Proposed Parallel Algorithm Research Articles

A Parallelized Database Damage Assessment Approach after Cyberattack for Healthcare Systems

In the current Internet of things era, all companies shifted from paper-based data to the electronic format. Although this shift increased the efficiency of data processing, it has security drawbacks. Healthcare databases are a precious target for attackers because they facilitate identity theft and cybercrime. This paper presents an approach for database damage assessment for healthcare systems. Inspired by the current behavior of COVID-19 infections, our approach views the damage assessment problem the same way. The malicious transactions will be viewed as if they are COVID-19 viruses, taken from infection onward. The challenge of this research is to discover the infected transactions in a minimal time. The proposed parallel algorithm is based on the transaction dependency paradigm, with a time complexity O((M+NQ+N^3)/L) (M = total number of transactions under scrutiny, N = number of malicious and affected transactions in the testing list, Q = time for dependency check, and L = number of threads used). The memory complexity of the algorithm is O(N+KL) (N = number of malicious and affected transactions, K = number of transactions in one area handled by one thread, and L = number of threads). Since the damage assessment time is directly proportional to the denial-of-service time, the proposed algorithm provides a minimized execution time. Our algorithm is a novel approach that outperforms other existing algorithms in this domain in terms of both time and memory, working up to four times faster in terms of time and with 120,000 fewer bytes in terms of memory.

A parallel randomized approximation algorithm for non-preemptive single machine scheduling with release dates and delivery times

Single machine scheduling is a very fundamental scheduling problem with extensive applications in various areas ranging from computer science to manufacturing. Also, this problem is the building block of different decomposition-based algorithms for shop scheduling problems. Most variants of the single machine scheduling problem are known to be NP-hard, and therefore, many efforts have been devoted to the development of approximation algorithms for solving them. In this paper, we design a parallel randomized approximation algorithm for the non-preemptive single machine scheduling problem with release dates and delivery times (1|rj,qj|Cmax), where the objective is to minimize the completion time of all jobs (i.e., makespan). To evaluate the performance of the proposed algorithm, we carry out a comprehensive experimental analysis on several instances of the problem. The results indicate that the proposed parallel algorithm can efficiently solve large instances achieving significant speedup on parallel systems with multiple cores.

Optimized Parallel Prefix Sum Algorithm on Optoelectronic Biswapped-Torus Architecture

The Biswapped-Torus is a recently reported optoelectronic node-symmetrical member of Biswapped-framework family. In this paper,optimized parallel approach is presented for prefix sum computation on [Formula: see text] Biswapped-Torus. The proposed parallel algorithm demands total 7[Formula: see text] electronic and three optical moves on odd network size or 7[Formula: see text] electronic and three optical moves on even network size. The algorithmic performance of the suggested parallel algorithm is also compared with the performances of recently reported optimal prefix sum algorithms on [Formula: see text] Biswapped-Mesh and [Formula: see text]-dimensional Biswapped Hyper Hexa-cell. Based on the comparative analysis, Biswapped-Torus claims to map prefix sum faster that require fewer communication moves compared to the Grid-based traditional architecture of biswapped family named Biswapped-Mesh. Moreover, the former also has architectural benefit of node-symmetry that leads to advantages such as easy embedding, mapping and designing of routing algorithms. Compared to symmetrical counter-part of biswapped family named Biswapped-Hyper Hexa-Cell, Biswapped-Torus is cost-efficient, but requires comparatively more communication moves for mapping prefix sum.

Component-based 2-/3-dimensional nearest neighbor search based on Elias method to GPU parallel 2D/3D Euclidean Minimum Spanning Tree Problem

