What this book covers
Chapter 1, Getting Started with Parallel Computing and Python, gives you an overview of parallel programming architectures and programming models. This chapter introduces the Python programming language, the characteristics of the language, its ease of use and learning, extensibility, and richness of software libraries and applications. It also shows you how to make Python a valuable tool for any application, and also, of course, for parallel computing.
Chapter 2, Thread-based Parallelism, discusses thread parallelism using the threading Python module. Through complete programming examples, you will learn how to synchronize and manipulate threads to implement your multithreading applications.
Chapter 3, Process-based Parallelism, will guide through the process-based approach to parallelize a program. A complete set of examples will show you how to use the multiprocessing Python module. Also, this chapter will explain how to perform communication through processes, using the message passing parallel programming paradigm via the mpi4py Python module.
Chapter 4, 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. This chapter will show you how to use the Python asyncio module to organize each task as a sequence of smaller steps that must be executed in an asynchronous manner.
Chapter 5, Distributed Python, introduces you to distributed computing. It is the process of aggregating several computing units logically and may even be geographically distributed to collaboratively run a single computational task in a transparent and coherent way. This chapter will present some of the solutions proposed by Python for the implementation of these architectures using the OO approach, Celery, SCOOP, and remote procedure calls, such as Pyro4 and RPyC. It will also include different approaches, such as PyCSP, and finally, Disco, which is the Python version of the MapReduce algorithm.
Chapter 6, GPU Programming with Python, describes the modern Graphics Processing Units (GPUs) that provide breakthrough performance for numerical computing at the cost of increased programming complexity. In fact, the programming models for GPUs require the programmer to manually manage the data transfer between a CPU and GPU. This chapter will teach you, through the programming examples and use cases, how to exploit the computing power provided by the GPU cards, using the powerful Python modules: PyCUDA, NumbaPro, and PyOpenlCL.
