Design and Implementation of a Computer Vision Based Inspection System Using CUDA

2011 ◽  
Vol 383-390 ◽  
pp. 18-24
Author(s):  
Yan Shi ◽  
Ai Guo Li ◽  
Lin Wang
Keyword(s):  
Computer Vision ◽  
Graphic Processing Unit ◽  
Work Conditions ◽  
Inspection System ◽  
Processing Unit ◽  
Device Architecture ◽  
Vision Processing ◽  
And Performance ◽  
Hardware In Loop ◽  
Real Vehicle

In the production process of electronic Instrument Clusters (IC) used in automobiles, a need for automated inspection of dynamic characteristics is identified. An inspection system based on hardware-in-loop emulation and computer vision using Computer Unified Device Architecture (CUDA) is proposed. The system generates network signals that emulate a real vehicle, sends the signals to an IC to turn it into various work conditions, captures the IC’s response into a graphic processing unit for real-time computer-vision processing and records inspection results into databases. An implementation of the design and performance analysis is provided.

SeisNoise.jl: Ambient Seismic Noise Cross Correlation on the CPU and GPU in Julia

2020 ◽  
Vol 92 (1) ◽  
pp. 517-527
Author(s):  
Timothy Clements ◽  
Marine A. Denolle
Keyword(s):  
Seismic Noise ◽  
High Performance ◽  
Cross Correlation ◽  
Graphic Processing Unit ◽  
Ambient Seismic Noise ◽  
Processing Unit ◽  
Central Processing ◽  
And Performance ◽  
Noise Cross Correlation ◽  
Performance Computing

Abstract We introduce SeisNoise.jl, a library for high-performance ambient seismic noise cross correlation, written entirely in the computing language Julia. Julia is a new language, with syntax and a learning curve similar to MATLAB (see Data and Resources), R, or Python and performance close to Fortran or C. SeisNoise.jl is compatible with high-performance computing resources, using both the central processing unit and the graphic processing unit. SeisNoise.jl is a modular toolbox, giving researchers common tools and data structures to design custom ambient seismic cross-correlation workflows in Julia.

Potential of General Purpose Graphic Processing Unit for Energy Management System

2012 ◽  
Vol 3 (2) ◽  
pp. 72-82
Author(s):  
Jean-Charles Tournier ◽  
Vaibhav Donde ◽  
Zhao Li ◽  
Martin Naef
Keyword(s):  
Energy Management ◽  
Management System ◽  
Graphic Processing Unit ◽  
General Purpose ◽  
Energy Management System ◽  
Processing Unit ◽  
Server Side ◽  
And Performance ◽  
Speedup Factor ◽  
Graphic Processing

This paper investigates the potential of General Purpose Graphic Processing Unit (GPGPU) for the server and HMI parts of Energy Management System (EMS). The HMI investigation focuses on the applicability and performance improvement of GPGPU for scattered data interpolation algorithms typically used to visually represent the overall state of a power network. The server side investigation focuses on fine grain parallelization of EMS applications by targeting the sparse linear solver. The different performance evaluations show the high potential of GPGPU for the HMI part with a speedup factor up to 100 at the cost of acceptable approximations, while the benefit on the server side varies from a speedup factor of up to 300 to 0 depending on the application.

GPU-Based Real-Time Occlusion in an Immersive Augmented Reality Environment

2009 ◽  
Vol 9 (2) ◽  
Author(s):  
Yuzhu Lu ◽  
Shana Smith
Keyword(s):  
Virtual Reality ◽  
Augmented Reality ◽  
Real Time ◽  
Graphic Processing Unit ◽  
Prototype System ◽  
Processing Unit ◽  
Virtual Objects ◽  
Remote Cameras ◽  
Real Scene ◽  
And Performance

In this paper, we present a prototype system, which uses CAVE-based virtual reality to enhance immersion in an augmented reality environment. The system integrates virtual objects into a real scene captured by a set of stereo remote cameras. We also present a graphic processing unit (GPU)-based method for computing occlusion between real and virtual objects in real time. The method uses information from the captured stereo images to determine depth of objects in the real scene. Results and performance comparisons show that the GPU-based method is much faster than prior CPU-based methods.

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>

CPU AND GPU PERFORMANCE ANALYSIS ON 2D MATRIX OPERATION

2021 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Kwek Benny Kurniawan ◽  
YB Dwi Setianto
Keyword(s):  
Graphic Processing Unit ◽  
Computing Time ◽  
General Purpose ◽  
Parallel Processors ◽  
Processing Unit ◽  
Matrix Operation ◽  
Industry Standard ◽  
Programming Interfaces ◽  
Complete Matrix ◽  
Matrix Calculation

GPU or Graphic Processing Unit can be used on many platforms in general GPUs are used for rendering graphics but now GPUs are general purpose parallel processors with support for easily accessible programming interfaces and industry standard languages such as C, Python and Fortran. In this study, the authors will compare CPU and GPU for completing some matrix calculation. To compare between CPU and GPU, the authors have done some testing to observe the use of Processing Unit, memory and computing time to complete matrix calculations by changing matrix sizes and dimensions. The results of tests that have been done shows asynchronous GPU is faster than sequential. Furthermore, thread for GPU needs to be adjusted to achieve efficiency in GPU load.

Sign in / Sign up

Export Citation Format

Share Document