Automatic detection and counting of stacked eucalypt timber using the YOLOv8 model by Casas, Gianmarco Goycochea, Ismail, Zool Hilmi, Limeira, Mathaus Messias Coimbra, Lopes da Silva, Antonilmar Araújo, Leite, Helio Garcia

“…The model was trained using an AdamW optimizer and implemented using Ultralytics YOLOv8.0.137, Python-3.10.12, and torch-2.0.1 + cu118 with CUDA support on NVIDIA T1000 (4096MiB). For model evaluation, the precision, recall, and mean Average Precision at a 50% confidence threshold (mAP50) were calculated. …”
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Study of the weakly-compressible SPH method for improving pressure distribution of violent fluid-structure impact flows by Junling He, Qingzhi Hou, Yuejin Cai, Chen Shaokang, Gao Ruixue

“…In this paper, to enhance computational efficiency, the SPH method is implemented on a General Processing Unit (GPU) platform using Compute Unified Device Architecture (CUDA). Parallelized SPH programs including the standard SPH method, Riemann-based SPH and Delta-SPH are verified by a dam break model with large Reynolds number and violent deformation of free surfaces. …”
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Fast dose calculation of convolution/superposition in radiotherapy based on multi GPU heterogeneous model by LAI Jialu, SONG Ying, ZHOU Li, BAI Xue, HOU Qing

“…High-end GPU, i.e., Tesla C2015, was used for experimental test of CS algorithm executing under the compute unified device architecture (CUDA) platform. Speeds of different number GPUs combined with CPU were compared to find the suitable solution.ResultsThe experimental results show that the speedup of CS algorithm is not completely linear with the number of GPUs. …”
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Virtual Clinical Trials in 2D and 3D X-ray Breast Imaging and Dosimetry: Comparison of CPU-Based and GPU-Based Monte Carlo Codes by Giovanni Mettivier, Antonio Sarno, Youfang Lai, Bruno Golosio, Viviana Fanti, Maria Elena Italiano, Xun Jia, Paolo Russo

“…We compared three different platforms for in-silico X-ray 3D breast imaging: the Agata (University & INFN Napoli) that was based on the Geant4 toolkit and running on a CPU-based server architecture; the XRMC Monte Carlo (University of Cagliari) that was based on the use of variance reduction techniques, running on a CPU hardware; and the Monte Carlo code gCTD (University of Texas Southwestern Medical Center) running on a single GPU platform with CUDA environment. The tests simulated the irradiation of cylindrical objects as well as anthropomorphic breast phantoms and produced 2D and 3D images and 3D maps of absorbed dose. …”
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WBIN-Tree: A Single Scan Based Complete, Compact and Abstract Tree for Discovering Rare and Frequent Itemset Using Parallel Technique by Shwetha Rai, Preetham Kumar, K. Nakul Shetty, M. Geetha, B. Giridhar

“…In this paper, a novel Weighted Binary Count Tree (WBIN-Tree) is proposed and implemented in CUDA to exploit the power of GPU and discover rules with rare antecedent and frequent consequent using parallel approach. …”
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A Flexible and General-Purpose Platform for Heterogeneous Computing by Jose Juan Garcia-Hernandez, Miguel Morales-Sandoval, Erick Elizondo-Rodríguez

“…Accelerating an algorithm for a specific device under a specific framework, i.e., CUDA/GPU, provides a solution with the highest possible performance at the cost of a loss in generality and requires an experienced programmer. …”
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FOCUS: fast Monte Carlo approach to coherence of undulator sources by M. Siano, G. Geloni, B. Paroli, D. Butti, T. Lefèvre, S. Mazzoni, G. Trad, U. Iriso, A. A. Nosych, L. Torino, M. A. C. Potenza

“…The core structure of the code, which is written in the language C++ accelerated with CUDA, combines an analytical description of the emitted electric fields and massively parallel computations on GPUs. …”
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Deep neural network-based physical distancing monitoring system with tensorRT optimization by Edi Kurniawan, Hendra Adinanta, Suryadi Suryadi, Bernadus Herdi Sirenden, Rini Khamimatul Ula, Hari Pratomo, Purwowibowo Purwowibowo, Jalu Ahmad Prakosa

“…The optimization process is based on TensorRT executed on Graphical Processing Unit (GPU) and Computer Unified Device Architecture (CUDA) platform. This research evaluates the inferencing speed of the well-known object detection model You-Only-Look-Once (YOLO) run on two different Artificial Intelligence (AI) machines. …”
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Automatic Detection and Counting of Stacked Eucalypt Timber Using the YOLOv8 Model by Gianmarco Goycochea Casas, Zool Hilmi Ismail, Mathaus Messias Coimbra Limeira, Antonilmar Araújo Lopes da Silva, Helio Garcia Leite

