Abstract

Compute Unified Device Architecture (CUDA) is a parallel computing platform developed by Nvidia for increase speed of graphics by usage of parallel mode for processes calculation. The success of this solution has opened technology General-Purpose Graphic Processor Units (GPGPUs) for applications not coupled with graphics. The GPGPUs system can be applying as effective tool for reducing huge number of data for pulse shape analysis measures, by on-line recalculation or by very quick system of compression. The simplified structure of CUDA system and model of programming based on example Nvidia GForce GTX580 card are presented by our poster contribution in stand-alone version and as ROOT application.

Highlights

  • The requirement of high speed for visualization of computer data needed for present general application - forces construction changes of graphic processors

  • It is important to present that this system is coupled with calculations and graphics while other company solutions connected strictly with graphics

  • The web page https://developer.nvidia.com/cuda-zone is devoted for description Compute Unified Device Architecture (CUDA) systems

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Summary

Usage of parallel computing CUDA routines in standard root analysis

Compute Unified Device Architecture (CUDA) is a parallel computing platform developed by Nvidia for increase speed of graphics by usage of parallel mode for processes calculation. The success of this solution has opened technology General-Purpose Graphic Processor Units (GPGPUs) for applications not coupled with graphics. The requirement of high speed for visualization of computer data needed for present general application - forces construction changes of graphic processors. The same architecture of these devices enables simple adapt the graphic card as independent strong parallel computer unit. These cards are existing in architecture of old. EPJ Web of Conferences version families, and in solutions from 2011 year named Fermi series 500, Kepler 2012/600, Maxwell 2013/700 and Volta in future

Internal memory
Global memory Constant memory Texture memory
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