Power analysis for decoding of the digital audio encoding format MP3: Decoding the central processing unit and the graphics processing unit.

2010 ◽  
Vol 127 (3) ◽  
pp. 2037-2037
Author(s):  
Seung Gu Kang ◽  
Pil Joung Sun ◽  
Cheol Hong Kim ◽  
Jong‐Myon Kim
Keyword(s):  
Power Analysis ◽  
Graphics Processing Unit ◽  
Central Processing Unit ◽  
Processing Unit ◽  
Digital Audio ◽  
Central Processing ◽  
Graphics Processing

scholarly journals Paralelização do Algoritmo Floyd-Warshall usando GPU

2013 ◽  
Author(s):  
Roussian R. A. Gaioso ◽  
Walid A. R. Jradi ◽  
Lauro C. M. de Paula ◽  
Wanderley De S. Alencar ◽  
Wellington S. Martins ◽  
...  
Keyword(s):  
Graphics Processing Unit ◽  
Central Processing Unit ◽  
Processing Unit ◽  
Central Processing ◽  
Graphics Processing

Este artigo apresenta uma implementação paralela baseada em Graphics Processing Unit (GPU) para o problema da identificação dos caminhos mínimos entre todos os pares de vértices em um grafo. A implementação é baseada no algoritmo Floyd-Warshall e tira o máximo proveito da arquitetura multithreaded das GPUs atuais. Nossa solução reduz a comunicação entre a Central Processing Unit (CPU) e a GPU, melhora a utilização dos Streaming Multiprocessors (SMs) e faz um uso intensivo de acesso aglutinado em memória para otimizar o acesso de dados do grafo. A vantagem da implementação proposta é demonstrada por vários grafos gerados aleatoriamente utilizando a ferramenta GTgraph. Grafos contendo milhares de vértices foram gerados e utilizados nos experimentos. Os resultados mostraram um excelente desempenho em diversos grafos, alcançando ganhos de até 149x, quando comparado com uma implementação sequencial, e superando implementações tradicionais por um fator de quase quatro vezes. Nossos resultados confirmam que implementações baseadas em GPU podem ser viáveis mesmo para algoritmos de grafos cujo acessos à memória e distribuição de trabalho são irregulares e causam dependência de dados.

History and Evolution of GPU Architecture

2016 ◽  
pp. 109-135
Author(s):  
Prashanta Kumar Das ◽  
Ganesh Chandra Deka
Keyword(s):  
Image Processing ◽  
Graphics Processing Unit ◽  
Hardware Architecture ◽  
Central Processing Unit ◽  
Processing Unit ◽  
3D Image ◽  
Central Processing ◽  
Graphics Processing ◽  
Graphics Engine ◽  
Gpu Architecture

The Graphics Processing Unit (GPU) is a specialized and highly parallel microprocessor designed to offload 2D/3D image from the Central Processing Unit (CPU) to expedite image processing. The modern GPU is not only a powerful graphics engine, but also a parallel programmable processor with high precision and powerful features. It is forcasted that by 2020, 48 Core GPU will be available while by 2030 GPU with 3000 core is likely to be available.This chapter describes the chronology of evolution of GPU hardware architecture and the future ahead.

scholarly journals CENTRAL PROCESSING UNIT-GRAPHICS PROCESSING UNIT COMPUTING SCHEME FOR MULTI-OBJECT TRACKING IN SURVEILLANCE

2017 ◽  
Vol 10 (13) ◽  
pp. 251
Author(s):  
Ankush Rai ◽  
Jagadeesh Kannan R
Keyword(s):  
Gpu Computing ◽  
Graphics Processing Unit ◽  
Research Work ◽  
Central Processing Unit ◽  
Processing Unit ◽  
Minimal Processing ◽  
Central Processing ◽  
Gpu Processing ◽  
Graphics Processing ◽  
Computing Scheme

This research work presents a novel central processing unit-graphics processing unit (CPU-GPU) computing scheme for multiple object trackingduring a surveillance operation. This facilitates nonlinear computational jobs to avail completion of computation in minimal processing time for tracking function. The work is divided into two essential objectives. First is to dynamically divide the processing operations into parallel units, and second is to reduce the communication between CPU-GPU processing units.

