Parallel Algorithm For Computing Research Articles

Mathematical modelling of fracture waves in a blocky medium with thin compliant interlayers.

Physical mechanisms that contribute to the generation of fracture waves in condensed media under intensive dynamic impacts have not been fully studied. One of the hypotheses is that this process is associated with the blocky structure of a material. As the loading wave passes, the compliant interlayers between blocks are fractured, releasing the energy of self-balanced initial stresses in the blocks, which supports the motion of the fracture wave. We propose a new efficient numerical method for the analysis of the wave nature of the propagation of a system of cracks in thin interlayers of a blocky medium with complex rheological properties. The method is based on a variational formulation of the constitutive relations for the deformation of elastic-plastic materials, as well as the conditions for contact interaction of blocks through interlayers. We have developed a parallel computational algorithm that implements this method for supercomputers with cluster architecture. The results of the numerical simulation of the fracture wave propagation in tempered glass under the action of distributed pulse disturbances are presented. This article is part of the theme issue 'Non-smooth variational problems with applications in mechanics'.

Algorithm-assisted discovery of an intrinsic order among mathematical constants

In recent decades, a growing number of discoveries in mathematics have been assisted by computer algorithms, primarily for exploring large parameter spaces. As computers become more powerful, an intriguing possibility arises-the interplay between human intuition and computer algorithms can lead to discoveries of mathematical structures that would otherwise remain elusive. Here, we demonstrate computer-assisted discovery of a previously unknown mathematical structure, the conservative matrix field. In the spirit of the Ramanujan Machine project, we developed a massively parallel computer algorithm that found a large number of formulas, in the form of continued fractions, for numerous mathematical constants. The patterns arising from those formulas enabled the construction of the first conservative matrix fields and revealed their overarching properties. Conservative matrix fields unveil unexpected relations between different mathematical constants, such as π and ln(2), or e and the Gompertz constant. The importance of these matrix fields is further realized by their ability to connect formulas that do not have any apparent relation, thus unifying hundreds of existing formulas and generating infinitely many new formulas. We exemplify these implications on values of the Riemann zeta function ζ (n), studied for centuries across mathematics and physics. Matrix fields also enable new mathematical proofs of irrationality. For example, we use them to generalize the celebrated proof by Apéry of the irrationality of ζ (3). Utilizing thousands of personal computers worldwide, our research strategy demonstrates the power of large-scale computational approaches to tackle longstanding open problems and discover unexpected connections across diverse fields of science.

Computer Algorithm Design and Linearity Analysis of Its Data Structures

Linearity optimization of data structure is in an important position in the design of computer algorithms, and the study of its optimization measures and strategies can help to promote the research of computer algorithms in depth. This paper introduces the multithreading technology, designs a multithreaded inverted sorted cited table on the basis of the data chain table structure, and prunes and optimizes the data in the table and its distribution repetitions, and puts forward the linearity optimization strategy. And the GaBP parallel computer algorithm for banded linear equations is designed by combining this strategy. The linearity analysis of the optimized data structure reveals that the linearity is significantly improved, and the overall linearity is smooth and reasonably skewed. In the calculation of large-scale numerical values, the running time of the Partition number of 30 is only 3756.9 s. The average linear solution speed is only 0.063 s when solving the satisfiable SAT instances, which is 4.631 s less than that of the Minisat algorithm, and the optimization strategy of the data structure linearity proposed in this paper provides a reference scheme to accelerate the speed of computer algorithms in solving the difficult problems, and the design of the computer parallel algorithm provides a reference scheme to accelerate the speed of computer algorithms in solving the difficult problems. The designed computer parallel algorithm provides an effective method for the field of large-scale numerical processing and SAT problem solving.

Parallel algorithms for interactive evaluation of complex hierarchical-network systems

Methods of interactive evaluation of complex hierarchical-network systems with fully and partially orde-red flowsmovement are considered. For realization thesemethods in real time the parallel computation algorithms are proposed. Speed up estimates were obtained, which confirm the high efficiency of the men-tioned algorithms.The proposed parallel algorithms are oriented to execution on modern computing me-ans: computers with multi-core processors, clusters, hybrid architectures and in high-performance distri-buted environments.The results of the work can be used to study the functioningprocess of motor trans-portsystems for large city, region, country or railway transport system and other complex naturalandartificial systems with a hierarchical-network structure, their elements or subsystems.

