Course Outline


Parallel Programming in Theory

  • Memory architecture
  • Memory organization

Thread-Based and Process-Based Parallelism

  • Instantiating and determining a thread
  • Working with thread synchronization
  • Creating, naming, running, and synchronizing a process
  • Using Asyncio for asynchronous programming

Distributed Python

  • Using Celery
  • Using SCOOP
  • Using Pyro4
  • Using PyCSP
  • Using RPyC

GPU Programming

  • Using the PyCUDA module
  • Using NumbaPro
  • Using PyOpenCL
  • Testing with PyOpenCL

Testing and Troubleshooting

  • Testing with unit testing
  • Testing with mock testing

Summary and Conclusion


  • Python programming experience


  • Software Developers
 14 Hours

Testimonials (5)

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