“…The model was trained using an AdamW optimizer and implemented using Ultralytics YOLOv8.0.137, Python-3.10.12, and torch-2.0.1 + cu118 with CUDA support on NVIDIA T1000 (4096MiB). For model evaluation, the precision, recall, and mean Average Precision at a 50% confidence threshold (mAP50) were calculated. …”
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High Performance Graph Data Imputation on Multiple GPUs by Chao Zhou, Tao Zhang

“…In this paper, we propose a scheme to perform the convolutional imputation algorithm with higher time performance on GPUs (Graphics Processing Units) by exploiting multi-core GPUs of CUDA architecture. We propose optimization strategies to achieve coalesced memory access for graph Fourier transform (GFT) computation and improve the utilization of GPU SM resources for singular value decomposition (SVD) computation. …”
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How the Processing Mode Influences Azure Kinect Body Tracking Results by Linda Büker, Vincent Quinten, Michel Hackbarth, Sandra Hellmers, Rebecca Diekmann, Andreas Hein

“…Euclidean distances of joint positions varied clinically relevantly with up to 87 mm between runs for CUDA and TensorRT; CPU and DirectML had no differences on the same computer. …”
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Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines by Barnes, Connelly, Adams, Andrew, Paris, Sylvain, Ragan-Kelley, Jonathan Millar, Durand, Fredo, Amarasinghe, Saman P.

“…From simple Halide programs written in a few hours, we demonstrate performance up to 5x faster than hand-tuned C, intrinsics, and CUDA implementations optimized by experts over weeks or months, for image processing applications beyond the reach of past automatic compilers.…”
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Multicore processors and graphics processing unit accelerators for parallel retrieval of aerosol optical depth from satellite data: implementation, performance, and energy efficien... by Liu, Jia, Feld, Dustin, Xue, Yong, Garcke, Jochen, Soddemann, Thomas

“…The compute unified device architecture C (CUDA-C) has been used for the GPU implementation for NVIDIA’s graphic cards and open multiprocessing (OpenMP) for thread- parallelism in the multicore implementation. …”
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High-performance computing and communication models for solving the complex interdisciplinary problems on DPCS by Alias, N., Sahnoun, R., Malyshkin, V.

“…The specific methodologies of PC software under consideration include PVM, MPI, LUNA, MDC, OpenMP, CUDA and LINDA integrated with COMSOL and C++/C. …”
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Deep Learning and Parallel Computing Environment for Bioengineering Systems / by Sangaiah, Arun Kumar 649738, ScienceDirect (Online service) 7722

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BrainPy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming by Chaoming Wang, Tianqiu Zhang, Xiaoyu Chen, Sichao He, Shangyang Li, Si Wu

“…Models defined in BrainPy can be JIT compiled into binary instructions for various devices, including Central Processing Unit, Graphics Processing Unit, and Tensor Processing Unit, which ensures high-running performance comparable to native C or CUDA. Additionally, BrainPy features an extensible architecture that allows for easy expansion of new infrastructure, utilities, and machine-learning approaches. …”
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Methanol extract of Black soldier fly (Hermetia illucens) prepupae against Aeromonas and Staphylococcus aureus bacteria in vitro and in silico by Dahliatul Qosimah, Sanarto Santosa, Maftuch Maftuch, Husnul Khotimah, Loeki Enggar Fitri, Aulanni'am Aulanni'am, Lucia Tri Suwanti

“…Molecular docking of the active ingredients (defensin, chitin, and chitosan as well as fatty acids) in BSF was downloaded from the NCBI database and docked with the Hex Cuda 8.0 program, Correlation type parameters Shape + Electro and Grid Dimension 0.6. …”
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Password Cracking with Brute Force Algorithm and Dictionary Attack Using Parallel Programming by Ibrahim Alkhwaja, Mohammed Albugami, Ali Alkhwaja, Mohammed Alghamdi, Hussam Abahussain, Faisal Alfawaz, Abdullah Almurayh, Nasro Min-Allah

“…The results show that the NVIDIA GeForce GTX 1050 Ti with CUDA is significantly faster than the Intel(R) HD Graphics 630 GPU for cracking passwords, with a speedup of 11.5× and 10.4× for passwords with and without special characters, respectively. …”
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PADME - a new code for modeling planet georesources formation on heterogeneous computing systems by Viktor Protasov, Igor Kulikov

“…The aim of the study is to develop a new method for modeling planet formation in 3D2V formulation based on two-phase approach, adapted for using in heterogeneous computing systems equipped with graphics accelerators supporting NVIDIA CUDA technology. The methods of the study. Fluids-in-cells method of Belotserkovskii-Davydov, modified with using the Godunov method, is used to model the gas component. …”
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MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application by Qingsong Yan, Junhua Kang, Teng Xiao, Haibing Liu, Fei Deng

“…Second, MVP-Stereo leverages multi-scale parallel patchmatch to reconstruct the depth map for each image in a highly efficient manner, which is implemented by CUDA with random initialization, multi-scale parallel spatial propagation, random refinement, and the coarse-to-fine strategy. …”
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