Parallel Implementation Of Algorithm Research Articles

Critical Phenomena and Phase Transitions in Large Lattices within Monte-Carlo Based Non-perturbative Approaches

Critical phenomena and Goldstone mode effects in spin models with the O(n) rotational symmetry are considered. Starting with Goldstone mode singularities in the XY and O(4) models, we briefly review various theoretical concepts, as well as state-of-the-art Monte Carlo simulation results. They support recent results of the GFD (grouping of Feynman diagrams) theory, stating that these singularities are described by certain nontrivial exponents, which differ from those predicted earlier by perturbative treatments. Furthermore, we present the recent Monte Carlo simulation results of the three-dimensional Ising model for lattices with linear sizes up to L = 1536, which are very large as compared to L ≤ 128 usually used in the finite-size scaling analysis. These results are obtained, using a parallel OpenMP implementation of the Wolff single-cluster algorithm. The finite-size scaling analysis of the critical exponent η, assuming the usually accepted correction-to-scaling exponent ω ≈ 0.8, shows that η is likely to be somewhat larger than the value 0.0335 ± 0.0025 of the perturbative renormalization group (RG) theory. Moreover, we have found that the actual data can be well described by different critical exponents: η = ω =1/8 and ν = 2/3, found within the GFD theory.

Parallel implementation of the primer search algorithm for loop-mediated isothermal amplification

In the paper, the implementation of an algorithm of search for primers in a DNA sequence with a size varying between one nucleotide and multiple million nucleotides was discussed. This analysis was done within the objective of finding a set of six specific primers that are used for a conduction of the loop-mediated isothermal amplification (LAMP). For the fastest search result possible, a parallel search for the primer with the use of the Rabin-Karp Algorithm which enables the search for a primer’s entry in DNA sequence in each thread was proposed. A new software for search for primers was developed using Python with BioPython library which implements the algorithm.

Improving Column Generation for Vehicle Routing Problems via Random Coloring and Parallelization

We consider a variant of the vehicle routing problem (VRP) where each customer has a unit demand and the goal is to minimize the total cost of routing a fleet of capacitated vehicles from one or multiple depots to visit all customers. We propose two parallel algorithms to efficiently solve the column-generation-based linear-programming relaxation for this VRP. Specifically, we focus on algorithms for the “pricing problem,” which corresponds to the resource-constrained elementary shortest path problem. The first algorithm extends the pulse algorithm for which we derive a new bounding scheme on the maximum load of any route. The second algorithm is based on random coloring from parameterized complexity which can be also combined with other techniques in the literature for improving VRPs, including cutting planes and column enumeration. We conduct numerical studies using VRP benchmarks (with 50–957 nodes) and instances of a medical home care delivery problem using census data in Wayne County, Michigan. Using parallel computing, both pulse and random coloring can significantly improve column generation for solving the linear programming relaxations and we can obtain heuristic integer solutions with small optimality gaps. Combining random coloring with column enumeration, we can obtain improved integer solutions having less than 2% optimality gaps for most VRP benchmark instances and less than 1% optimality gaps for the medical home care delivery instances, both under a 30-minute computational time limit. The use of cutting planes (e.g., robust cuts) can further reduce optimality gaps on some hard instances, without much increase in the run time. Summary of Contribution: The vehicle routing problem (VRP) is a fundamental combinatorial problem, and its variants have been studied extensively in the literature of operations research and computer science. In this paper, we consider general-purpose algorithms for solving VRPs, including the column-generation approach for the linear programming relaxations of the integer programs of VRPs and the column-enumeration approach for seeking improved integer solutions. We revise the pulse algorithm and also propose a random-coloring algorithm that can be used for solving the elementary shortest path problem that formulates the pricing problem in the column-generation approach. We show that the parallel implementation of both algorithms can significantly improve the performance of column generation and the random coloring algorithm can improve the solution time and quality of the VRP integer solutions produced by the column-enumeration approach. We focus on algorithmic design for VRPs and conduct extensive computational tests to demonstrate the performance of various approaches.

