scholarly journals Compute-unified device architecture implementation of a block-matching algorithm for multiple graphical processing unit cards

2011 ◽  
Vol 20 (3) ◽  
pp. 033004 ◽  
Author(s):  
Francesc Massanes
Keyword(s):  
Block Matching ◽  
Graphical Processing Unit ◽  
Processing Unit ◽  
Compute Unified Device Architecture ◽  
Matching Algorithm ◽  
Block Matching Algorithm ◽  
Device Architecture ◽  
Graphical Processing

Computational Aspects of Lattice-Based Cryptography on Graphical Processing Unit

2016 ◽  
pp. 255-284 ◽  
Author(s):  
Sedat Akleylek ◽  
Zaliha Yuce Tok
Keyword(s):  
Quotient Ring ◽  
Graphical Processing Unit ◽  
Processing Unit ◽  
Compute Unified Device Architecture ◽  
Signature Scheme ◽  
Polynomial Multiplication ◽  
Device Architecture ◽  
Computational Aspects ◽  
Graphical Processing ◽  
Lattice Based Cryptography

In this chapter, the aim is to discuss computational aspects of lattice-based cryptographic schemes focused on NTRU in view of the time complexity on a graphical processing unit (GPU). Polynomial multiplication algorithms, having a very important role in lattice-based cryptographic schemes, are implemented on the GPU using the compute unified device architecture (CUDA) platform. They are implemented in both serial and parallel way. Compact and efficient implementation architectures of polynomial multiplication for lattice-based cryptographic schemes are presented for the quotient ring both Zp [x]/(xn-1) and Zp [x]/(xn+1), where p is a prime number. Then, by using these implementations the NTRUEncrypt and signature scheme working over Zp [x]/(xn+1) are implemented on the GPU using CUDA platform. Implementation details are also discussed.

Application of lattice Boltzmann methods for the multiphase fluid pipe flow on graphical processing unit

2017 ◽  
Vol 10 (3) ◽  
pp. 109-118 ◽  
Author(s):  
Pengxin Cheng ◽  
Nan Gui ◽  
Xingtuan Yang ◽  
JiyuanTu ◽  
Shengyao Jiang
Keyword(s):  
Lattice Boltzmann Method ◽  
Pipe Flow ◽  
Lattice Boltzmann ◽  
Graphical Processing Unit ◽  
Processing Unit ◽  
Compute Unified Device Architecture ◽  
Device Architecture ◽  
Graphical Processing ◽  
Multiphase Fluid ◽  
Boltzmann Method

In this paper, we employ the lattice Boltzmann method implemented on compute unified device architecture-enabled graphical processing unit to investigate the multiphase fluid pipe flow. The basics of lattice Boltzmann method as well as the Shan–Chen multiphase model and the fundamentals of graphical processing unit with compute unified device architecture are thoroughly introduced. The procedure of implementation of lattice Boltzmann method on graphical processing unit and the comparison of the computing performance between graphical processing unit and CPU are presented. It is demonstrated that the graphical processing unit-based lattice Boltzmann method has remarkable advantages over CPU especially with selected appropriate parameters. The results of validation cases agree well with previous numerical results or analytical solutions. The vertical and horizontal multiphase pipe flow are simulated and discussed.

scholarly journals GPU Algorithm for the Scaled Opposite-Spin (SOS) MP2 Energy Evaluation

2017 ◽  
Vol 61 (1) ◽  
Author(s):  
Luis Ángel Martínez-Martínez ◽  
Carlos Amador-Bedolla
Keyword(s):  
Correlation Energy ◽  
Energy Calculation ◽  
Processing Unit ◽  
Compute Unified Device Architecture ◽  
Energy Evaluation ◽  
Device Architecture ◽  
Linear Alkanes ◽  
Computationally Intensive ◽  
Graphical Processing ◽  
And Performance

<p>The most computationally intensive part of the SOS-MP2 algorithm for the calculation of the correlation energy [1], as executed in Q-Chem, is implemented for use in a graphical processing unit (GPU). Our approach adds new routines to the library initially developed by Aspuru-Guzik and co-workers [2], aiming at maximization of bandwidth and performance, by taking advantage of the asynchronous CPU-GPU communication capability of modern GPUs. These changes permit an almost six-fold acceleration in the correlation energy calculation of linear alkanes. This was achieved employing a NVIDIA Tesla K40C (Kepler) GPU and the Compute Unified Device Architecture (CUDA).</p>

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.

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