We present improved data parallel approaches working on graphics processing unit (GPU) compute unified device architecture (CUDA) platform to build hierarchical Euclidean minimum spanning forest or tree (EMSF/EMST) for applications whose input only contains N points with arbitrary data distribution in 2D/3D Euclidean space. Characteristic of the proposed parallel algorithms follows “data parallelism, decentralized control and O(1) local memory occupied by each GPU thread”. This research has to solve GPU parallelism of component-based nearest neighbor search (component-based NNS), tree traversal, and other graph operations like union-find. For exact NNS, instead of using classical K-d tree search or brute-force computing method, we propose a K-d search method working based on dividing the Euclidean K-dimensional space into congruent and non-overlapping square/cubic cells where size of points in each cell is bounded. For component-based NNS, with the uniqueness property based on 2D/3D square/cubic space partition, we propose dynamic and static pruning techniques to prune unnecessary neighbor cells’ search. For tree traversal, instead of using breadth-first-search, this paper proposes CUDA kernels working with a distributed dynamic link list for selecting a local spanning tree’s shortest outgoing edge since size of local EMSTs in EMSF cannot be predicted. Source code is provided online and experimental comparisons are conducted on both 2D and 3D benchmarks with up to 107 points to build final EMST. Results show that applying K-d search with static pruning technique and the proposed operators totally working in parallel on GPU, our current implementation runs faster than our previous work and current optimal sequential dual-tree mlpack EMST library.

An efficient parallel strategy for high-cost prefix operation

The prefix computation strategy is a fundamental technique used to solve many problems in computer science such as sorting, clustering, and computer vision. A large number of parallel algorithms have been introduced that are based on a variety of high-performance systems. However, these algorithms do not consider the cost of the prefix computation operation. In this paper, we design a novel strategy for prefix computation to reduce the running time for high-cost operations such as multiplication. The proposed algorithm is based on (1) reducing the size of the partition and (2) keeping a fixed-size partition during all the steps of the computation. Experiments on a multicore system for different array sizes and number sizes demonstrate that the proposed parallel algorithm reduces the running time of the best-known optimal parallel algorithm in the average range of 62.7–79.6%. Moreover, the proposed algorithm has high speedup and is more scalable than those in previous works.

Optimal Parallel Prefix Sum Computation on Three-Dimensional Torus Network

In this article, an optimal parallel algorithm is suggested for fast computation of a crucial yet fundamental numerical problem in science named ‘prefix sum’ on a popular three-dimensional torus network. The proposed parallel algorithm demands total ‘4n + 2’ communication moves for mapping prefix sum problem on odd network sizes of n × n×n three-dimensional torus network, whereas total ‘4n’ communication moves is required for parallel mapping of same on even network sizes. In fact, a unit increase in each dimension from odd to even and even to odd network sizes resulted in the constant increase of 2 and 6 units, respectively, in the required communication moves for mapping prefix sum. The algorithmic performance of the proposed parallel algorithm can be compared to counter-part parallel prefix sum approaches on grid-based networks.

Scalable Parallel Algorithm for Solving Non-stationary Systems of Linear Inequalities

In this paper, a scalable iterative projection-type algorithm for solving non-stationary systems of linear inequalities is considered. A non-stationary system is understood as a large-scale system of inequalities in which coefficients and constant terms can change during the calculation process. The proposed parallel algorithm uses the concept of pseudo-projection which generalizes the notion of orthogonal projection. The parallel pseudo-projection algorithm is implemented using the parallel BSF-skeleton. An analytical estimation of the algorithm scalability boundary is obtained on the base of the BSF cost metric. The large-scale computational experiments were performed on a cluster computing system. The obtained results confirm the efficiency of the proposed approach.

Efficient parallel algorithm for detecting influential nodes in large biological networks on the Graphics Processing Unit

In biological networks, some nodes are more influential than others. The most influential nodes are those whose elimination induces a network collapse, and detecting these nodes is crucial in many circumstances. However, this is a difficult task when the size of the biological networks is large. In this paper, we have designed and implemented an efficient parallel algorithm for detecting influential nodes for large biological networks by exploiting a Graphics Processing Unit (GPU). The essential concept behind the proposed parallel algorithm is that several computationally expensive procedures in detecting influential nodes are redesigned and transformed into quite efficient GPU-accelerated primitives such as parallel sort, scan, and reduction. Four local metrics, including the Degree Centrality (DC), Companion Behavior (CB), Clustering Coefficient (CC), and H-Index, are used to measure the nodal influence. To evaluate the efficiency of the proposed parallel algorithm, five large real biological networks are employed in the experiments. The experimental results show that (1) the proposed parallel algorithm can achieve speedups of approximately 48∼94 over the corresponding serial algorithm; (2) compared to a baseline parallel algorithm developed on a multi-core CPU, the proposed parallel algorithm yields speedups of 5∼9 for DC and H-Index, while it is slightly slower for CB and CC due to the uneven degree distribution; and (3) when using DC and H-Index, the proposed parallel algorithm is capable of detecting the influential nodes in a large biological network consisting of 150 million edges in less than 3 s.

