioned above. The distribution file for this program is over 115 Mbytes and therefore is not delivered directly when download or Email is requested. Instead a html file giving details of how the program can be obtained is sent.Running time: The software runs on single processor computers at low-to-moderate computational cost depending on the computer performance, the biomolecule size, the grid resolution, and required calculations.

Large-Scale Optimization of Polymer Electrolyte Membrane Fuel Cells

The design of low-temperature polymer electrolyte membrane fuel cells (PEMFCs) consists of two separate but related problems: (1) geometry optimization, which focuses on finding the optimum channel configuration and overall cell dimensions; and (2) microstructure optimization, which focuses on finding the optimum distributions of the porosity, catalyst, and electrolyte that maximize the power density of the cell [1]. The number of design parameters in each of the above problems is relatively large, which makes the design of PEMFCs a difficult mathematical problem. For instance, let us consider problem (2): if we assume that the fuel cell is discretized using a 3-D finite element discretization with 106 nodes, the design variables consist of the mesh-point values of the porosity, catalyst concentration, and electrolyte density at each mesh point. Hence, the number of design variables is 3 ×106, which make the design of PEMFC a large-scale optimization problem. Usually, such large optimization problems are solved on parallel computer clusters, with a large number of concurrent processors. In this presentation we focus on the large-scale design of the microstructure of the PEMFC by developing a gradient-based method for the computation of the optimum (mathematically exact) distributions of the porosity, catalyst, and electrolyte that maximize the power density of the cell. It should be noted that traditional heuristic techniques such as genetic algorithms, swarm-optimization or other evolutionary algorithms are inappropriate for the large-scale optimization of PEMFCs. Indeed, the number of optimization variables in non-heuristic techniques in of the order or 5-10 variables, which is a few orders of magnitude smaller than the total number of design variables in PEMFCs. Since the computation time of heuristic techniques increases approximately exponentially with the number of design variables, the total optimization time of heuristic techniques becomes unpractical in large-scale optimization problems. The technique presented in this work is based on evaluating the sensitivity functions of the cell voltage and discharge current and applying a gradient-based maximization method to optimize the PEMFC. The sensitivity functions are computed efficiently using an adjoint space approach suitable for large-scale systems that can be simulated using 2-D or 3-D finite element models. The computational cost required to compute the sensitivity functions is relatively small since our technique requires solving only one sparse system of linear equations instead of performing multiple device simulations. For this reason, the numerical algorithm presented in this work can be implemented on single processor computers even in the case of 3-D finite element problems. Using the proposed algorithm we are able to predict the optimum distribution of platinum particles and electrolyte inside the catalyst layers, as well as the optimum porosity profile that maximizes the power density of the cell. The optimum distributions and porosity profiles depends on the positions of landings and openings, and on the geometry and dimensions of the layers. In agreement with existing experimental data, we obtain that the catalyst density should be distributed non-uniformly inside the cell in order to increase the power density of the cell (see figure). In the case of the porosity profile it is more efficient to increase the porosity towards the gas diffusion layer side in order to increase the flow of the oxygen and water vapors. In the case of the electrolyte distribution in the catalyst layers it is more efficient to increase the fraction of the electrolyte towards the membrane in order to decrease the ohmic losses of the protons. Finally, it should be noted that our numerical algorithm can also show how the optimum distributions of the catalyst, porosity and electrolyte vary with the discharge conditions. The results describe the complete profiles of the optimal catalyst and porosity by providing the full distribution of optimum locations and concentration of platinum particles in the catalyst layer, as well as the “ideal” porosity distribution of the PEMFC under the specified operating conditions. More details about the technique, the numerical implementation, and the final optimized structures will be presented at the conference.

Software Implememntation of Autonomous Short-range Radio Navigation System

In the presented paper problems of a software implementation of a groundbased and on board part of an autonomous system of short-range radio navigation of aerial vehicle are discussed. Operation stages of a ground-based part of the system are described, the most important of which is a selection of aerical vehicle (AV) flight trajectory correction points, which are controlled by on-board inertial navigation system. The final result produced by a ground-based part of the system is a flight plan for on-board module. In the last part of the paper problems of range measurements’ filtering and monitoring of navigation field integrity are discussed. A control flow schemeis presented,which is implemented for a single processor computer.

