scholarly journals Computing of 3D Bifurcation Diagrams With Nvidia CUDA Technology

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 157773-157780
Author(s):  
Artur Pala ◽  
Marek Machaczek
Keyword(s):  
Bifurcation Diagrams ◽  
Nvidia Cuda ◽  
Cuda Technology

A SERVICE FOR SOLVING BOOLEAN SATISFIABILITY PROBLEM USING NVIDIA CUDA TECHNOLOGY

2017 ◽  
Vol 4 (56) ◽  
pp. 107-114
Author(s):  
A. D. Kolosov ◽  
◽  
V. O. Gorovoy ◽  
V. V. Kondratiev ◽  
◽  
...  
Keyword(s):  
Boolean Satisfiability ◽  
Satisfiability Problem ◽  
Boolean Satisfiability Problem ◽  
Nvidia Cuda ◽  
Cuda Technology

scholarly journals Acceleration of boundary element calculations for closed domain using nonlinear form functions and CUDA technology

Doklady BGUIR ◽  
2021 ◽  
Vol 19 (3) ◽  
pp. 14-21
Author(s):  
S. S. Sherbakov ◽  
M. M. Polestchuk
Keyword(s):  
Boundary Element ◽  
Mutual Influence ◽  
Multicore Processors ◽  
Boundary Elements ◽  
Mechanics Of Solids ◽  
Closed Domain ◽  
Is Implementation ◽  
Nvidia Cuda ◽  
Cuda Technology ◽  
Significant Acceleration

The evolution of computer technologies, as a hardware and a software parts, allows to attain fast and accurate  solutions  to  many  applied  problems  in  scientific  areas.  Acceleration  of  calculations  is  broadly  used technic that is basically implemented by multithreading and multicore processors. NVidia CUDA technology or simply CUDA opens a way to efficient acceleration of boundary elements method (BEM), that includes many independent stages. The main goal of the paper is implementation and acceleration of indirect boundary element method using three form functions. Calculation of the potentialdistribution inside a closed boundary under the action of the defined boundary condition is considered. In order to accelerate corresponding calculations, they were parallelized at the graphic accelerator using NVidia CUDA technology. The dependences of acceleration of parallel  computations  as  compared  with  sequential  ones  were explored  for  different  numbers  of  boundary elements  and  computational  nodes.  A  significant  acceleration  (up  to  52  times)  calculation  of  the  potential distribution  without  loss  in  accuracy  is  shown.  Acceleration  of up  to  22  times  was  achieved  in  calculation of mutual  influence  matrix  for  boundary  elements.  Using  CUDA  technology  allows  to  attain  significant acceleration without loss in accuracy and convergence. So application of CUDA is a good way to parallelizing BEM.  Application  of  developed  approach  allows  to  solve  problems in  different  areas  of  physics  such as acoustics, hydromechanics, electrodynamics, mechanics of solids and many other areas, efficiently.

scholarly journals The implementation of ”Kuznyechik” encryption algorithm using NVIDIA CUDA technology

2019 ◽  
pp. 308-313
Author(s):  
A N Borisov ◽  
E V Myasnikov
Keyword(s):  
Encryption Algorithm ◽  
Global Memory ◽  
Graphics Processor ◽  
Nvidia Cuda ◽  
Cuda Technology ◽  
Lookup Tables ◽  
Block Encryption

In this paper, we discuss various options for implementing the ”Kuznyechik” block encryption algorithm using the NVIDIA CUDA technology. We use lookup tables as a basis for the implementation. In experiments, we study the influence of the size of the block of threads and the location of lookup tables on the encryption speed. We show that the best results are obtained when the lookup tables are stored in the global memory. The peak encryption speed reaches 30.83 Gbps on the NVIDIA GeForce GTX 1070 graphics processor.

scholarly journals COMPARISON OF GPU AND CPU EFFICIENCY WHILE SOLVING HEAT CONDUCTION PROBLEMS

2020 ◽  
Vol 12 (0) ◽  
pp. 1-5
Author(s):  
Julija Semenenko ◽  
Aliaksei Kolesau ◽  
Vadimas Starikovičius ◽  
Artūras Mackūnas ◽  
Dmitrij Šešok
Keyword(s):  
Heat Conduction ◽  
Numerical Experiments ◽  
Heat Conduction Problem ◽  
Floating Point ◽  
Relative Speed ◽  
Nvidia Cuda ◽  
Cuda Technology ◽  
Engineering Problems ◽  
The Matrix ◽  
Speed Up

Overview of GPU usage while solving different engineering problems, comparison between CPU and GPU computations and overview of the heat conduction problem are provided in this paper. The Jacobi iterative algorithm was implemented by using Python, TensorFlow GPU library and NVIDIA CUDA technology. Numerical experiments were conducted with 6 CPUs and 4 GPUs. The fastest used GPU completed the calculations 19 times faster than the slowest CPU. On average, GPU was from 9 to 11 times faster than CPU. Significant relative speed-up in GPU calculations starts when the matrix contains at least 4002 floating-point numbers.

Abundance estimation algorithms using NVIDIA CUDA technology

2008 ◽  
Author(s):  
David González ◽  
Christian Sánchez ◽  
Ricardo Veguilla ◽  
Nayda G. Santiago ◽  
Samuel Rosario-Torres ◽  
...  
Keyword(s):  
Abundance Estimation ◽  
Estimation Algorithms ◽  
Nvidia Cuda ◽  
Cuda Technology
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