Simulation of heat transfer with considering permafrost thawing in 3D media

An approach to mathematical modeling of heat transfer with a permafrost algorithm in 3D media based on the idea of localizing the phase transition area is considered. The paper presents a problem statement for a non-stationary heat transfer and a description of a numerical method based on a predictor-corrector scheme. For a better understanding of the proposed splitting method, the accuracy order of approximation considering inhomogeneous right-hand side was studied. The phase changes in the numerical implementation of permafrost thawing is considered in the temperature range and requires recalculation of coefficients values of the heat equation at each iteration step with respect to time. A brief description of the parallel algorithm based on a 3D decomposition method and the parallel sweep method is presented. A study of the parallel algorithm implementations using a high-performance computing system of the Siberian Supercomputer Center of the SB RAS was performed. The results of the permafrost algorithm on models with wellbores are also presented.

Parallel network simplex algorithm for the minimum cost flow problem

AbstractIn this work, we contribute a parallel implementation of the network simplex algorithm that is used for the solution of minimum cost flow problem. In the network simplex algorithm, finding an entering arc requires searching through many arcs to decide which one should be included in the spanning tree solution on the next iteration. We propose finding the entering arc in parallel as it often takes the majority of the execution time. A usual strategy is to pick the arc violating the optimality the most out of all possible candidates. Scanning all arcs can take quite some time, so it is common to consider only a fixed number of arcs which is referred as the block search pivoting rule. Arc scans can easily be done in parallel to find the best candidate as the calculations are independent of each other. We used shared memory parallelism using OpenMP along with vectorization using AVX instructions. We also tried adjusting block sizes to increase the parallel portion of the algorithm. Our dataset consists of various natural and synthetic graphs with sizes up to a billion arc. Our experiments show speedups up to four are possible, though they are typically lower.

Performance evaluation of Java/PCJ implementation of parallel algorithms on the cloud (extended version)

AbstractLarge‐scale computing and data processing with cloud resources is gaining popularity. However, the usage of the cloud differs from traditional high‐performance computing (HPC) systems and both algorithms and codes have to be adjusted. This work is often time‐consuming and performance is not guaranteed. To address this problem we have developed the PCJ library (parallel computing in Java), a novel tool for scalable HPC and big data processing in Java. In this article, we present a performance evaluation of parallel applications implemented in Java using the PCJ library. The performance evaluation is based on the examples of highly scalable applications of different characteristics focusing on CPU, communication or I/O. They run on the traditional HPC system and Amazon web services Cloud as well as Linaro Developer Cloud. For the clouds, we have used Intel x86 and ARM processors for running Java codes without changing any line of the program code and without the need for time‐consuming recompilation. Presented applications have been parallelized using the partitioned global address space programming model and its realization in the PCJ library. Our results prove that the PCJ library, due to its performance and ability to create simple portable code, has great promise to be successful for the parallelization of various applications and run them on the cloud with a performance close to HPC systems.

A numerical study of Markov decision process algorithms for multi-component replacement problems

We present a unified modeling framework for Time-Based Maintenance (TBM) and Condition-Based Maintenance (CBM) for optimization of replacements in multi-component systems. The considered system has a K-out-of-N reliability structure, and components deteriorate according to a multivariate gamma process with Lévy copula dependence. The TBM and CBM models are formulated as Markov Decision Processes (MDPs), and optimal policies are found using dynamic programming. Solving the CBM model requires that the continuous deterioration process is discretized. We therefore investigate the discretization level required for obtaining a near-optimal policy. Our results indicate that a coarser discretization level than previously suggested in the literature is adequate, indicating that dynamic programming is a feasible approach for optimization in multi-component systems. We further demonstrate this through empirical results for the size limit of the MDP models when solved with an optimized implementation of modified policy iteration. The TBM model can generally be solved with more components than the CBM model, since the former has a sparser state transition structure. In the special case of independent component deterioration, transition probabilities can be calculated efficiently at runtime. This reduces the memory requirements substantially. For this case, we also achieved a tenfold speedup when using ten processors in a parallel implementation of algorithm. Altogether, our results show that the computational requirements for systems with independent component deterioration increase at a slower rate than for systems with stochastic dependence.