Practical Earthquake Location on a Continental Scale in Australia Using the AuSREM 3D Velocity Model

This study explores the full 3D earthquake location for the Australian continent, exploiting the recent 3D Australian Seismological Reference Model (AuSREM). Seismic velocities from AuSREM were used as input to precompute finely spaced P ‐ and S ‐travel‐time grids for each station in the Australian National Seismograph Network using the multistage fast marching method. Travel times from anywhere in the grid to the corresponding station can then be computed by interpolation. The location search using these travel times is based on matching observed and computed arrival times using the neighborhood algorithm. All computations involved can be performed in practical time frames on a single processor computer. The performance of the 3D approach relative to location using the 1D global ak135 velocity model was assessed by locating a set of recent earthquakes. The arrival‐time residuals for P and S arrivals are significantly smaller when using the 3D AuSREM model. The improvements over ak135 are especially large in the 10°–18° distance range, in which a distance bias is strongly reduced and for those paths where the ak135 residuals are large. A small set of six ground‐truth events was used to assess to what extent the reduction in travel‐time residuals leads to better absolute location accuracy. The 3D location offset from the ground‐truth position is typically half that of the ak135 offset. The patterns of offsets suggest that the already fast mantle wavespeeds in western Australia need to be even faster than in AuSREM.

Solving difficult mixed integer and disjunctive non-linear problems on single and parallel processors

Difficult non-linear, mixed integer non-linear and general disjunctive programming (NLP/MINLP/GDP) problems are considered in the present study. We show using random relaxation procedures that sampling over a subset of the integer variables and parallel processing have potential to simplify large scale MINLP/GDP problems and hence to solve them faster and more reliably than conventional approaches on single processors. Gray coding can be utilized to assign problems to the local processors. Some efficient non-linear solvers have been connected to the Genetic Hybrid Algorithm (GHA) through a switch board minlp_machine(), monitoring a vector of caller functions. The switch board is tested on single processor computers and massively parallel supercomputers in a sample of optimization problems. A new line search algorithm based on multi-sectioning, i.e. repeated bi-sectioning of parallel threads is applied to a set of irregular NLP-problems. Simulations indicate that step search based on multi-sectioning and re-centering is robust. Time recording indicates that the step search procedure is fast also in large optimization problems. Parallel processing with Gray coding and random sampling over a subset of the integer variables improves the performance of the sequential quadratic programming algorithm with multi-sectioning in large-scale problems. An early mesh interrupt guarantees load balancing in regular problems. General disjunctive programming (GDP) problems can be simplified through parallel processing. Certain problems are solved in larger scale than reported previously. Function pointer logic and intelligent switching procedures provide a fruitful basis for solving high-order GDP problems, where the computational challenge in direct MINLP-formulations would be insurmountable.

The Cognitive Programming Paradigm the Next Programming Struture

The development of computer programming started with the development of the switching logic, because, the computer hardware is made up of millions of digital switches. The activation and deactivation of these switches are through codified instructions – (program) which trigger the switches to function. The computer programming languages have gone through a revolution, from the machine code language, through assembly mnemonics to the high level programming languages like FORTRAN, ALGOL, COBOL, LISP, BASIC, ADA and C/C++. It is a fact that, these programming languages are not the exact codes that microprocessors do understand and work with, because through compiler and interpreter programs, these high level programming languages that are easily understood by people are converted to machine code languages for the microprocessor to understand and do the work human knowledge has instructed it to do. The various programming languages stem from the difficulties man has in using one programming language to solve different problems on the computer. Hence, for mathematical and trigonometrically problems, FORTRAN is the best, for business problems, COBOL is the right language, whilst for computer games and designs, BASIC language is the solution. The trend of using individual programming languages to solve specific problems by single processor computers have changed drastically, from single core processors to present day dual and multi-core processors. The main target of engineers and scientists is to reach a stage that the computer can think like the human brain. The human brain contains many cognitive (thinking) modules that work in parallel to produce a unique result. With the presence of multi-core processors, why should computers continue to draw summaries from stored databases, and allow us to sit hours to analyse these results to find solutions to problems? The subject of ‘Cognitive Programming Paradigm’, analyses the various programming structures and came out that these programming structures are performing similar tasks of processing stored databases and producing summarized information. These summarized information are not final, business managers and Executives have to sit hours to deliberate on what strategic decisions to take. Again, present day computers cannot solve problems holistically, as normally appear to human beings. Hence, there’s the need for these programming structures be grouped together to solve human problems holistically, like the human brains processing complex problems holistically. With the presence of multi-core processors, its possible to structure programming such that these programming structures could be run in parallel to solve a specific problem completely, i.e. be able to analyse which programming structure will be suitable for a particular problem solving or be able to store first solution and compare with new solutions of a problem to arrive at a strategic decision than its being done at present. This approach could lift the burden on Managers and Executives in deliberating further on results of a processed business problem.

