introduction to gpu programming with the cuda platform · resources...

Introduction to GPU Programmingwith the CUDA Platform

Marco Dimas Gubitosogubi@ime.usp.br4th INFIERI, 2017

Instituto de Matemática e EstatísticaUniversidade de São Paulo

About

Pedro Bruelphrb@ime.usp.br

Alfredo Goldmangold@ime.usp.br

Marco Dimas Gubitoso

gubi@ime.usp.br

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Hardware Acceleration

Percentage of systems with accelerators in the Top500:

Image: top500.org/lists/2016/06/download/TOP500_201606_Poster.pdf [Accessed in 29/07/16]2/7

CUDA C

Programming Model:

• Kernels• Threads hierarchy• Memory hierarchy• Heterogeneous Programming

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CUDA Libraries

Image: developer.nvidia.com/gpu-accelerated-libraries [Accessed in 29/07/16] 4/7

The Lab: Part I

1. Introduction

2. Heterogeneous Computing

3. GPUs

4. CUDA Platform

5. CUDA C

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The Lab: Part II

6. Recapitulation

7. Compiling CUDA Applications

8. Optimization Best Practices

9. Analysing CUDA Applications

10. Otimizing CUDA Applications

11. Conclusion

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Resources

This presentation and all source code are available at GitHub:

• github.com/phrb/intro-cuda

More resources:

• CUDA C: docs.nvidia.com/cuda/cuda-c-programming-guide• CUDA Toolkit: developer.nvidia.com/cuda-toolkit• Best Practices Guide:

• docs.nvidia.com/cuda/cuda-c-best-practices-guide

• GPU Teaching Kit: syllabus.gputeachingkit.com• iPython: ipython.org/notebook.html• Anaconda: continuum.io/downloads

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Introduction to GPU Programmingwith the CUDA Platform

Marco Dimas Gubitosogubi@ime.usp.br4th INFIERI, 2017

Instituto de Matemática e EstatísticaUniversidade de São Paulo