This work evaluates the computing performance of finite element analysis in structural mechanics using modern multi-GPU systems. We can avoid the usual memory limitations when using one GPU device for many-core computing using multiple GPUs for scien...
Spectral unmixing is a vital procedure in hyperspectral remote sensing image exploitation. The linear mixture model has been widely utilized to unmix hyperspectral images by extracting a set of pure spectral signatures, called endmembers in hyperspec...
Electrical capacitance tomography (ECT) is one of non-invasive visualization techniques which can be used for industrial process monitoring. However, acquiring images trough 3D ECT often requires performing time consuming complex computations on larg...
Currently, many of the leading supercomputers are equipped with GPUs from manufacturers such as NVIDIA, AMD, or Intel, which provide substantial parallelism and high throughput. It is common for a single compute node to host multiple GPUs (typically...
Robust vehicle detection in real-world traffic surveillance remains challenging due to degraded imagery caused by motion blur, adverse weather, and low illumination, which significantly increases detector sensitivity to hyperparameter configurations....
Molecular dynamics simulations take advantage of supercomputing environments, e.g., to solve molecular systems composed of millions of atoms. Supercomputers are increasing their computing and memory power while they are becoming more complex with the...
Industrial CFD and DEM benchmarks were reanalyzed to examine how workload characteristics and hardware constraints affect GPU performance in Ansys Fluent and Ansys Rocky. The dataset comprised separate CFD and DEM calculations, not coupled fluid&ndas...
This paper addresses the challenge of extracting feature points and image matching in Synthetic Aperture Radar (SAR) satellite images, particularly focusing on large-scale embedding. The widely used Scale Invariant Transform (SIFT) algorithm, success...
Synthetic Aperture Radar (SAR) satellite image ortho-rectification requires pixel-level calculations, which are time-consuming. Moreover, for SAR images with large overlapping areas, the processing time for ortho-rectification increases linearly, sig...
Nowadays, Biclustering is one of the most widely used machine learning techniques to discover local patterns in datasets from different areas such as energy consumption, marketing, social networks or bioinformatics, among them. Particularly in bioinf...
We introduce <tt>ssc-cdi</tt>, an open-source software package from the Sirius Scientific Computing family, designed for memory-efficient, single-node multi-GPU ptychography reconstruction. <tt>ssc-cdi</tt> offers a range of r...
The reproduction of the brain ’sactivity and its functionality is the main goal of modern neuroscience. To this aim, several models have been proposed to describe the activity of single neurons at different levels of detail. Then, single neuron...
This paper presents a novel parallel-GPU discontinuous Galerkin time domain (DGTD) method with a third-order local time stepping (LTS) scheme for the solution of multi-scale electromagnetic problems. The parallel-GPU implementations were developed ba...
In this work, a novel multi-objective efficient global optimization (EGO) algorithm, namely GMOEGO, is presented by proposing an approach of available threads’ multi-objective infill criterion. The work applies the outstanding hypervolume-based...
Large-scale constrained multi-objective optimization problems (LCMOPs) pose significant challenges due to the curse of dimensionality, complex constraint landscapes, and high computational overhead. In time-sensitive scenarios, existing large-scale c...
The importance of autonomous marine vehicles is increasing in a wide range of ocean science and engineering applications. Multi-objective optimization, where trade-offs between multiple conflicting objectives are achieved (such as minimizing expected...
Interactive multi-beam laser machining simulation is crucial in the context of tool path planning and optimization of laser machining parameters. Current simulation approaches for heat transfer analysis (1) rely on numerical Finite Element methods (o...
The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method is one of the most widely used multi-criteria decision-making (MCDM) approaches in industrial, financial, and scientific fields. However, its sequential computational...
To simulate dendrite growth with different orientations more efficiently, a high-performance cellular automata (CA) model based on heterogenous central processing unit (CPU)+ graphics processing unit (GPU) architecture has been proposed in this paper...
Cardiac CINE, a form of dynamic cardiac MRI, is indispensable in the diagnosis and treatment of heart conditions, offering detailed visualization essential for the early detection of cardiac diseases. As the demand for higher-resolution images increa...
Kernel density estimation (KDE) is a commonly used method for spatial point pattern analysis, but it is computationally demanding when analyzing large datasets. GPU-based parallel computing has been adopted to address such computational challenges. T...
It is well known that aurorae have very high research value, but the data volume of aurora spectral data is very large, which brings great challenges to storage and transmission. To alleviate this problem, compression of aurora spectral data is indis...
Power flow analysis is critical for power systems due to the development of multiple energy supplies. For safety, stability, and real-time response in grid operation, grid planning, and analysis of power systems, it requires designing high-performanc...
In this work, we present a multi-camera surveillance system based on the use of self-organizing neural networks to represent events on video. The system processes several tasks in parallel using GPUs (graphic processor units). It addresses multiple v...
