Preface
Chapter 1： Getting Started with Parallel Computing and Python
Introduction
The parallel computing memory architecture
Memory organization
Parallel programming models
How to design a parallel program
How to evaluate the performance of a parallel program
Introducing Python
Python in a parallel world
Introducing processes and threads
Start working with processes in Python
Start working with threads in Python
Chapter 2： Thread-based Parallelism
Introduction
Using the Python threading module
How to define a thread
How to determine the current thread
How to use a thread in a subclass
Thread synchronization with Lock and RLock
Thread synchronization with RLock
Thread synchronization with semaphores
Thread synchronization with a condition
Thread synchronization with an event
Using the with statement
Thread communication using a queue
Evaluating the performance of multithread applications
Chapter 3： Process-based Parallelism
Introduction
How to spawn a process
How to name a process
How to run a process in the background
How to kill a process
How to use a process in a subclass
How to exchange objects between processes
How to synchronize processes
How to manage a state between processes
How to use a process pool
Using the mpi4py Python module
Point-to-point communication
Avoiding deadlock problems
Collective communication using broadcast
Collective communication using scatter
Collective communication using gather
Collective communication using AIItoall
The reduction operation
How to optimize communication
Chapter 4： Asynchronous Programming
Introduction
Using the concurrent．futures Python modules
Event loop management with Asyncio
Handling coroutines with Asyncio
Task manipulation with Asyncio
Dealing with Asyncio and Futures
Chapter 5： Distributed Python
Introduction
Using Celery to distribute tasks
How to create a task with Celery
Scientific computing with SCOOP
Handling map functions with SCOOP
Remote Method Invocation with Pyro4
Chaining objects with Pyro4
Developing a client-server application with Pyro4
Communicating sequential processes with PyCSP
Using MapReduce with Disco
A remote procedure call with RPyC
Chapter 6： GPU Programming with Python
Introduction
Using the PyCUDA module
How to build a PyCUDA application
Understanding the PyCUDA memory model with matrix manipulation
Kernel invocations with GPUArray
Evaluating element-wise expressions with PyCUDA
The MapReduce operation with PyCUDA
GPU programming with NumbaPro
Using GPU-accelerated libraries with NumbaPro
Using the PyOpenCL module
How to build a PyOpenCL application
Evaluating element-wise expressions with PyOpenCI
Testing your GPU application with PyOpenCL
Index
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