Taskflow: A General-Purpose Parallel and Heterogeneous Task Programming System

Taskflow tackles the long-standing question: How can we make it easier for developers to program parallel and heterogeneous computer-aided design (CAD) applications with high performance and simultaneous high productivity? Taskflow introduces a new powerful task graph programming model to assist developers in the implementation of parallel and heterogeneous algorithms with complex control flow. We develop an efficient system runtime to solve many of the new scheduling challenges arising out of our models and optimize the performance across latency, energy efficiency, and throughput. Taskflow has demonstrated promising performance on both micro-benchmarks and real-world applications. As an example, Taskflow solved a large-scale circuit placement problem up to 17% faster, with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.3\times $ </tex-math></inline-formula> fewer memory, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.1\times $ </tex-math></inline-formula> less power consumption, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.9\times $ </tex-math></inline-formula> higher throughput than two industrial-strength systems, oneTBB and StarPU, on a machine of 40 CPUs and 4 GPUs.

Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable

There has been significant recent interest in parallel graph processing due to the need to quickly analyze the large graphs available today. Many graph codes have been designed for distributed memory or external memory. However, today even the largest publicly-available real-world graph (the Hyperlink Web graph with over 3.5 billion vertices and 128 billion edges) can fit in the memory of a single commodity multicore server. Nevertheless, most experimental work in the literature report results on much smaller graphs, and the ones for the Hyperlink graph use distributed or external memory. Therefore, it is natural to ask whether we can efficiently solve a broad class of graph problems on this graph in memory. This paper shows that theoretically-efficient parallel graph algorithms can scale to the largest publicly-available graphs using a single machine with a terabyte of RAM, processing them in minutes. We give implementations of theoretically-efficient parallel algorithms for 20 important graph problems. We also present the interfaces, optimizations, and graph processing techniques that we used in our implementations, which were crucial in enabling us to process these large graphs quickly. We show that the running times of our implementations outperform existing state-of-the-art implementations on the largest real-world graphs. For many of the problems that we consider, this is the first time they have been solved on graphs at this scale. We have made the implementations developed in this work publicly-available as the Graph Based Benchmark Suite (GBBS).

GPU-Based Hybrid Cellular Genetic Algorithm for Job-Shop Scheduling Problem

In task scheduling, the job-shop scheduling problem is notorious for being a combinatorial optimization problem; it is considered among the largest class of NP-hard problems. In this paper, a parallel implementation of hybrid cellular genetic algorithm is proposed in order to reach the best solutions at a minimum execution time. To avoid additional computation time and for real-time control, the fitness evaluation and genetic operations are entirely executed on a graphic processing unit in parallel; moreover, the chosen genetic representation, as well as the crossover, will always give a feasible solution. In this paper, a two-level scheme is proposed; the first and fastest uses several subpopulations in the same block, and the best solutions migrate between subpopulations. To achieve the optimal performance of the device and to reshape a more complex problem, a projection of the first on different blocks will make the second level. The proposed solution leads to speedups 18 times higher when compared to the best-performing algorithms.

Parallel implementation of algorithm for creating and verifying digital signature based on elliptic curves

The article discusses the use of parallel data processing in the implementation in the C ++ programming language using the Visual Studio integrated development environment of an algorithm for information protection and authentication. Data processing is based on the method of discrete logarithm in a group of points of an elliptic curve. The studied case has been developed as Cost Optimization Program for an Oil and Gas Company and includes creating an Optimization Map information system. A part of the system is a module generating and verifying a digital signature. Due to the use of parallel data processing and the ECDSA algorithm for digital signature creation, oil and gas company will be able to increase its working efficiency and information security.

Implementation of the computer tomography parallel algorithms with the incomplete set of data.

Computer tomography has a wide field of applicability; however, most of its applications assume that the data, obtained from the scans of the examined object, satisfy the expectations regarding their amount and quality. Unfortunately, sometimes such expected data cannot be achieved. Then we deal with the incomplete set of data. In the paper we consider an unusual case of such situation, which may occur when the access to the examined object is difficult. The previous research, conducted by the author, showed that the CT algorithms can be used successfully in this case as well, but the time of reconstruction is problematic. One of possibilities to reduce the time of reconstruction consists in executing the parallel calculations. In the analyzed approach the system of linear equations is divided into blocks, such that each block is operated by a different thread. Such investigations were performed only theoretically till now. In the current paper the usefulness of the parallel-block approach, proposed by the author, is examined. The conducted research has shown that also for an incomplete data set in the analyzed algorithm it is possible to select optimal values of the reconstruction parameters. We can also obtain (for a given number of pixels) a reconstruction with a given maximum error. The paper indicates the differences between the classical and the examined problem of CT. The obtained results confirm that the real implementation of the parallel algorithm is also convergent, which means it is useful.

