Parallel Identifying (l,d)-Motifs in Biosequences Using CPU and GPU Computing

2016 ◽  
pp. 257-268
Author(s):  
Cheng Zhong ◽  
Jing Zhang ◽  
Bei Hua ◽  
Feng Yang ◽  
Zhengping Liu
Keyword(s):  
Gpu Computing

Mixed-mode database miner classifier: Parallel computation of graphical processing unit mining

2021 ◽  
pp. 002072092098849
Author(s):  
Soumya Ranjan Nayak ◽  
S Sivakumar ◽  
Akash Kumar Bhoi ◽  
Gyoo-Soo Chae ◽  
Pradeep Kumar Mallick
Keyword(s):  
Credit Card ◽  
Mixed Mode ◽  
Processing Time ◽  
Gpu Computing ◽  
Graphical Processing Unit ◽  
Computational Time ◽  
Processing Unit ◽  
Large Set ◽  
Minimal Processing ◽  
Graphical Processing

Graphical processing unit (GPU) has gained more popularity among researchers in the field of decision making and knowledge discovery systems. However, most of the earlier studies have GPU memory utilization, computational time, and accuracy limitations. The main contribution of this paper is to present a novel algorithm called the Mixed Mode Database Miner (MMDBM) classifier by implementing multithreading concepts on a large number of attributes. The proposed method use the quick sort algorithm in GPU parallel computing to overcome the state of the art limitations. This method applies the dynamic rule generation approach for constructing the decision tree based on the predicted rules. Moreover, the implementation results are compared with both SLIQ and MMDBM using Java and GPU with the computed acceleration ratio time using the BP dataset. The primary objective of this work is to improve the performance with less processing time. The results are also analyzed using various threads in GPU mining using eight different datasets of UCI Machine learning repository. The proposed MMDBM algorithm have been validated on these chosen eight different dataset with accuracy of 91.3% in diabetes, 89.1% in breast cancer, 96.6% in iris, 89.9% in labor, 95.4% in vote, 89.5% in credit card, 78.7% in supermarket and 78.7% in BP, and simultaneously, it also takes less computational time for given datasets. The outcome of this work will be beneficial for the research community to develop more effective multi thread based GPU solution in GPU mining to handle large set of data in minimal processing time. Therefore, this can be considered a more reliable and precise method for GPU computing.

scholarly journals High-Speed Lightweight Ship Detection Algorithm Based on YOLO-V4 for Three-Channels RGB SAR Image

2021 ◽  
Vol 13 (10) ◽  
pp. 1909
Author(s):  
Jiahuan Jiang ◽  
Xiongjun Fu ◽  
Rui Qin ◽  
Xiaoyan Wang ◽  
Zhifeng Ma
Keyword(s):  
Deep Learning ◽  
Gpu Computing ◽  
Hot Spot ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Processing Unit ◽  
Sar Image ◽  
Marine Monitoring ◽  
Ship Detection

Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.

scholarly journals Efficient ResNet Model to Predict Protein-Protein Interactions With GPU Computing

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 127834-127844
Author(s):  
Shengyu Lu ◽  
Qingqi Hong ◽  
Beizhan Wang ◽  
Hongji Wang
Keyword(s):  
Protein Interactions ◽  
Gpu Computing ◽  
Protein Protein Interactions

scholarly journals Probing the Statistical Validity of the Ductile-to-Brittle Transition in Metallic Nanowires Using GPU Computing

2013 ◽  
Vol 9 (12) ◽  
pp. 5558-5566 ◽  
Author(s):  
William R. French ◽  
Amulya K. Pervaje ◽  
Andrew P. Santos ◽  
Christopher R. Iacovella ◽  
Peter T. Cummings
Keyword(s):  
Gpu Computing ◽  
Metallic Nanowires ◽  
Brittle Transition ◽  
Statistical Validity ◽  
Ductile To Brittle Transition

scholarly journals Quantitative study of computing time of direct/iterative solver for MoM by GPU computing

2013 ◽  
Vol 2 (8) ◽  
pp. 359-364 ◽  
Author(s):  
Keisuke Konno ◽  
Hajime Katsuda ◽  
Kei Yokokawa ◽  
Qiang Chen ◽  
Kunio Sawaya ◽  
...  
Keyword(s):  
Quantitative Study ◽  
Gpu Computing ◽  
Computing Time ◽  
Iterative Solver

Rogue waves: New forms enabled by GPU computing

2014 ◽  
Vol 378 (32-33) ◽  
pp. 2377-2381 ◽  
Author(s):  
Christopher Chabalko ◽  
Ayan Moitra ◽  
Balakumar Balachandran
Keyword(s):  
Gpu Computing ◽  
Rogue Waves

Clustered Cell Parallelization for GPU Computing of Silicon Anisotropic Etching Simulation

2013 ◽  
Author(s):  
Jianhua Li ◽  
Jingyuan Chen ◽  
Yan Wang ◽  
Jianhua Huang
Keyword(s):  
Graphics Processing Units ◽  
Gpu Computing ◽  
Cell Model ◽  
Application Programming Interface ◽  
Simulation Method ◽  
Anisotropic Etching ◽  
Time Step ◽  
Ca Model ◽  
Application Programming ◽  
Graphics Processing

The parallelization of silicon anisotropic etching simulation with the cellular automata (CA) model on graphics processing units (GPUs) is challenging, because the numbers of computational tasks in etching simulation dynamically change and the existing parallel CA mechanisms do not fit in GPU computation well. In this paper, an improved CA model, called clustered cell model, is proposed for GPU-based etching simulation. The model consists of clustered cells, each of which manages a scalable number of atoms. In this model, only the etching and update of states for the atoms on the etching surface and their unexposed neighbors are performed at each CA time step, whereas the clustered cells are reclassified in a longer time step. With this model, a crystal cell parallelization method is given, where clustered cells are allocated to threads on GPUs in the simulation. With the optimizations from the spatial and temporal aspects as well as a proper granularity, this method provides a faster process simulation. The proposed simulation method is implemented with the Compute Unified Device Architecture (CUDA) application programming interface. Several computational experiments are taken to analyze the efficiency of the method.

Application of GPU computing to the Characteristic Basis Function Method

2012 ◽  
Author(s):  
Juan Ignacio Perez ◽  
Eliseo Garcia ◽  
Jose A. de Frutos ◽  
J. Ramon Almagro ◽  
M. Felipe Catedra
Keyword(s):  
Basis Function ◽  
Function Method ◽  
Gpu Computing
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