A robust experimental design for conceptual model discrimination based on information theory

This study introduces firm information gain for model discrimination based on Shannon entropy and worst-case scenario experimental design. Firm information gain is the minimal additional information gained by an experimental design with respect to existing information. Robust experimental design aims to maximize the firm information gain by searching for the least number of new pumping wells and observation wells. Robust experimental design includes a Bayes factor threshold to ensure that new data provide strong evidence for model discrimination. To maximize the firm information gain, a framework is proposed that combines the parallel-sequential genetic algorithm (GA) for parallel computing and the nested quadrature rule for efficiently solving multidimensional integrals. The numerical experiment involves the true model for the purpose of verification. The results show that using a full covariance matrix is imperative to avoid exaggerating firm information gain. Collecting new groundwater data is prioritized over exploring additional pumping wells. Maximizing firm information gain is able to identify the same and true model.

Experimental Validation Through a Parallel Computation Algorithm for Evaluation Uncertainty of the Mathematical Model of Direct Expansion Solar Assisted Heat Pump

This paper presents the development of a mathematical model for a direct-expansion solar-assisted heat pump (DX-SAHP) operating in steady-state. The mathematical model was implemented using the scientific software EES and using a code written in Python. It was utilized a lumped parameter model for the heat exchangers and a semi-empirical model for the compressor. The mathematical model was validated using experimental data of a DX-SAHP running with R134a. Two hundred simulations were made combining different correlations for estimating the convective heat transfer coefficient in the evaporator/collector. The Mean Absolute Deviation (MAD) and the Mean Deviation (MD) between the theoretical and experimental values for the COP were 2.6±1.8 % and 0.9±1.8 %, respectively. The MAD and MD between the discharge temperature were 1.56±0.16 % and -1.45±0.16 %. The mean difference between the results using EES and Python were 1.4 %. The use of Python with parallel computing for uncertainty analyses, reduced the simulation time in 88 % if compared with EES. The model in Python is available as open-source through the platform Google Colaboratory.

JSICsim—An Analog Simulator for Superconductor Integrated Circuit

JSICsim, a new analog simulator for superconductor integrated circuits, is introduced in this brief. It works on SPICE (Simulation Program with Integrated Circuit Emphasis)-style netlists and employs the node voltage analysis method with the parallel computation algorithm. Additionally, the JSICsim simulator focuses on accelerating simulation by using an adjustable simulation accuracy method for large-scale circuit analysis, with a complete simulation platform and GUI integrated into the package. To compare JSICsim with other simulators like PSCAN2, WRspice, and JoSIM, several testbench circuits are simulated and analyzed. The result shows that JSICsim could accelerate the simulation up to 47,16,12 times compared with PSCAN2, WRspice, and JoSIM, respectively. Moreover, JSICsim supports a simulation scale of up to 1 million junctions. Other functionalities such as IV-curve sweeping and thermal noise simulation are also demonstrated.

Dynamic Skyline Computation with LSD Trees

Given a set of high-dimensional feature vectors S⊂Rn, the skyline or Pareto problem is to report the subset of vectors in S that are not dominated by any vector of S. Vectors closer to the origin are preferred: we say a vector x is dominated by another distinct vector y if x is equally or further away from the origin than y with respect to all its dimensions. The dynamic skyline problem allows us to shift the origin, which changes the answer set. This problem is crucial for dynamic recommender systems where users can shift the parameters and thus shift the origin. For each origin shift, a recomputation of the answer set from scratch is time intensive. To tackle this problem, we propose a parallel algorithm for dynamic skyline computation that uses multiple local split decision (LSD) trees concurrently. The geometric nature of the LSD trees allows us to reuse previous results. Experiments show that our proposed algorithm works well if the dimension is small in relation to the number of tuples to process.