Highly Parallelable Bidimensional Median Filter for Modern Parallel Programming Models

The median filter is a non-linear filter used for removal of Salt & Pepper noise from images, where each pixel of the image is replaced by the median of its surrounding elements, which is calculated by sorting the data. The complexity of the sorting algorithms used for the median filters are $O(n^2)$ or $O(n)$ , depending on the kernel size. These algorithms were formulated for scalar single processor computers, with few of them successfully adapted and implemented for computers with a parallel architecture. In this paper we greatly improve the results of our earlier work, in which by means of a novel sorting algorithm, based on the Complementary Cumulative Distribution function, with $O(n)$ computational complexity and a highly parallelable structure, we presented a 2D median filter that achieved $O(1)$ or $O(n)$ computational complexity, depending memory constraints. The improvements are twofold: we propose a trade-off between $O(1)$ complexity and $O(n)$ complexity in order to improve the overall throughput; additionally we make use of the Salt & Pepper noise model to improve the image reconstruction quality with a small performance impact. The proposed algorithm have been implemented in three parallel programming models: SIMD Intel, Multicore Intel with SIMD, and SIMT (CUDA), achieving a peak throughput of 27.0, 100.1 and 91.6 megapixels per second respectively.

Applications and Algorithms for Inference of Huge Phylogenetic Trees: a Review

Phylogenetics enables us to use various techniques to extract evolutionary relationships from sequence analysis. Most of the phylogenetic analysis techniques produce phylogenetic trees that represent relationship between any set of spe- cies or their evolutionary history. This article presents a comprehensive survey of the applications and the algorithms for inference of huge phylogenetic trees and also gives the reader an overview of the methods currently employed for the in- ference of phylogenetic trees. A comprehensive comparison of the methods and algorithms is presented in this paper. Phylogenetics is the study of evolutionary relationships. It enables us to use various methods to extract necessary in- formation from a sequence. Most of the phylogenetic analy- sis techniques produce phylogenetic trees. These trees rep- resent relationships between any set of species or their evo- lutionary history. The most accurate tree describing the evolution of a sequence can be obtained by phylogenetic analysis. Reconstruction of phylogenetic relationship using a DNA, RNA or amino acid sequences is a hierarchal process consisting of four steps(1) i.e. sequence alignment, selection of appropriate model, tree building method and the assess- ment of the resulting phylogeny(2). A variety of systems and applications(3-7) have been developed to infer phylogenetic trees. Early approaches were based on single node or single processor computers. Due to advances in high throughput sequencing technologies, pub- lically available genomic data is increasing exponentially. This huge amount of data needs sophisticated, efficient and high throughput methods to be analysed. This requirement brings parallel and distributed computing to the field of bioinformatics. Recent systems and algorithms for sequence alignment and phylogenetics are based on parallel and dis- tributed computing. This paper presents a comprehensive survey of the applications and algorithms for the inference of phylogenetic trees. Comparison of the methods and algo- rithms is also presented.

Wave power extraction by arbitrary arrays of non-diffracting oscillating water columns

A model to predict wave power conversion from arbitrary arrays of oscillating water columns is presented. Based on linear potential theory and idealized non-diffracting machines, it relies on a published analytical solution for the flow field generated by a single oscillating pressure patch on the ocean surface. Estimates of wave power conversion are obtained for several array configurations and well-defined wave resources at two sites in Hawaii, using a single-processor computer. Such results illustrate the versatility and computational efficiency of the model. The proposed approach allows a relatively easy evaluation of interferences among individual machines. This would be useful in optimizing the spatial arrangement of oscillating water columns in large wave farms.

Quantum mechanical simulation of electronic transport in nanostructured devices by efficient self-consistent pseudopotential calculation

We present a new empirical pseudopotential (EPM) calculation approach to simulate the million atom nanostructured semiconductor devices under potential bias using periodic boundary conditions. To treat the nonequilibrium condition, instead of directly calculating the scattering states from the source and drain, we calculate the stationary states by the linear combination of bulk band method and then decompose the stationary wave function into source and drain injecting scattering states according to an approximated top of the barrier splitting (TBS) scheme based on physical insight of ballistic and tunneling transports. The decomposed electronic scattering states are then occupied according to the source/drain Fermi-Levels to yield the occupied electron density which is then used to solve the potential, forming a self-consistent loop. The TBS is tested in a one-dimensional effective mass model by comparing with the direct scattering state calculation results. It is also tested in a three-dimensional 22 nm double gate ultra-thin-body field-effect transistor study, by comparing the TBS-EPM result with the nonequilibrium Green’s function tight-binding result. We expected the TBS scheme will work whenever the potential in the barrier region is smoother than the wave function oscillations and it does not have local minimum, thus there is no multiple scattering as in a resonant tunneling diode, and when a three-dimensional problem can be represented as a quasi-one-dimensional problem, e.g., in a variable separation approximation. Using our approach, a million atom nonequilibrium nanostructure device can be simulated with EPM on a single processor computer.