Science method

# Parallel Computing - Science method

Explore the latest publications in Parallel Computing, and find Parallel Computing experts.

Publications related to Parallel Computing (10,000)

Sorted by most recent

With the development of IoT technology, more and more IoT devices are connected to the network. Due to the hardware constraints of IoT devices themselves, it is difficult for developers to embed security software into them. Therefore, it is better to protect IoT devices at the traffic level. The effect of malicious traffic detection based on neural...

The Large Hadron Collider beauty (LHCb) experiment at CERN is undergoing an upgrade in preparation for the Run 3 data collection period at the Large Hadron Collider (LHC). As part of this upgrade, the trigger is moving to a full software implementation operating at the LHC bunch crossing rate. We present an evaluation of a CPU-based and a GPU-based...

Numerical methods play a dominant role in structural reliability analysis, and the goal has long been to produce a failure probability estimate with a desired level of accuracy using a minimum number of performance function evaluations. In the present study, we attempt to offer a Bayesian perspective on the failure probability integral estimation,...

Defect prediction in software development is a very active topic of study. Software defect prediction (SDP) findings give the list of defect-prone source code artefacts, enabling quality assurance teams to efficiently allocate limited resources for validating software products. In order to enable both developers and reduce the time to market for mo...

Uncertainties existing in physical and engineering systems can be characterized by different kinds of mathematical models according to their respective features. However, efficient propagation of hybrid uncertainties via an expensive-to-evaluate computer simulator is still a computationally challenging task. In this contribution, estimation of resp...

The scalability issue has always been a bottleneck for formal concept analysis (FCA) since the number of formal concepts is possibly exponential in the formal context. Motivated by the need to handle large formal contexts efficiently, we propose a parallel algorithm for computing formal concepts, the aim of which is to make Close-by-One (CbO) and i...

Various numerical methods have been extensively studied and used for reliability analysis over the past several decades. However, how to understand the effect of numerical uncertainty (i.e., numerical error due to the discretization of the performance function) on the failure probability is still a challenging issue. The active learning probabilist...

With the development of multimedia technology and network technology applications, it is possible to implement online teaching systems in schools. This article aims to realize the design of online English teaching system based on interactive speech recognition system. The teaching system uses the characteristics of English course learning to develo...

Domain Decomposition Methods (DDM) are a set of numerical techniques that efficiently implement parallel computing for the structural analysis of large domains. This work presents the implementation of mixed DDM for linear elasticity problems along with non-linear problems such as crack propagation. In addition, optimization algorithms have been us...

The Jacobi iterative algorithm has the characteristic of low computational load, and multiple components of the solution can be solved independently. This paper applies these characteristics to the ternary optical computer, which can be used for parallel optimization because it has a large number of data bits and reconfigurable processor bits. Ther...

Swarm Intelligence (SI) algorithms are frequently applied to tackle complex optimization problems. SI is especially used when good solutions are requested for NP hard problems within a reasonable response time. And when such problems possess a very high dimensionality, a dynamic nature, or present intrinsic complex intertwined independent variables...

As an important approach of distributed artificial intelligence, multi-agent system provides an efficient way to solve large-scale computational problems through high-parallelism processing with nonlinear interactions between the agents. However, the huge capacity and complex distribution of the individual agents make it difficult for efficient har...

Smoothed Particle Hydrodynamics (SPH) has emerged as a viable alternative to conventional computational methods for the solution of the partial differential equations of continuum mechanics. For problems with large deformations (in fluid and solid mechanics), SPH exhibits clear advantages over conventional methods. Furthermore, technological progre...

Cellular networks equipped with mobile edge computing (MEC) servers can be beneficial for unmanned aerial vehicles (UAVs) with limited onboard computation power and battery life-time. In this paper, we compare energy consumption of a UAV connected to cellular MEC servers in various possible scenarios such as onboard/MEC processing or parallel compu...

Asynchronous evolutionary algorithms are becoming increasingly popular as a means of making full use of many processors while solving computationally expensive search and optimization problems. These algorithms excel at keeping large clusters fully utilized, but may sometimes inefficiently sample an excess of fast‐evaluating solutions at the expens...

Trajectory data represent a trace of an object that changes its position in space over time. This kind of data is complex to handle and analyze, since it is generally produced in huge quantities, often prone to errors generated by the geolocation device, human mishandling, or area coverage limitation. Therefore, there is a need for software specifi...

The embarrassingly parallel nature of the Bisection Algorithm makes it easy and efficient to program on a parallel computer, but with an expensive time cost when all symmetric tridiagonal eigenvalues are wanted. In addition, few methods can calculate a single eigenvalue in parallel for now, especially in a specific order. This paper solves the issu...

Manuscript symbols can be stored, recognized and retrieved from an entropic digital memory that is associative and distributed but yet declarative; memory retrieval is a constructive operation, memory cues to objects not contained in the memory are rejected directly without search, and memory operations can be performed through parallel computation...