This paper introduces a GPU (graphics processing unit)-based fast motion synthesis algorithm for a large crowd. The main parts of the algorithms were selecting the most appropriate joint model given adaptive screen-space occupancy of each character a...
Discrete event system specification (DEVS) has been widely used in event-driven simulations for sensor-driven Internet of things (IoT) applications, such as monitoring the spread of fire disaster. Event-driven models for IoT sensor nodes and their co...
As an important method for seismic data processing, reverse time migration (RTM) has high precision but involves high-intensity calculations. The calculation an RTM surface offset (shot–receiver distance) domain gathers provides intermediary data for...
This paper present contributions to the state-of-the art for graphics processing unit (GPU-based) embedded intelligence (EI) research for architectures and applications. This paper gives a comprehensive review and representative studies of the emergi...
Graphics processing units (GPUs) have been in the spotlight in various fields because they can process a massive amount of computation at a relatively low price. This research proposes a performance acceleration framework applied to Monte Carlo metho...
In a distributed deep learning system, a parameter server and workers must communicate to exchange gradients and parameters, and the communication cost increases as the number of workers increases. This paper presents a communication data optimizatio...
As a foundation for numerical solvers in computational electromagnetics, particularly for multiphysics and electromagnetic compatibility applications, Jefimenko’s equations offer a generalized solution to Maxwell’s equations, enabling the...
In detection-free tracking, after users freely designate the location of the object to be tracked in the first frame of the video sequence, the location of the object is continuously found in the following video frame sequence. Recently, technologies...
This work deals with the improvement of multi-target prediction models through a proposed optimization called Selection Of medical Features by Induced Alterations in numeric labels (SOFIA). This method performs a data transformation when: (1) weighti...
Nowadays, almost all network traffic is encrypted. Attackers hide themselves using this traffic and attack over encrypted channels. Inspections performed only on packet headers and metadata are insufficient for detecting cyberattacks over encrypted c...
A Python non-uniform fast Fourier transform (PyNUFFT) package has been developed to accelerate multidimensional non-Cartesian image reconstruction on heterogeneous platforms. Since scientific computing with Python encompasses a mature and integrated...
In recent years, demand has been increasing for target detection and tracking from aerial imagery via drones using onboard powered sensors and devices. We propose a very effective method for this application based on a deep learning framework. A stat...
Large-scale atmospheric dispersion model for emergency response to nuclear accidents requires high computational efficiency and numerical reliability. A GPU-oriented Lagrangian particle dispersion model was developed within FLEXPART framework to addr...
The ability to automate machine translation has various applications in international commerce, medicine, travel, education, and text digitization. Due to the different grammar and lack of clear word boundaries in Chinese, it is challenging to conduc...
Most modern mobile cyber-physical systems such as smartphones come equipped with multi-processor systems-on-chip (MPSoCs) with variant computing capacity both to cater to performance requirements and reduce power consumption when executing an applica...
To meet the high throughput requirement of communication systems, the design of high-throughput low-density parity-check (LDPC) decoders has attracted significant attention. This paper proposes a high-throughput GPU-based LDPC decoder, aiming at the...
This paper presents a fast and robust approach to evaluate the singular values of small (e.g.,
High-Performance Computing (HPC) has become essential for training medium- and large-scale Artificial Intelligence (AI) models, yet two bottlenecks remain under-exploited: the semantic coherence of training data and the interaction between distribute...
With respect to distributed MIMO radar systems, conventional far-field detection methods fail under near-field conditions due to significant wavefront curvature, which inevitably results in target energy loss and erroneous parameter estimation. To so...
The Biconjugate Gradient Stabilized (BiCGStab) algorithm is a widely used iterative method for solving large, sparse, and non-symmetric linear systems in scientific and engineering applications. While efficient, its performance is constrained by high...
Graphics processing units (GPUs) facilitate massive parallelism and high-capacity storage, and thus are suitable for the iterative reconstruction of ultrahigh-resolution micro computed tomography (CT) scans by on-the-fly system matrix (OTFSM) calcula...
In real applications, massive data with graph structures are often incomplete due to various restrictions. Therefore, graph data imputation algorithms have been widely used in the fields of social networks, sensor networks, and MRI to solve the graph...
While the importance of indexing strategies for optimizing query performance in database systems is widely acknowledged, the impact of rapidly evolving hardware architectures on indexing techniques has been an underexplored area. As modern computing...
Modern video coding standards make use of sub-pixel motion estimation to improve the video quality and reduce the bitrate. It is known that the fraction motion estimation (FME) part follows the integer motion estimation (IME) and adds an extra comput...
The Monte Carlo sampling (MCS) method is a simple and practical way for hydrological model parameter optimization. The MCS procedure is used to generate a large number of data points. Therefore, its computational efficiency is a key issue when applie...
Nowadays, driven by the exponential growth of parameters and training data of AI applications and Large Language Models, a single GPU is no longer sufficient in terms of computing power and storage capacity. Building high-performance multi-GPU system...
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