Comparative analysis of parallel algorithms for solving oil recovery problem using CUDA and OpenCL

‎In this paper the implementation of parallel algorithm of alternating direction implicit (ADI) method has been considered‎. ‎ADI parallel algorithm is used to solve a multiphase multicomponent fluid flow problem in porous media‎. ‎There are various technologies for implementing parallel algorithms on the CPU and GPU for solving hydrodynamic problems‎. ‎In this paper GPU-based (graphic processor unit) algorithm was used‎. ‎To implement the GPU-based parallel ADI method‎, ‎CUDA and OpenCL were used‎. ‎ADI is an iterative method used to solve matrix equations‎. ‎To solve the tridiagonal system of equations in ADI method‎, ‎the parallel version of cyclic reduction (CR) method was implemented‎. ‎The cyclic reduction is a method for solving linear equations by repeatedly splitting a problem as a Thomas method‎. ‎To implement of a sequential algorithm for solving the oil recovery problem‎, ‎the implicit Thomas method was used‎. ‎Thomas method or tridiagonal matrix algorithm is used to solve tridiagonal systems of equations‎. ‎To test parallel algorithms personal computer installed Nvidia RTX 2080 graphic card with 8 GB of video memory was used‎. ‎The computing results of parallel algorithms using CUDA and OpenCL were compared and analyzed‎. ‎The main purpose of this research work is a comparative analysis of the parallel algorithm computing results on different technologies‎, ‎in order to show the advantages and disadvantages each of CUDA and OpenCL for solving oil recovery problems‎.

RETRACTED: Design and implementation of parallel algorithm for image matching based on Hausdorff Distance

Object matching two-dimensional images in computer vision has become a significant subject of article acknowledgment and picture investigation. Hausdorff Distance assumes a significant function in coordinating image. To proposed system parallel algorithm for image matching manage the instance of arbitrary commotion, image coordinating, new Hausdorff Distance is proposed in this. In contrast to coordinating two twofold pictures, different techniques, the proposed strategy might be coordinated with a few dark scale image pixel esteems. One case of article acknowledgment is utilized to show the productivity of the proposed strategy. The outcomes demonstrated that, contrasted and, the new Hausdorff Distance (HD) might be more alluring approach to discard image clamor coordinating, because of the extensive reflection to decide the dark scale data Hausdorff Distance of adjoining pixels in the shooting of his realities into account. Besides, the strategy can be acknowledged in a straightforward manner.

Parallel simulation of drift–diffusion–recombination by cellular automata and global random walk algorithm

We suggest in this paper a parallel implementation of cellular automation and global random walk algorithms for solving drift–diffusion–recombination problems which in contrast to the classical random walk on spheres (RWS) methods calculate the solution in any desired family of m prescribed points. The method uses only N trajectories in contrast to mN trajectories in the conventional RWS algorithm. The idea is based on the adjoint symmetry property of the Green function and a double randomization approach. The synchronous multi-particle cellular automaton model of drift–diffusion–recombination is based on known cellular automata. The global RWS and cellular automaton models are tested against the exact solutions of the equation. The accuracy and computer time of both algorithms is analyzed. Parallel codes for the Monte Carlo and cellular automaton algorithms are implemented. The domain decomposition method is employed for the cellular automaton parallel implementation. The global random walk algorithm is parallelized by the Monte Carlo trajectories distribution among the cluster cores. The efficiency of the parallel codes is studied.