We propose a stable, parallel approach to train Wasserstein conditional generative adversarial neural networks (W-CGANs) under the constraint of a fixed computational budget. Differently from previous distributed GANs training techniques, our approach avoids inter-process communications, reduces the risk of mode collapse and enhances scalability by...

The assembly of overlapping grids is a key technology to deal with the relative motion of multi-bodies in computational fluid dynamics. However, the conventional implicit assembly techniques for overlapping grids are often confronted with the problem of complicated geometry analysis, and consequently, they usually have a low parallel assembly effic...

Recently, memory-based approaches show promising results on semi-supervised video object segmentation. These methods predict object masks frame-by-frame with the help of frequently updated memory of the previous mask. Different from this per-frame inference, we investigate an alternative perspective by treating video object segmentation as clip-wis...

Multiprocessor systems with parallel computing play an important role in data processing. Considering the optimal use of existing computing systems, scheduling on parallel systems has gained great significance. Usually, a sequential program to run on parallel systems is modeled by a task graph. Because scheduling of task graphs onto processors is c...

We revisit parallel-innermost term rewriting as a model of parallel computation on inductive data structures and provide a corresponding notion of runtime complexity parametric in the size of the start term. We propose automatic techniques to derive both upper and lower bounds on parallel complexity of rewriting that enable a direct reuse of existi...

In view of the technical challenge that convolutional neural networks involve in a large amount of computation caused by the information redundancy of the interlayer activation, a flexible sparsity-aware accelerator is proposed in this paper. It realizes the basic data transmission with coarse-grained control and realizes the transmission of sparse...

Rate theory (RT) is a commonly used method to simulate the evolution of material defects. A promising numerical method, exponential time difference (ETD), can reduce the stiff RT equations to explicit ordinary differential equations (ODEs). Previous implementations of ETD on the “Sunway TaihuLight” supercomputer suffer from high computation cost an...

With the rapid development of network technology and parallel computing, clusters formed by connecting a large number of PCs with high-speed networks have gradually replaced the status of supercomputers in scientific research and production and high-performance computing with cost-effective advantages. The research purpose of this paper is to integ...

This paper is concerned with approximating the scalar response of a complex computational model subjected to multiple input interval variables. Such task is formulated as finding both the global minimum and maximum of a computationally expensive black-box function over a prescribed hyper-rectangle. On this basis, a novel non-intrusive method, calle...

We present numerical experiments for geophysics electromagnetic (EM) modeling based upon high-order edge elements and supervised $h+p$ refinement approaches on massively parallel computers. Our high-order $h+p$ refinement strategy is based on and extends the PETGEM code. We focus on the performance study in terms of accuracy, convergence rate, and...

SkePU is a pattern-based high-level programming model for transparent program execution on heterogeneous parallel computing systems. A key feature of SkePU is that, in general, the selection of the execution platform for a skeleton-based function call need not be determined statically. On single-node systems, SkePU can select among CPU, multithread...

Fog computing provides cloud services at the user end. User requests are processed on the fog nodes deployed near the end-user layer in a fog computing environment. Fog computing can play an essential role in executing parallel computational tasks, i.e. scientific workflow applications. To run workflow applications on the cloud datacenters may take...

The principal and supreme concern in medical information systems is the issue of safe guarding the electronic health records of patients. Defending a health care industry’s computer network against attacks on its nodes and links requires placing mobile guards on the nodes of a network. Bloom graph topologies are attractive networks that are potenti...

In this paper, a two-wing chaotic system is transformed into a four-wing chaotic system and an eight-wing chaotic system using fractal processing and the dynamic characteristics of new multi-wing chaotic systems are analyzed. The encryption of the image is accomplished by combining the eight-wing chaotic system and the improved AES algorithm. The n...

In mobile edge computing (MEC), task offloading can solve the problem of resource constraints on mobile devices effectively, but it is not always optimal to offload all the computation subtasks of an application. In order to solve the energy and delay of fine-grained offloading in MEC, this paper proposes a novel offloading scheduling based on mult...

In this article, we discuss the numerical solution of Boolean polynomial programs by algorithms borrowing from numerical methods for differential equations, namely the Houbolt scheme, the Lie scheme, and a Runge-Kutta scheme. We first introduce a quartic penalty functional (of Ginzburg-Landau type) to approximate the Boolean program by a continuous...

The visualization of geographic vector data is an important premise for spatial analysis and spatial cognition. Traditional geographic vector data visualization methods are data-driven, and their computational costs have increased rapidly with the growth of the scale of data used. Even if the distributed parallel strategy is used, it is still diffi...

The methods of Chinese relation extraction(CRE) based on the neural network can be divided into two categories according to the input mode(word-based and character-based). The performance of word-based models depends on the accuracy of word segmentation. Unfortunately, there are still errors in existing word segmentation tools (methods). Among the...

In this paper, a parallel computing method is proposed to perform the background denoising and wheezing detection from a multi-channel recording captured during the auscultation process. The proposed system is based on a non-negative matrix factorization (NMF) approach and a detection strategy. Moreover, the initialization of the proposed model is...