scholarly journals Numerical simulation of flattened heat pipe with double heat sources for CPU and GPU cooling application in laptop computers

2020 ◽  
Author(s):  
Wisoot Sanhan ◽  
Kambiz Vafai ◽  
Niti Kammuang-Lue ◽  
Pradit Terdtoon ◽  
Phrut Sakulchangsatjatai
Keyword(s):  
Experimental Data ◽  
Heat Pipe ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Heat Sources ◽  
Final Thickness ◽  
Laptop Computers ◽  
Central Processing ◽  
Graphics Processing ◽  
Good Agreement

Abstract An investigation of the effect of the thermal performance of the flattened heat pipe on its double heat sources acting as central processing unit and graphics processing unit in laptop computers is presented in this work. A finite element method is used for predicting the flattening effect of the heat pipe. The cylindrical heat pipe with a diameter of 6 mm and the total length of 200 mm is flattened into three final thicknesses of 2, 3, and 4 mm. The heat pipe is placed under a horizontal configuration and heated with heater 1 and heater 2, 40 W in combination. The numerical model shows good agreement compared with the experimental data with the standard deviation of 1.85%. The results also show that flattening the cylindrical heat pipe to 66.7 and 41.7% of its original diameter could reduce its normalized thermal resistance by 5.2%. The optimized final thickness or the best design final thickness for the heat pipe is found to be 2.5 mm.

ALGORITHM OF SKELETON-BASED STATIC HAND GESTURE RECOGNITION

2020 ◽  
pp. 13-22
Author(s):  
D. A. Kalina ◽  
R. V. Golovanov ◽  
D. V. Vorotnev
Keyword(s):  
Gesture Recognition ◽  
Graphics Processing Unit ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Processing Unit ◽  
The Novel ◽  
Central Processing ◽  
Graphics Processing ◽  
Artificial Network

We present the monocamera approach of static hand gestures recognition based on skeletonization. The problem of creating skeleton of the human’s hand, as well as body, became solvable a few years ago after inventing so called convolutional pose machines – the novel architecture of artificial neural network. Our solution uses such kind of pretrained convolutional artificial network for extracting hand joints keypoints with further skeleton reconstruction. In this work we also propose special skeleton descriptor with proving its stability and distinguishability in terms of classification. We considered a few widespread machine learning algorithms to build and verify different classifiers. The quality of the classifier’s recognition is estimated using the wellknown Accuracy metric, which identified that classical SVM (Support Vector Machines) with radial basis kernel gives the best results. The testing of the whole system was conducted using public databases containing about 3000 of test images for more than 10 types of gestures. The results of a comparative analysis of the proposed system with existing approaches are demonstrated. It is shown that our gesture recognition system provides better quality in comparison with existing solutions. The performance of the proposed system was estimated for two configurations of standard personal computer: with CPU (Central Processing Unit) only and with GPU (Graphics Processing Unit) in addition where the latest one provides realtime processing with up to 60 frames per second. Thus we demonstrate that the proposed approach can find an application in the practice.

scholarly journals Graphics processing unit implementation of the F-statistic for continuous gravitational wave searches

2021 ◽  
Author(s):  
Liam Dunn ◽  
Patrick Clearwater ◽  
Andrew Melatos ◽  
Karl Wette
Keyword(s):  
Gravitational Wave ◽  
Graphics Processing Units ◽  
Graphics Processing Unit ◽  
Computational Cost ◽  
Processing Unit ◽  
Central Processing ◽  
Long Baseline ◽  
Using Data ◽  
Graphics Processing ◽  
Gpu Implementation

Abstract The F-statistic is a detection statistic used widely in searches for continuous gravitational waves with terrestrial, long-baseline interferometers. A new implementation of the F-statistic is presented which accelerates the existing "resampling" algorithm using graphics processing units (GPUs). The new implementation runs between 10 and 100 times faster than the existing implementation on central processing units without sacrificing numerical accuracy. The utility of the GPU implementation is demonstrated on a pilot narrowband search for four newly discovered millisecond pulsars in the globular cluster Omega Centauri using data from the second Laser Interferometer Gravitational-Wave Observatory observing run. The computational cost is 17:2 GPU-hours using the new implementation, compared to 1092 core-hours with the existing implementation.

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