Parallel and Distributed Implementation of Sine Cosine Algorithm on Apache Spark Platform

The Sine Cosine Algorithm (SCA) has experienced wide spread use in solving optimization problems in many disciplines mainly due to its simplicity and efficiency. However, like many other meta-heuristics, SCA requires considerable amount of compute time when solving large size optimization problems. Therefore, in order to tackle such challenging problems efficiently, this work proposes Spark-SCA, a scalable and parallel implementation of SCA algorithm on Apache Spark distributed framework. Spark-SCA exploits Spark platform native support for iterative algorithms through in-memory computing to speed-up computations when handling large optimization problems. Both the design and implementation details of Spark-SCA are presented herein. The performance of Spark-SCA was compared to standard SCA on different benchmark functions with up to 1,000-dimension as well as three practical engineering design problems. Simulation experiments conducted on Amazon Web Services (AWS) public cloud demonstrated how Spark-SCA outperforms the standard version in terms of solution quality and run time as well as it competitiveness in exploring solution space of complex optimization problems.

Parallel implementation of pulse compression method on a multi-core digital signal processor

Pulse compression algorithm is widely used in radar applications. It requires a huge processing power in order to be executed in real time. Therefore, its processing must be distributed along multiple processing units. The present paper proposes a real time platform based on the multi-core digital signal processor (DSP) C6678 from Texas Instruments (TI). The objective of this paper is the optimization of the parallel implementation of pulse compression algorithm over the eight cores of the C6678 DSP. Two parallelization approaches were implemented. The first approach is based on the open multi processing (OpenMP) programming interface, which is a software interface that helps to execute different sections of a program on a multi core processor. The second approach is an optimized method that we have proposed in order to distribute the processing and to synchronize the eight cores of the C6678 DSP. The proposed method gives the best performance. Indeed, a parallel efficiency of 94% was obtained when the eight cores were activated.

Application of Parallel Processing Techniques to Satellite Ocean Color Data Processing

Recent advances demand that remote-sensing satellites efficiently process massive amounts of ocean color data. This paper compares the open multi-processing (OpenMP), the open computing language (OpenCL), the Message Passing Interface (MPI), the hybrid MPI/OpenMP, and the hybrid MPI/OpenCL in the parallel implementation of ocean color processing algorithms using data from the Geostationary Ocean Color Imager (GOCI), which is the first ocean color remote sensor operated in geostationary orbit. Since 2010, GOCI has observed ocean color around the Korean Peninsula and has generated hundreds of terabytes of big data. When any of the data-processing algorithms are updated, all preexisting data is required to be reprocessed, which can take hundreds of days because GOCI data are currently processed sequentially. Therefore, we attempted to develop an efficient parallel processing methodology for GOCI data. We tested well-known GOCI data-processing algorithms, like the chlorophyll (CHL) and total suspended solid (TSS) concentration estimation algorithms, using a cluster system. This cluster uses the Red Hat Linux operating system with two Intel Xeon 8-core processors (CPU), an AMD Radeon HD 7970 (GPU), and InfiniBand 4x QDR (network). As a result of this study we were able to improve the GOCI ocean color algorithms

Parallel methods for linear systems solution in extreme learning machines: an overview

This paper aims to present an updated review of parallel algorithms for solving square and rectangular single and double precision matrix linear systems using multi-core central processing units and graphic processing units. A brief description of the methods for the solution of linear systems based on operations, factorization and iterations was made. The methodology implemented, in this article, is a documentary and it was based on the review of about 17 papers reported in the literature during the last five years (2016-2020). The disclosed findings demonstrate the potential of parallelism to significantly decrease extreme learning machines training times for problems with large amounts of data given the calculation of the Moore Penrose pseudo inverse. The implementation of parallel algorithms in the calculation of the pseudo-inverse will allow to contribute significantly in the applications of diversifying areas, since it can accelerate the training time of the extreme learning machines with optimal results.

ПАРАЛЛЕЛЬНЫЙ АЛГОРИТМ НА CUDA ДЛЯ РЕШЕНИЯ ЗАДАЧ МНОГОФАЗНОЙ ФИЛЬТРАЦИИ МНОГОКОМПОНЕНТНОЙ ЖИДКОСТИ В ПОРИСТЫХ СРЕДАХ

The paper discusses the implementation of alternating direction implicit parallel algorithm with CUDA technology to solve the problems of multiphase filtration of a multicomponent fluid in porous media. To solve the tridiagonal equations systems of alternating direction implicit method, cyclic and parallel cyclic reduction methods were used. The results of the study showed that the implementation of cyclic and parallel cyclic reduction algorithms on modern GPUs is much more efficient than on CPUs.