In recent years, with the rapid development of computing technology, developing parallel procedures to solve large-scale ranking and selection (R&S) problems has attracted a lot of research attention. In this paper, we take fixed-budget R&S procedure as an example to investigate potential issues of developing parallel procedures. We argue that to m...

Grid renumbering techniques have been shown to be effective in improving the efficiency of computational fluid dynamics (CFD) numerical simulations based on the finite volume method (FVM). However, with the increasing complexity of real-world engineering scenarios, there is still a huge challenge to choose better sequencing techniques to improve pa...

The parallel computing framework Spark 2.x adopts a unified memory management model. In the case of the memory bottleneck, the memory allocation of active tasks and the RDD(Resilient Distributed Datasets) cache causes memory contention, which may reduce computing resource utilization and persistence acceleration effects, thus affecting program exec...

The need for computation speed is ever increasing. A promising solution for this requirement is parallel computing but the degree of parallelism in electronic computers is limited due to the physical and technological barriers. DNA computing proposes a fascinating level of parallelism that can be utilized to overcome this problem. This paper presen...

The paper is devoted to an approach to solving a problem of the efficiency of parallel computing. The theoretical basis of this approach is the concept of a $Q$-determinant. Any numerical algorithm has a $Q$-determinant. The $Q$-determinant of the algorithm has clear structure and is convenient for implementation. The $Q$-determinant consists of $Q...

This paper presents $\mu\text{KG}$, an open-source Python library for representation learning over knowledge graphs. $\mu\text{KG}$ supports joint representation learning over multi-source knowledge graphs (and also a single knowledge graph), multiple deep learning libraries (PyTorch and TensorFlow2), multiple embedding tasks (link prediction, enti...

ASIC hash engines are specifically optimized for parallel computations of cryptographic hashes and thus a natural environment for mounting brute-force attacks on hash functions. Two fundamental advantages of ASICs over general purpose computers are the area advantage and the energy efficiency. The memory-hard functions approach the problem by reduc...

Multiple sequence alignment approaches refer to algorithmic solutions for the alignment of biological sequences. Since multiple sequence alignment has exponential time complexity when a dynamic programming approach is applied, a substantial number of parallel computing approaches have been implemented in the last two decades to improve their perfor...

Quantization for CNN has shown significant progress with the intention of reducing the cost of computation and storage with low-bitwidth data representations. There are, however, no systematic studies on how an existing full-bitwidth processing unit, such as ALU in CPUs and DSP in FPGAs, can be better utilized to deliver significantly higher comput...

Background
Cluster analysis is an integral part of precision medicine and systems biology, used to define groups of patients or biomolecules. Consensus clustering is an ensemble approach that is widely used in these areas, which combines the output from multiple runs of a non-deterministic clustering algorithm. Here we consider the application of c...

We will describe our developments leading to an automated workflow approach that will utilize machine learning methods to combine first principles density functional theory (DFT) calculations with classical Monte-Carlo (MC) simulations. This method allows the investigation of the statistical mechanics and the finite temperature behavior of real mat...

The performance of brain-computer interface (B-CI) is reflected in the accuracy and information transmission rate (ITR). In this paper, a novel dynamic inverse learning network (DILN) is proposed for brain signal recognition of the BCI system. Different from the existing neural networks, the weights of DILN between inputs and hidden are obtained by...

With the emergence and development of computer technology, the computing power of computer is also constantly improving and has driven the development of other fields. As an important way to improve the computing power, availability, and reliability of computer system, parallel computing is the hot spot and trend of the development of computer tech...

In this paper we present an alternative approach to formalize the theory of logic programming. In this formalization we allow existential quantified variables and equations in queries. In opposite to standard approaches the role of answer will be played by existentially quantified systems of equations. This allows us to avoid problems when we deal...

This paper presents the development and implementation of a framework for automated numerical model generation and dynamic analysis of bridges considering soil-structure interaction (SSI). The framework allows the automatic generation of a detailed finite element (FE) continuum model, which comprises the three-dimensional site's stratigraphy and br...

One of the problems encountered in the field of computer vision and video data analysis is the extraction of information from low-contrast images. This problem can be addressed in several ways, including the use of histogram equalisation algorithms. In this work, a method designed for this purpose—the Contrast-Limited Adaptive Histogram Equalizatio...

Simulation of the localization and development of plastic shear bands in fluid-saturated rocks is considered using a nonlinear poroelastoplastic model generalizing Biot’s model for a two-phase fluid-saturated porous medium under small and finite strains. A Drucker-Prager yield criterion and a non-associated plastic flow rule are applied to describe...

Visualizing the uncertainty of ensemble simulations is challenging due to the large size and multivariate and temporal features of ensemble data sets. One popular approach to studying the uncertainty of ensembles is analyzing the positional uncertainty of the level sets. Probabilistic marching cubes is a technique that performs Monte Carlo sampling...

Due to the increasing volume and variety of their data, major challenges are being faced by health care providers in integrating effectively analyzing healthcare information .Traditional health information technology systems such as EHR and PHR systems used variety of technical and semantics standards for representing and storing data which are bas...