Parallel Algorithm for Spatial Data Mining Using CUDA

Recently, there is an increasing demand for applications utilizing maps and locations such as autonomous vehicles and location-based services. Since these applications are developed based on spatial data, interest in spatial data processing is increasing and various studies are being conducted. In this paper, I propose a parallel mining algorithm using the CUDA library to efficiently analyze large spatial data. Spatial data includes both geometric (spatial) and non-spatial (aspatial) attributes. The proposed parallel spatial data mining algorithm analyzes both the geometric and non-spatial relationships between two layers. The experiment was performed on graphics cards containing CUDA cores based on TIGER/Line data, which is the actual spatial data for the US census. Experimental results show that the proposed parallel algorithm using CUDA greatly improves spatial data mining performance.

A GPU parallel Bernstein algorithm for polynomial global optimization

We come up with a graphics processing unit (GPU) parallel Bernstein algorithm (BA) aimed at global optimization of multi-variate real polynomials (Garloff in Interval Comput 2:164–168, 1993). We first propose parallel algorithms for (a) computing the multi-index set associated with the Bernstein coefficients (BCs), (b) computing the initial set of BCs using the Matrix method (Ray and Nataraj in Reliab Comput 17(1):40–71, 2012), (c) finding the minimum BC from a given set of BCs, and (d) finding the BCs of the child patches from the parent patch. We then incorporate the above components into the proposed parallel Bernstein algorithm. All the parallel algorithms are programmed for GPU accelerating devices through compute unified device architecture. We compared performance of serial and GPU parallel BA using a test suite of 8 multivariate examples. For the test examples, the proposed parallel algorithm is found 30 times faster as compared to serial one, and needs 96% less time.

AMG based on compatible weighted matching for GPUs

We describe main issues and design principles of an efficient implementation, tailored to recent generations of Nvidia Graphics Processing Units (GPUs), of an Algebraic MultiGrid (AMG) preconditioner previously proposed by one of the authors and already available in the open-source package BootCMatch: Bootstrap algebraic multigrid based on Compatible weighted Matching for standard CPUs. The AMG method relies on a new approach for coarsening sparse symmetric positive definite (s.p.d.) matrices, named coarsening based on compatible weighted matching. It exploits maximum weight matching in the adjacency graph of the sparse matrix, driven by the principle of compatible relaxation, providing a suitable aggregation of unknowns which goes beyond the limits of the usual heuristics applied in the current methods. We adopt an approximate solution of the maximum weight matching problem, based on a recently proposed parallel algorithm, referred to as the Suitor algorithm, and show that it allows us to obtain good quality coarse matrices for our AMG on GPUs. We exploit inherent parallelism of modern GPUs in all the kernels involving sparse matrix computations both for the setup of the preconditioner and for its application in a Krylov solver, outperforming preconditioners available in the original sequential CPU code as well as the single node Nvidia AmgX library. Results for a large set of linear systems arising from discretization of scalar and vector partial differential equations (PDEs) are discussed.

Non-Stationary Acceleration Strategies for PageRank Computing

In this work, a non-stationary technique based on the Power method for accelerating the parallel computation of the PageRank vector is proposed and its theoretical convergence analyzed. This iterative non-stationary model, which uses the eigenvector formulation of the PageRank problem, reduces the needed computations for obtaining the PageRank vector by eliminating synchronization points among processes, in such a way that, at each iteration of the Power method, the block of iterate vector assigned to each process can be locally updated more than once, before performing a global synchronization. The parallel implementation of several strategies combining this novel non-stationary approach and the extrapolation methods has been developed using hybrid MPI/OpenMP programming. The experiments have been carried out on a cluster made up of 12 nodes, each one equipped with two Intel Xeon hexacore processors. The behaviour of the proposed parallel algorithms has been studied with realistic datasets, highlighting their performance compared with other parallel techniques for solving the PageRank problem. Concretely, the experimental results show a time reduction of up to 58.4 % in relation to the parallel Power method, when a small number of local updates is performed before each global synchronization, outperforming both the two-stage algorithms and the extrapolation algorithms, more sharply as the number of processes increases.

