Initial results on computational performance of Intel many integrated core, sandy bridge, and graphical processing unit architectures: implementation of a 1D c++/OpenMP electrostatic particle-in-cell code

2014 ◽  
Vol 27 (3) ◽  
pp. 581-593 ◽  
Author(s):  
A. Vapirev ◽  
J. Deca ◽  
G. Lapenta ◽  
S. Markidis ◽  
I. Hur ◽  
...  
Keyword(s):  
Graphical Processing Unit ◽  
Processing Unit ◽  
Particle In Cell ◽  
Computational Performance ◽  
Graphical Processing ◽  
Many Integrated Core ◽  
Initial Results

Numerical Continuation on a Graphical Processing Unit for Kinematic Synthesis

2020 ◽  
Vol 20 (6) ◽  
Author(s):  
Jeffrey Glabe ◽  
J. Michael McCarthy
Keyword(s):  
Polynomial System ◽  
Fixed Number ◽  
Graphical Processing Unit ◽  
Numerical Continuation ◽  
Processing Unit ◽  
Step Size ◽  
Kinematic Synthesis ◽  
Computational Performance ◽  
Continuation Algorithm ◽  
Graphical Processing

Abstract This paper presents an implementation of a homotopy path tracking algorithm for polynomial numerical continuation on a graphical processing unit (GPU). The goal of this algorithm is to track homotopy curves from known roots to the unknown roots of a target polynomial system. The path tracker solves a set of ordinary differential equations to predict the next step and uses a Newton root finder to correct the prediction so the path stays on the homotopy solution curves. In order to benefit from the computational performance of a GPU, we organize the procedure so it is executed as a single instruction set, which means the path tracker has a fixed step size and the corrector has a fixed number iterations. This trade-off between accuracy and GPU computation speed is useful in numerical kinematic synthesis where a large number of solutions must be generated to find a few effective designs. In this paper, we show that our implementation of GPU-based numerical continuation yields 85 effective designs in 63 s, while an existing numerical continuation algorithm yields 455 effective designs in 2 h running on eight threads of a workstation.

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.

Interactive, graphical processing unit-based evaluation of evacuation scenarios at the state scale

SIMULATION ◽  
2011 ◽  
Vol 88 (6) ◽  
pp. 746-761 ◽  
Author(s):  
Kalyan S Perumalla ◽  
Brandon G Aaby ◽  
Srikanth B Yoginath ◽  
Sudip K Seal
Keyword(s):  
The State ◽  
Graphical Processing Unit ◽  
Processing Unit ◽  
Graphical Processing ◽  
State Scale
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