Near Real-Time Estimation of Super-Resolved Depth and All-In-Focus Images from a Plenoptic Camera Using Graphics Processing Units by J. P. Lüke, F. Pérez Nava, J. G. Marichal-Hernández, J. M. Rodríguez-Ramos, F. Rosa

“…This paper presents a method for simultaneously recovering depth and all-in-focus images from a plenoptic camera in near real time using graphics processing units (GPUs). Previous methods for 3D reconstruction using plenoptic images suffered from the drawback of low spatial resolution. …”
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Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data by Sungyoung Lee, Min-Seok Kwon, Taesung Park

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Facile Conversion and Optimization of Structured Illumination Image Reconstruction Code into the GPU Environment by Kwangsung Oh, Piero R. Bianco

“…In addition, they do not take into consideration the processing power of the graphics processing unit (GPU) of the computer. To address these issues, we present simple but efficient approaches to first revise MATLAB code, followed by conversion to GPU-optimized code. …”
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High Performance Computing of Complex Electromagnetic Algorithms Based on GPU/CPU Heterogeneous Platform and Its Applications to EM Scattering and Multilayered Medium Structure by Zhe Song, Xing Mu, Hou-Xing Zhou

“…Unlike the previous parallel algorithms or those implemented by means of parallel programming on multicore CPU with OpenMP or on a cluster of computers with MPI, the new type of large-scale parallel processor based on graphics processing unit (GPU) has shown impressive ability in various scenarios of supercomputing, while its application in computational electromagnetics is especially expected. …”
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A Fast Fully Parallel Ant Colony Optimization Algorithm Based on CUDA for Solving TSP by Zhi Zeng, Yuxing Cai, Kwok L. Chung, Hui Lin, Jinwei Wu

“…Moreover, based on the compute unified device architecture (CUDA), the ACO is implemented as a kernel function on a graphics processing unit (GPU), which shortens the running time of massive iterations. …”
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A Fast CT Reconstruction Scheme for a General Multi-Core PC by Kai Zeng, Erwei Bai, Ge Wang

“…However, specialized hardware is expensive and not flexible. The graphics processing unit (GPU) in a current graphic card can only reconstruct images in a reduced precision and is not easy to program. …”
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Blood Vessel Segmentation and Classification of Diabetic Retinopathy with Machine Learning-Based Ensemble Model by Cihan Akyel, Bünyamin Ciylan

“…Artificial intelligence-based classification training using images as input takes a long time and requires high resource requirements such as Random Access Memory (RAM) and Graphics Processing Unit (GPU). By using Gray Level Cooccurrence Matrix (GLCM) attributes in the classification phase, a lower resource requirement was aimed for. …”
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Image Recognition and Simulation Based on Distributed Artificial Intelligence by Tao Fan

“…The parallelization processing of HOG features on graphics processing unit (GPU) is studied. GPU is used for high parallel and high-density calculations, and the calculation of HOG features is very complicated. …”
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High Performance GPU-Based Fourier Volume Rendering by Marwan Abdellah, Ayman Eldeib, Amr Sharawi

“…Due to the rapid evolution of its underlying architecture, the graphics processing unit (GPU) became an attractive competent platform that can deliver giant computational raw power compared to the central processing unit (CPU) on a per-dollar-basis. …”
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Speedup of Interval Type 2 Fuzzy Logic Systems Based on GPU for Robot Navigation by Long Thanh Ngo, Dzung Dinh Nguyen, Long The Pham, Cuong Manh Luong

“…In the T2FLS, many rules and algorithms can be speedup on a graphics processing unit (GPU) as long as the majority of computation a various stages and components are not dependent on each other. …”
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Multi-phase-field modeling and high-performance computation for predicting material microstructure evolution during sintering by Aoi Nakazawa, Shinji Sakane, Tomohiro Takaki

“…We also establish an efficient algorithm on graphics processing unit (GPU) to accelerate the computations of rigid-body motions of particles, which cause densification, and implement it on multiple GPUs in parallel. …”
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Implementation of Membrane Algorithms on GPU by Xingyi Zhang, Bangju Wang, Zhuanlian Ding, Jin Tang, Juanjuan He

“…In this work, we consider the implementation of membrane algorithms on the parallel computing device graphics processing unit (GPU). In such implementation, all cells of membrane algorithms can work simultaneously. …”
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Document-Level Neural TTS Using Curriculum Learning and Attention Masking by Sung-Woong Hwang, Joon-Hyuk Chang

“…First, through curriculum learning, our model automatically increases the length of the speech trained for each epoch, while reducing the batch size so that long sentences can be trained with a limited graphics processing unit (GPU) capacity. During synthesis, the document-level text is synthesized using only the necessary contexts of the current time step and masking the rest through an attention-masking mechanism. …”
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GPU-Accelerated Parallel FDTD on Distributed Heterogeneous Platform by Ronglin Jiang, Shugang Jiang, Yu Zhang, Ying Xu, Lei Xu, Dandan Zhang

“…Since both Central Processing Unit (CPU) and Graphics Processing Unit (GPU) resources are utilized, a faster execution speed can be reached compared to a traditional pure GPU code. …”
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Exploring Many-Core Design Templates for FPGAs and ASICs by Ilia Lebedev, Christopher Fletcher, Shaoyi Cheng, James Martin, Austin Doupnik, Daniel Burke, Mingjie Lin, John Wawrzynek

“…Throughout this study, we use a general-purpose graphics processing unit (GPGPU) implementation as a performance and area baseline. …”
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Detailed Nonlinear Modeling and High-Fidelity Parallel Simulation of MMC With Embedded Energy Storage for Wind Farm Grid Integration by Bingrong Shang, Ning Lin, Venkata Dinavahi

“…In this paper, a high-voltage dc (HVDC) link model based on the modular multilevel converter with embedded energy storage (MMC-EES) is presented and, utilizing the massively parallel computing feature of the graphics processing unit (GPU), its efficacy in compensating a varying wind energy generation is studied. …”
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Harnessing distributed GPU computing for generalizable graph convolutional networks in power grid reliability assessments by Somayajulu L.N. Dhulipala, Nicholas Casaprima, Audrey Olivier, Bjorn C. Vaagensmith, Timothy R. McJunkin, Ryan C. Hruska

“…For pre-training the GCN ARMA model across a variety of topologies, distributed graphics processing unit (GPU) computing afforded us significant training scalability. …”
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Accelerating AI-Based Battery Management System’s SOC and SOH on FPGA by Satyashil D. Nagarale, B. P. Patil

“…To provide an alternative solution to high power consuming processors such as central processing unit (CPU) and graphics processing unit (GPU), we implemented the model on field programmable gate Aarray (FPGA) PYNQ Z2 hardware device. …”
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Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes by S. Ubbiali, C. Kühnlein, C. Schär, L. Schlemmer, T. C. Schulthess, T. C. Schulthess, M. Staneker, H. Wernli

“…We benchmark all prototype codes on three European supercomputers characterized by diverse graphics processing unit (GPU) and central processing unit (CPU) hardware, node designs, software stacks, and compiler suites. …”
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Marigold: a machine learning-based web app for zebrafish pose tracking by Gregory Teicher, R. Madison Riffe, Wayne Barnaby, Gabrielle Martin, Benjamin E. Clayton, Josef G. Trapani, Gerald B. Downes

“…By leveraging a highly efficient, custom-designed neural network architecture, Marigold achieves reasonable training and inference speeds even on modestly powered computers lacking a discrete graphics processing unit. Notably, as a web app, Marigold does not require any installation and runs within popular web browsers on ChromeOS, Linux, macOS, and Windows. …”
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