Parallel brute-force algorithm for deriving reset sequences from deterministic incomplete finite automata

A reset sequence (RS) for a deterministic finite automaton $\mathscr{A}$ is an input sequence that brings $\mathscr{A}$ to a particular state regardless of the initial state of $\mathscr{A}$. Incomplete finite automata (FA) are strong in modeling reactive systems, but despite their importance, there are no works published for deriving RSs from FA. This paper proposes a massively parallel algorithm to derive short RSs from FA. Experimental results reveal that the proposed parallel algorithm can construct RSs from FA with 16,000,000 states. When multiple GPUs are added to the system the approach can handle larger FA.

An efficient parallel and distributed solution to nonconvex penalized linear SVMs

Support vector machines (SVMs) have been recognized as a powerful tool to perform linear classification. When combined with the sparsity-inducing nonconvex penalty, SVMs can perform classification and variable selection simultaneously. However, the nonconvex penalized SVMs in general cannot be solved globally and efficiently due to their nondifferentiability, nonconvexity, and nonsmoothness. Existing solutions to the nonconvex penalized SVMs typically solve this problem in a serial fashion, which are unable to fully use the parallel computing power of modern multi-core machines. On the other hand, the fact that many real-world data are stored in a distributed manner urgently calls for a parallel and distributed solution to the nonconvex penalized SVMs. To circumvent this challenge, we propose an efficient alternating direction method of multipliers (ADMM) based algorithm that solves the nonconvex penalized SVMs in a parallel and distributed way. We design many useful techniques to decrease the computation and synchronization cost of the proposed parallel algorithm. The time complexity analysis demonstrates the low time complexity of the proposed parallel algorithm. Moreover, the convergence of the parallel algorithm is guaranteed. Experimental evaluations on four LIBSVM benchmark datasets demonstrate the efficiency of the proposed parallel algorithm.

A parallel path-following phase unwrapping algorithm based on a top-down breadth-first search approach

Path-following methods for two-dimensional phase unwrapping such as the Goldstein algorithm are the most efficient and robust methods in remote sensing, digital phase shifting, and nuclear magnetic resonance imaging, among others. Several authors have attempted to sketch parallel versions of path-following methods. However, only the first stages of the algorithm such as residue identification and branch-cut placement have been improved using parallel architectures, with limitations such as phase maps with a single continuous region and without isolated regions owing to the cuts. In this article, a systematic parallel Goldstein algorithm that can handle phase data with multi-regions and isolated regions is proposed. Our proposal can improve the three steps of the serial Goldstein algorithm, residue identification, branch cut, and integration. In particular, the integration step is formulated as a top-down breadth-first search problem on a graph for which a parallel algorithm was developed. Synthetic and real phase maps were used to validate the performance and robustness of the proposed parallel algorithm on a multicore architecture. For simulated and real phase maps, we obtained a speedup of 3.3 and 1.98, respectively, on a laptop computer with modest hardware resources.

A three-phase mapreduce-based algorithm for searching biomedical document databases

Retrieving information from large document databases is in the focus of scientific research in recent years. In this paper, a parallel algorithm for searching biomedical documents based on the MapReduce technique is presented. The algorithm consists of three phases: preprocessing phase, document representation phase, and searching phase. In the first phase, lemmatization and elimination of stop words are performed. In the second phase, each of the documents is represented as a list of pairs (word, tf-idf index of the word). The third phase represents the main searching procedure. It uses a specially designed ranking criterion, which is based on a combination of the term frequency - inverse document frequency (tf-idf) index and the indicator function for each query word. Four different versions of ranking criteria are proposed and analyzed. The algorithm performances are tested on different subsets of the large and well-known PubMed biomedical document database. The results obtained by the experiments indicate that the proposed parallel algorithm succeeds in finding high-quality results in a reasonable time. Comparing to the sequential variant of the algorithm, the experiments show that the parallel algorithm is more efficient since it finds high-quality solutions in significantly less time.

