This paper proposes Hermes, a container-based preemptive GPU scheduling framework for accelerating hyper-parameter optimization in deep learning (DL) clusters. Hermes accelerates hyper-parameter optimization by time-sharing between DL jobs and priori...
This paper proposes an optimized shared memory access technique to enhance parallel processing performance and reduce memory accesses for the ARIA block cipher in GPU environments. To overcome the limited size of GPU shared memory, we merged ARIA&rsq...
Traditional population-based metaheuristic algorithms are effective in solving complex real-world problems but require careful strategy selection and parameter tuning. Metaphorless population-based optimization algorithms have gained importance due t...
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...
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...
Limited by the computational performance limits of the CPU(Central Processing Unit), the traditional Spark architecture struggles to achieve high throughput and low latency under the dual pressure of a large data scale and real-time requirements in c...
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...
This paper presents a novel Graphics Processing Unit (GPU) accelerated implementation of a modified Particle Swarm Optimization (PSO) algorithm specifically designed to solve large-scale Systems of Nonlinear Equations (SNEs). The proposed GPU-based p...
Quantitative kinetic parameters derived from dynamic contrast-enhanced (DCE) data are dependent on signal measurement quality and choice of pharmacokinetic model. However, the fundamental optimization analysis method is equally important and its impa...
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....
Background: Intermittent retail sales create substantial uncertainty for inventory control, particularly across large portfolios of heterogeneous item–location systems. This study develops a scalable GPU-accelerated Fokker–Planck framewor...
The simulation of massive multiple-input multiple-output (MIMO) channel models is becoming increasingly important for testing and validation of fifth-generation new radio (5G NR) wireless networks and beyond. However, simulation performance tends to...
This study aims to collect GPU rendering programs and analyze their characteristics to construct a benchmark dataset that reflects the characteristics of GPU rendering programs, providing a reference basis for designing the next generation of graphic...
Phase holography is a critical optical imaging and information processing technique with applications ranging from microscopy to optical communications. However, optimizing phase hologram generation remains a significant challenge due to the non-conv...
Machine-learning-based computer vision is increasingly versatile and being leveraged by a wide range of smart devices. Due to the limited performance/energy budget of computing units in smart devices, the careful implementation of computer vision alg...
Cryptography and associated technologies have existed for a long time. This field is advancing at a remarkable speed. Since the inception of its initial application, blockchain has come a long way. Bitcoin is a cryptocurrency based on blockchain, als...
Graph data structures have been used in a wide range of applications including scientific and social network applications. Engineers and scientists analyze graph data to discover knowledge and insights by using various graph algorithms. A breadth-fir...
With the development of engineering technology, engineering has higher requirements for the accuracy and the scale of simulation calculation. The computational efficiency of traditional serial programs cannot meet the requirements of engineering. The...
On GPU-based clusters, the training workloads of machine learning (ML) models, particularly neural networks (NNs), are often structured as Directed Acyclic Graphs (DAGs) and typically deployed for parallel execution across heterogeneous GPU resources...
The ever-increasing complexity of industrial and engineering problems poses nowadays a number of optimization problems characterized by thousands, if not millions, of variables. For instance, very large-scale problems can be found in chemical and mat...
The Unified Gas–Kinetic Wave–Particle (UGKWP) method is a multiscale method that offers high computational efficiency when solving complex high-Mach-number flows around spacecraft. When the UGKWP method, based on a Graphics Processing Uni...
This article describes the implementation of an efficient and fast in-house computed tomography (CT) reconstruction framework. The implementation principles of this cone-beam CT reconstruction tool chain are described here. The article mainly covers...
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...
In modern information technology systems, secure storage and transmission of personal and sensitive data are recognized as important tasks. These requirements are achieved through secure and robust encryption methods. Argon2 is an advanced cryptograp...
Using artificial intelligence tools to evaluate financial derivatives has become increasingly popular. PSO (particle swarm optimization) is one such tool. We present a comprehensive study of PSO for pricing American options on GPUs using OpenCL. PSO...