A Parallel Computing Algorithm for the Emergency-Oriented Atmospheric Dispersion Model CALPUFF

The calculation of the three-dimensional atmospheric dispersion model is often time-consuming, which makes the model difficult to apply to the emergency field. With the aim of addressing this problem, we propose a parallel computing algorithm for the CALPUFF atmospheric dispersion model. Existing methods for parallelizing the atmospheric dispersion model can be divided into two categories, with one using the parallel computing interface to rewrite the source code and the other directly dividing the repetitive elements in the computation task. This paper proposes an improved method based on the latter approach. Specifically, the method of spatial division with buffers is adopted to parallelize the wind field module of the CALPUFF model system, and the method for receptor layering is adopted to parallelize the dispersion module. In addition, the message queue software RabbitMQ is used as the communication middleware. A performance test is conducted on nine computing nodes on the Alibaba Cloud Computing Platform for a single-source continuous emergency leak case. The results show that the division method with a buffer of ten cells is most suitable for the case above in order to maintain the balance between computation speed and accuracy. This reduces the computation time of the model to about one-sixth, which is of great significance for extending the atmospheric dispersion model to the emergency field.

Parallel analysis of Ethereum blockchain transaction data using cluster computing

Ability to perform fast analysis on massive public blockchain transaction data is needed in various applications such as tracing fraudulent financial transactions. The blockchain data is continuously growing and is organized as a sequence of blocks containing transactions. This organization, however, cannot be used for parallel graph algorithms which need efficient distributed graph data structures. Using message passing libraries (MPI), we develop a scalable cluster-based system that constructs a distributed transaction graph in parallel and implement various transaction analysis algorithms. We report performance results from our system operating on roughly 5 years of 10.2 million block Ethereum Mainnet blockchain data. We report timings obtained from tests involving distributed transaction graph construction, partitioning, page ranking of addresses, degree distribution, token transaction counting, connected components finding and our new parallel blacklisted address trace forest computation algorithm on a 16 node economical cluster set up on the Amazon cloud. Our system is able to construct a distributed graph of 766 million transactions in 218 s and compute the forest of blacklisted address traces in 32 s.

Parallel Computational Algorithm for Object-Oriented Modeling of Manipulation Robots

An algorithm for parallel calculations in a dynamic model of manipulation robots obtained by the Lagrange–Euler method is developed. Independent components were identified in the structure of the dynamic model by its decomposition. Using the technology of object-oriented programming, classes corresponding to the structures of the selected components of the dynamic model were described. The algorithmization of parallel computing is based on the independence of the calculation of objects of individual classes and the sequence of matrix operations. The estimation of the execution time of parallel algorithms, the resulting acceleration, and the efficiency of using processors is given.

Comparison of Two Parallel Offsetting Algorithms Free from Conflicts Between Threads

Offset computation for expanding a polyhedral object by an offset radius is a fundamental geometric process frequently used in manufacturing applications. This process combined with the triple-dexel representation solid model has become popular because of its robustness and compatibility with parallel processing using a graphics processing unit (GPU). In parallel geometric processing, conflicts between threads must be avoided. Thus, we propose a novel parallel offsetting algorithm free from conflicts between threads. The triple-dexel model is a combination of x-, y-, and z-axis-aligned dexel models. Each dexel model is defined based on an orthogonal grid given on a coordinate plane. We subdivide the grid into several sub-grids of a fixed size in advance. For each sub-grid, a block of GPU threads is assigned. As each GPU thread always processes different dexel elements from the other threads in this method, no conflict occurs. Our research group has previously presented a parallel offset computation algorithm for a polyhedral solid model that also uses a triple-dexel representation model and a GPU. In the previous algorithm, the surface polygons of the model are classified into several groups in advance. The parallel offset computation of multiple polygon groups is realized by selecting groups of polygon in which the offset processing of the polygons does not affect one another. This selection process is time-consuming. Computational experiments were performed to analyze the performance difference between the current algorithm and our previous algorithm. In our experiments, the current algorithm achieved speedups of 1.4 times to 3.2 times compared to our previous offsetting algorithm.