Privacy preserving similarity joins using MapReduce

Similarity join is an essential operator in data processing, mining and analysis. However, it is resource intensive and time consuming, particularly when processing big data. There is also a need to ensure data confidentiality in similarity joins, as joining between two files may result in personal information disclosure. Based on these two considerations, this paper proposes a MapReduce-based similarity joins with differential privacy technology (hereafter, referred to as PSJoin). The proposed parallel algorithm is designed to achieve high efficiency, in terms of answering similarity join queries privately and effectively. Specifically, the use of PSJoin ensures the preservation of privacy during the similarity join process and in the published results. A new private global ordering approach is presented in this paper, which is designed to deal with potential disclosure issues during the process, and a differential private similarity function is provided for this algorithm. Findings from our evaluations using large-scale real-world datasets demonstrate that our method can effectively guarantee privacy with only minimal accuracy loss in similarity queries, while offering good scalability consistently.

Square Matrix Multiplication Using CUDA on GP-GU

This paper focuses on matrix multiplication algorithm, particularly square parallel matrix multiplication using Computer Unified Device Architecture (CUDA) programming model with C programming language. Matrix multiplication is under the list of time-consuming problems that require s huge computational resources to improve its speedup. As many studies have shown, it is not easy to achieve high performance speedup in sequential matrix multiplication algorithm using larger input. The emphasis of this study is to propose a parallel algorithm to calculate the product of two square matrices with improved speedup performance compared to the sequential and OpenMP algorithms. In this research, biruni (super machine workstation) in the School of Computer Sciences, USM, Malaysia with General Purpose Graphics Processing Unit (GP-GU) was used to parallelize the matrix product algorithm. A comparison between parallel OpenMp versions and sequential algorithm with the proposed CUDA based algorithm of this research was carried out to evaluate the speedup performance of the proposed parallel CUDA based algorithm. The overall results show that CUDA based parallel matrix multiplication is approximately 400 times faster than sequential matrix multiplication and 4 times faster than OpenMp matrix multiplication algorithms, respectively. Therefore, the proposed parallel algorithm can help the researchers working with matrix multiplication application problems. It can also help mathematicians to easily calculate the product of any two matrices and obtain the result in a shorter time.

Link Prediction Based on Community Information and Its Parallelization

Link prediction refers to predicting the likelihood of the existence of an unknown link or a future link based on the observed information. It plays an important role in complex network analysis. Classical similarity indices based on common neighbor nodes consider that each common neighbor has the same effect to the link likelihood. However, in real networks, the contribution of common neighbor nodes belonging to different communities may be different. This paper proposes a new link prediction algorithm with an adjustable parameter based on community information (CI). Applying the proposed algorithm to nine similarity indices, a family of CI-based indices, referred to as CI forms, is proposed. We empirically investigate the impact of the CI on the accuracy of link prediction with nine classical indices. The experiments on ten real-world networks show that, compared with tradition local indices, the proposed CI forms have better overall prediction performance. Furthermore, a parallelization algorithm is developed to apply the proposed CI-based link prediction algorithm to large-scale complex networks using Spark GraphX. The experiment results show that the proposed parallel algorithm significantly improves the computing efficiency of link prediction.

Modern computer technologies for modeling the life cycle of buildings based on parallel calculations

The article deals with high-performance information technology (HPC) for the problems of stress-strain analysis at all stages of the life cycle of buildings and structures: construction, operation and reconstruction. The results of numerical simulation of high buildings using software as a processor component based on a new hybrid algorithm for solving systems of linear algebraic equations [1] with a symmetric positive-definite matrix that combines computation on multi-core processors and graphs. It has been found that to accelerate the calculations, hybrid systems that combine multi-core CPUs with accelerator coprocessors, including GPUs, are promising [5]. To test the effectiveness of the proposed parallel algorithm for solving systems of linear algebraic equations [1], numerical experiments were carried out at the most dangerous loads of a 27-story building. Results of numerical researches with use for preprocessor (input of initial data) and postprocessor (output of results of calculations) of processing of the LIRA-SAPR software complex are presented [2, 4, 6]. The results of numerical studies of the behavior of structures of high buildings have shown a multiple reduction in the time of solving systems of linear algebraic equations with symmetric matrices on multiprocessor (multi-core) computers with graphical accelerators using the proposed hybrid algorithms [1]. High-performance technologies based on parallel calculations give more effect than more complex processes: modeling of life cycle of high buildings, bridges, especially complex structures of NPPs, etc. for static and dynamic loads, including emergencies in normal and difficult geological conditions, which make up 70% of Ukraine's territories.