Trajectory Optimization (TO) and Reinforcement Learning (RL) offer complementary strengths for solving optimal control problems. TO efficiently computes locally optimal solutions but can struggle with non-convexity, while RL is more robust to non-con...
This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of Io...
In this survey, we provide a comprehensive classification of GPU task scheduling approaches, categorized by their underlying algorithmic techniques and evaluation metrics. We examine traditional methods—including greedy algorithms, dynamic prog...
The main task while developing a mobile robot is to achieve accurate and robust navigation in a given environment. To achieve such a goal, the ability of the robot to localize itself is crucial. In outdoor, namely agricultural environments, this task...
Lagrangian Relaxation (LR) is an effective method for solving spatial optimization problems in geospatial analysis and GIS. Among others, it has been used to solve the classic p-median problem that served as a unified local model in GIS since the 199...
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...
The exponential growth of AI applications has intensified the demand for efficient inference hardware capable of delivering low-latency, high-throughput, and energy-efficient performance. This study presents a systematic, empirical comparison of GPU-...
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...
The report considers the task of training a multilayer perceptron, formulated as a problem of multiobjective optimization under uncertainty. To solve this problem, the principle of vector minimax was used. A parallel software implementation of a hier...
Matrix–matrix multiplication is of singular importance in linear algebra operations with a multitude of applications in scientific and engineering computing. Data structures for storing matrix elements are designed to minimize overhead informat...
With the development of the Internet of Things (IoT) and cloud computing technology, various cryptographic systems have been proposed to protect increasing personal information. Recently, Post-Quantum Cryptography (PQC) algorithms have been proposed...
Deep learning (DL) has emerged as a promising alternative for signal detection in multiple-input multiple-output (MIMO) wireless communication systems, achieving a performance close to the optimal maximum likelihood (ML) criterion. However, implement...
The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler...
Pseudospectral methods are effective tools for solving optimal control problems, but they result in large-scale nonlinear programming (NLP) problems that are computationally demanding. A major bottleneck is the repeated evaluation of the objective fu...
Modern terrain rendering frameworks often struggle with the inherent trade-off between geometric continuity and hardware utilization. State-of-the-art watertight tessellation methods typically rely on explicit adjacency management and multi-pass sync...
Pulsar and fast radio burst observations widely adopt coherent dedispersion to compensate for dispersion effects introduced by the interstellar medium. The multisegment overlap-save method (MS-OSM) was proposed to alleviate the extremely large FFT re...
Short-term GPU utilization forecasts are useful for scheduling, resource allocation, and capacity planning, but production traces are rarely smooth. They contain spikes, regime changes, idle periods, and incomplete observations. We study this problem...
The direct porting of the Range Migration Algorithm to GPUs for three-dimensional (3D) cylindrical synthetic aperture radar (CSAR) imaging faces difficulties in achieving real-time performance while the architecture and programming models of GPUs sig...
Neural-network-based image denoising is one of the promising approaches to deal with problems in image processing. In this work, a deep fully symmetric convolutional–deconvolutional neural network (FSCN) is proposed for image denoising. The pro...
Dimensionality reduction (DR) is of great significance for simplifying and optimizing hyperspectral image (HSI) features. As a widely used DR method, kernel minimum noise fraction (KMNF) transformation preserves the high-order structures of the origi...
Registration of long-wave infrared (LWIR) hyperspectral images with their thermal and emissivity components has until now received comparatively less attention with respect to the visible near and short wave infrared hyperspectral images. In this pap...
This paper introduces a parallelized approach to reconstruct Koopman computational graphs from the perspective of parallel computing to address the computational efficiency bottleneck in approximating Koopman operators within high-dimensional spaces....
Convolution is the most computationally intensive task of the Convolutional Neural Network (CNN). It requires a lot of memory storage and computational power. There are different approaches to compute the solution of convolution and reduce its comput...
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...
The Industrial Internet of Things (IIoT) increasingly relies on vector embeddings for analytics and AI-driven applications such as anomaly detection, predictive maintenance, and sensor fusion. Efficient approximate nearest neighbor search (ANNS) is e...
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