The pros are as follows:
- They allow the use of heterogeneous systems with different types of microprocessors.
- The same code runs on different systems.
Pros of OpenCL and PyOpenCL
by Giancarlo Zaccone
Python Parallel Programming Cookbook - Second Edition
- Title Page
- Copyright and Credits
- Dedication
- About Packt
- Contributors
- Preface
- Getting Started with Parallel Computing and Python
- Thread-Based Parallelism
- What is a thread?
- Python threading module
- Defining a thread
- Determining the current thread
- Defining a thread subclass
- Thread synchronization with a lock
- Thread synchronization with RLock
- Thread synchronization with semaphores
- Thread synchronization with a condition
- Thread synchronization with an event
- Thread synchronization with a barrier
- Thread communication using a queue
- Process-Based Parallelism
- Message Passing
- Technical requirements
- Understanding the MPI structure
- Using the mpi4py Python module
- Implementing point-to-point communication
- Avoiding deadlock problems
- Collective communication using a broadcast
- Collective communication using the scatter function
- Collective communication using the gather function
- Collective communication using Alltoall
- The reduction operation
- Optimizing communication
- Asynchronous Programming
- Distributed Python
- Cloud Computing
- Heterogeneous Computing
- Understanding heterogeneous computing
- Understanding the GPU architecture
- Understanding GPU programming
- Dealing with PyCUDA
- Heterogeneous programming with PyCUDA
- Implementing memory management with PyCUDA
- Introducing PyOpenCL
- Building applications with PyOpenCL
- Element-wise expressions with PyOpenCL
- Evaluating PyOpenCL applications
- GPU programming with Numba
- Python Debugging and Testing
- Other Books You May Enjoy
Pros of OpenCL and PyOpenCL
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