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Parallelism Real Time

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This paper explores the improvements that graphical processing units (GPUs) can provide for real-time systems. We present the essential steps for developing applications using CUDA, emphasizing the significance of parallelism in achieving stringent time requirements in critical applications such as aerospace and biomedical systems. Key concepts and experiments demonstrate how GPUs' unique capabilities outperform traditional CPUs, highlighting their efficiency in processing and execution time. The findings encourage the shift towards parallel programming in the development of modern real-time systems.

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Parallelism Real Time

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  1. “Politehnica” University of Timişoara Automation and Computing University Computers Department Parallelism Real Time Anca Batori Lavinia Basarabă Anca Brandimbur iunie 2010

  2. Objectives • presenting the use of graphical processing units (GPU) to achieve significant improvements for real time systems • presenting the main steps for developing an application using CUDA • offering a source of resources

  3. Contents • Introduction • Parallelism • Application description • Experiments • Conclusions

  4. Introduction • Real Time Systems are an important area of research and development • Many applications: Airplanes, biomedical accelerators, nuclear power plants • Necessity of parallelism to achieve desired time limits

  5. Parallelism • Can be hardware and software • GPUs represent a combination • GPUs have certain characteristcs, that CPUs do not poses, that can be useful for certain application • More processing power, less flexibility • The application are developed using CUBLAS

  6. ArchitecturesCPU vs GPU • Cores number 4 • Threads 2 • Cache memory => random address acces • Cores number 240 • Threads 1024 • Cache memory => fast access to consecutive addresses Sursa:www.nvidia.com

  7. Support Vector Machines (SVM) Possibilities to split two classes

  8. SVM geometrical representation

  9. CPU versus GPU • Speedup 54xfor1000 images • Even better results for bigger training sets

  10. Multiclass SVM • “One Against All” (OAA) • training: M binary classifiers(M number of classes) • testing: strategy„the winner takes it all” • “One Against One” (OAO) • training : M(M-1)/2binary classifiers • testing : strategymaximum number of votes • “Directed Acyclic Graph” (DAG) • testing : decision tree

  11. Multiclass Algorithms • NVIDIA GTX 280 • IDSIA sets

  12. Multiclass Algorithms

  13. Conclusions • In the period of ubiquitous and pervasive systems, real time systems are a very important field • Since CPUs do not evolve, so fast as they used to, there is a shift towards parallel programming and more and more systems are developed this way • GPUs can improve the execution time for an application 100x times

  14. Thank you

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