High Performance 3D PET Reconstruction Using Spherical Basis Functions on a Polar Grid by J. Cabello, J. E. Gillam, M. Rafecas

“…In this work, we have implemented the reconstruction algorithm using Graphical Processing Unit (GPU) technology for speed and a precomputed Monte-Carlo-calculated SRM for accuracy. …”
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Graph convolutional network as a fast statistical emulator for numerical ice sheet modeling by Younghyun Koo, Maryam Rahnemoonfar

“…Although several deep learning emulators using graphic processing units (GPUs) have been proposed to accelerate ice sheet modeling, most of them rely on convolutional neural networks (CNNs) designed for regular grids. …”
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From data to diagnosis: leveraging deep learning in IoT-based healthcare by Miracle A. Atianashie, Chukwuma Chinaza Adaobi

“…This article also discusses the pivotal role of Graphics Processing Units (GPUs)-accelerated computing and the expertise of medical professionals in refining deep learning models for clinical applications. …”
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Comparison and Selection of Channel Desilting Schemes Based on Hydrodynamic Models by TONG Yu, JIA Jinrui, HOU Jingming, JING Jing, ZHANG Fan, ZHOU Qingshi, WANG Tian, LIU Yuling

“…In order to explore the desilting effects of different channel desilting schemes, a hydrodynamic model accelerated by a graphic process unit (GPU) was used to construct a flood evolution model for the study area and simulate the flood inundation situation of the channels in the study area after implementing four desilting schemes at different return periods. …”
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Deep Learning Algorithm Analysis of Potato Disease Classification for System on Chip Implementation by John Adebisi, Sesham Srinu, Varqa Mitonga

“…This study proposes the potential of aligning existing software-based Central Processing Units (CPUs) and Graphic Processing Units (GPUs) with FPGA-based potato disease classification using CNNs. …”
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Bringing Intelligence to SAR Missions: A Comprehensive Dataset and Evaluation of YOLO for Human Detection in TIR Images by Mostafa Rizk, Israa Bayad

“…Also, the trained models are deployed on graphical processing units. The tiniest trained model, YOLOv8n, achieves an inference rate of 273.6 frames per second (FPS) while the largest model, YOLOv8, achieves an inference rate of 100.29 FPS. …”
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High Performance Frequent Subgraph Mining on Transaction Datasets: A Survey and Performance Comparison by Bismita S. Jena, Cynthia Khan, Rajshekhar Sunderraman

“…Many authors have tried to achieve better performance using Graphic Processing Units (GPUs) which has multi-fold improvement over in-memory while dealing with large datasets. …”
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A Fast Parallel Processing Algorithm for Triangle Collision Detection Based on AABB and Octree Space Slicing in Unity3D by Kunthroza Hor, Nak-Jun Sung, Jun Ma, Min-Hyung Choi, Min Hong

“…We describe an improved algorithm through a comparison in the application of a central processing unit (CPU) and graphics processing units (GPU). Although leveraging CPU for computational speed improvements has gained significant recognition in recent years, this study distinguishes by tracking 3D geometry bounding volume hierarchy (BVH) constructed in a spatial decomposition structure with a focus on Octree-based Axis-Aligned Bounding Box (AABB) structure in 3D scene to compute collision detection to swiftly reject disjoint objects and minimize the number of triangle primitives that need to be processed and then the Möller method is utilized to compute precise triangle primitives, further enhancing the efficiency and precision of the collision detection process. …”
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A Block-Based and Highly Parallel CNN Accelerator for Seed Sorting by Xiaoting Sang, Zhenghui Hu, Huanyu Li, Chunlei Li, Zhoufeng Liu

“…For embedded devices, the high-power consumption of graphics processing units (GPUs) is generally prohibitive, and the field programmable gate array (FPGA) becomes a solution to perform high-speed inference by providing a customized accelerator for a particular user. …”
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Accelerated pseudo-transient method for elastic, viscoelastic, and coupled hydromechanical problems with applications by Y. Alkhimenkov, Y. Y. Podladchikov

“…Recent advancements have demonstrated the APT method's computational efficiency, particularly when applied to quasi-static nonlinear problems using Graphical Processing Units (GPUs). This study presents a comprehensive analysis of the APT method, focusing on its application to quasi-static elastic, viscoelastic, and coupled hydromechanical problems, specifically those governed by quasi-static Biot poroelastic equations, across 1D, 2D, and 3D domains. …”
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ML-NIC: accelerating machine learning inference using smart network interface cards by Raghav Kapoor, David C. Anastasiu, Sean Choi

“…A widely studied and used technique to overcome this challenge is to offload some or all parts of the inference tasks onto specialized hardware such as graphic processing units. More recently, offloading machine learning inference onto programmable network devices, such as programmable network interface cards or a programmable switch, is gaining interest from both industry and academia, especially due to the latency reduction and computational benefits of performing inference directly on the data plane where the network packets are processed. …”
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Multitask Learning-Based Pipeline-Parallel Computation Offloading Architecture for Deep Face Analysis by Faris S. Alghareb, Balqees Talal Hasan

“…Nevertheless, the superior accuracy of a DNN is achieved at the expense of intensive computations and storage complexity, requiring custom expandable hardware, i.e., graphics processing units (GPUs). Interestingly, leveraging the synergy of parallelism and edge computing can significantly improve CPU-based hardware platforms. …”
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Cyberinfrastructure for machine learning applications in agriculture: experiences, analysis, and vision by Lucas Waltz, Sushma Katari, Chaeun Hong, Adit Anup, Julian Colbert, Anirudh Potlapally, Taylor Dill, Canaan Porter, John Engle, John Engle, Christopher Stewart, Hari Subramoni, Scott Shearer, Raghu Machiraju, Osler Ortez, Laura Lindsey, Arnab Nandi, Sami Khanal

“…IntroductionAdvancements in machine learning (ML) algorithms that make predictions from data without being explicitly programmed and the increased computational speeds of graphics processing units (GPUs) over the last decade have led to remarkable progress in the capabilities of ML. …”
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Conservation in action: Cost-effective UAVs and real-time detection of the globally threatened swamp deer (Rucervus duvaucelii) by Ravindra Nath Tripathi, Karan Agarwal, Vikas Tripathi, Ruchi Badola, Syed Ainul Hussain

“…This framework operates on a local server, synchronizing with consumer-grade UAVs at a rate of 32 frames per second (fps) with 320 pixel resolution using a frame sampling technique, notably without requiring a dedicated Graphical Processing Unit (GPU). This deliberate choice of not using GPU underscores the commitment to cost-effectiveness and aligns with the research's purpose, prioritizing accessibility and affordability for broader scientific exploration. …”
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