Chapter 1, Getting Started with Parallel Computing and Python, provides an overview of parallel programming architectures and programming models. The chapter introduces the Python programming language, discussing how the characteristics of the language, its ease of learning and use, its extensibility, and the richness of the available software libraries and applications all make Python a valuable tool for any application, and especially, of course, for parallel computing.
Chapter 2, Thread-Based Parallelism, discusses thread parallelism using the threading Python module. Readers will learn, through full programming examples, how to synchronize and manipulate threads to implement in their multithreading applications.
Chapter 3, Process-Based Parallelism, guides the reader through the process-based approach to parallelizing a program. A complete set of examples will show readers how to use the multiprocessing Python module.
Chapter 4, Message Passing, is focused on message-passing exchange communication systems. In particular, the mpi4py library will be described with a lot of application examples.
Chapter 5, Asynchronous Programming, explains the asynchronous model for concurrent programming. In some ways, it is simpler than the threaded one because there is a single instruction stream, and tasks explicitly relinquish control instead of being suspended arbitrarily. The chapter shows readers how to use the asyncyio module to organize each task as a sequence of smaller steps that must be executed in an asynchronous manner.
Chapter 6, Distributed Python, introduces the reader to distributed computing, which is the process of aggregating several computing units to collaboratively run a single computational task in a transparent and coherent way. In particular, the example applications provided in the chapter describe the use of the socket and Celery modules to manage distributed tasks.
Chapter 7, Cloud Computing, provides an overview of the main cloud-computing technologies in relation to the Python programming language. The PythonAnywhere platform is very useful for deploying Python applications on the cloud, and will be examined in this chapter. This chapter also contains example applications demonstrating the use of containers and serverless technologies.
Chapter 8, Heterogeneous Computing, looks at the modern GPUs that are providing breakthrough performance for numerical computing at the cost of increased programming complexity. In fact, the programming models for GPUs require that the coder manually manage the data transfer between the CPU and GPU. This chapter will teach the reader, using programming examples and use cases, how to exploit the computing power provided by GPU cards using the powerful Python modules of PyCUDA, Numba, and PyOpenCL.
Chapter 9, Python Debugging and Testing, is the last chapter that introduces two important topics on software engineering: debugging and testing. In particular, the following Python frameworks will be described: winpdb-reborn for debugging, and unittest and nose for software testing.