Implementation of Parallel-Computing Algorithms for Systems with Distributed Memory in the EUKLID/V1 Integrated Computer Code

Implementation of Parallel-Computing Algorithms for Systems with Distributed Memory in the EUKLID/V1 Integrated Computer Code

Theoretical study on amplified spontaneous emission (ASE) in a V-pumped thin-disk alkali laser

In the paper, a mathematical model was systematically constructed to evaluate the amplified spontaneous emission (ASE) effects and temperature distribution of a vapor cell of the V-pumped thin-disk alkali lasers. There have been few literatures on studying ASE in a diode-pumped alkali laser (DPAL) although such a phenomenon is normally serious especially in a thin-disk configuration. By reducing the number of the outputted conditions and adopting a parallel computation algorithm, the computation speed has been demonstrated to be greatly improved by comparing to the method we ever used before. The results show that the ASE effects in a thin-disk DPAL cannot be ignored especially when the size of a pump beam becomes big. It has also been proved that ASE in a thin-disk DPAL tightly relates to the pump power, thickness and temperature of a thin-disk cell, and reflectivity of the output coupler (OC). The calculation approaches in this paper are thought to be valuable in the design of a high-powered DPAL and can also be applied to the other kinds of high-powered lasers.

A Reconfigurable Memory based Fast VLSI Architecture for Histogram Computation

Histogram computation is the crucial task used in processing so many image guided applications like pattern recognition, image segmentation etc. Image registration is one of the fundamental techniques for pre-processing of the images. Registration is the process of overlaying multiple images to geometrically align them. In medical Image processing, the improper registration can have negative impact on the analysis of the image which influences the final diagnosis. The accurate result of image registration is obtained by matching of multimodal images. Mutual Information is one of the commonly used techniques to find the similarity measurement between multi-modal images. Measurement of similarity requires a computation of histogram of individual images and joint histogram between the images. The hardware implementation of histogram computation has advantages in terms of flexible design, low power consumption, high speed, less execution time than the software implementation. This paper proposed a parallel algorithm for histogram computation. A memory based pipeline architecture is designed for implementing the proposed algorithm. The hardware mapping of the algorithm on FPGA is proposed and simulating them using Xilinx software tools.

GPU-Based DEM Simulations of Global Ice Resistance on Ship Hull During Navigation in Level Ice

The ice resistance on a ship hull affects the safety of the hull structure and the ship maneuvering performance in ice-covered regions. In this paper, the discrete element method (DEM) is adopted to simulate the interaction between level ice and ship hull. The level ice is modeled with 3D bonded spherical elements considering the buoyancy and drag force of the water. The parallel bonding approach and the de-bonding criterion are adopted to model the freezing and breakage of level ice. The ship hull is constructed with rigid triangle elements. To improve computational efficiency, the GPU-based parallel computational algorithm was developed for the DEM simulations. During the interaction between the ship hull and level ice, the ice cover is broken into small blocks when the inter-particle stress approaches the bonding strength. The global ice resistance on the hull is calculated through the contacts between ice elements and hull elements during the navigation process. The influences of the ice thickness and navigation speed on the dynamic ice force are analyzed considering the breakage mechanism of ice cover. The Lindqvist and Riska formulas for the determination of ice resistance on ship hull are employed to validate the DEM simulation. The comparison of results of DEM, Lindqvist, and Riska formula show that the DEM result is between those the Lindqvist formula and Riska formula. Therefore the proposed DEM is an effective approach to determine the ice resistance on the ship hull. This work can be aided in the hull structure design and the navigation operation in ice-covered fields.

Data-Driven Planning of Electric Vehicle Charging Infrastructure: A Case Study of Sydney, Australia

With the increase of vehicle travelling range and the decrease of vehicle expense, Electric Vehicles (EVs) are gaining popularity as a new transport option which offers a great opportunity for creating a smart city in terms of transportation electrification. However, the existing research of EV charger planning primarily concentrates on the optimal location seeking from a perspective of global optimization. To achieve a competitive strategy in future, a market-based mechanism is proposed for the problem of EV charger planning with a case study of Sydney where a predict-then-optimise diagram is introduced to predict the EV charging demand by a multi-relation graph convolutional network (GCN)-based encoder-decoder deep architecture and optimise the competitive resource allocation strategy for the charger planning through a Cournot competition game model. A new parallel computational algorithm is proposed to seek the Cournot competition equilibrium with the convergence analysis. Furthermore, the optimal EV charger sizing problem is considered to obtain each service provider’s EV charger number at each zone of the city. Experiments are implemented by using the Sydney public transportation dataset and the corresponding key economic indicators. The results show the effectiveness of our proposed approach which can fill the gap between data and the optimal EV charging infrastructure planning.

Innovative Dual-Binary-Field Architecture for Point Multiplication of Elliptic Curve Cryptography

High-performance Elliptic Curve Cryptography (ECC) implementation in encryption authentication severs has become a challenge due to the explosive growth of e-commerce’s demand for speed and security. Point multiplication (PM) is the most common and complex operation in ECC which directly determines the performance of the whole system. This article proposes a 6CC-6CC (clock cycle) dual-field PM architecture and a 6CC-4CC dual-field PM architecture based on maximizing utilization of Karatsuba multipliers and re-ordering schedule strategy in PM. The Montgomery Ladder algorithm used in PM is modified to a 4CC algorithm for better resource utilization and parallel computation. To solve the frequency drop problem while working on large finite field, the PM architectures for high and low field are carefully studied to have universal critical path length and balanced performance. Both of the architectures are implemented over GF(2571) and GF(2283) on Xilinx Virtex-5 and Virtex-7 FPGAs (Field-Programmable Gate Array) for comparison. The 6CC-6CC architecture is shown to have the best performance on GF(2571), which achieves one PM operation in $17.44~\mu s$ using 81549 LUTs (Look-Up-Table) with the frequency of 197.2 MHz on Virtex-5, and $12.55~\mu s$ using 80970 LUTs with the frequency of 274.1 MHz on Virtex-7. The 6CC-4CC architecture performs better on GF(2283) with the shortest computation time. It takes only $3.21~\mu s$ to finish one PM operation on Virtex-5 and $2.22~\mu s$ on Virtex-7, which are faster than all the previous designs. The implementation results prove that the proposed architectures have state-of-the-art performance as well as higher versatility for ECC designs.

Synchronous and asynchronous parallel computation for large-scale optimal control of connected vehicles

Connected vehicles is an important intelligent transportation system to improve the traffic performance. This paper proposes two parallel computation algorithms to solve a large-scale optimal control problem in the coordination of multiple connected vehicles. The coordination is formulated as a centralized optimization problem in the receding horizon fashion. A decentralized computation network is designed to facilitate the development of parallel algorithms. We use Taylor series to linearize non-convex constraints, and introduce a set of consensus constraints to transform the centralized problem to a standard consensus optimization problem. A synchronous parallel algorithm is firstly proposed to solve the consensus optimization problem by applying the alternating direction method of multipliers (ADMM). The ADMM framework allows us to decompose the coupling constraints and decision variables, leading to parallel iterations for each vehicle in a synchronous fashion. We then propose an asynchronous version of the parallel algorithm that allows the vehicles to update their variables asynchronously in the computation network. The effectiveness and efficiency of the proposed algorithms are validated by extensive numerical simulations.

New Parallel Technics for GPU, Fast SURF Algorithm

Computer vision algorithms, especially real-time tasks, require intensive computation and reduced time. That’s why many algorithms are developed for interest point detection and description. For instance, SURF (Speeded Up Robust Feature) is extensively adopted in tracking or detecting forms and objects. SURF algorithm remains complex and massive in term of computation. So, it’s a challenge for real time usage on CPU. In this paper we propose a fast SURF parallel computation algorithm designed for Graphics-Processing-Unit (GPU). We describe different states of the algorithm in detail, using several optimizations. Our method can improve significantly the original application by reducing the computation time. Thus, it presents a good performance for real-time processing.