Python并行编程参考手册-(影印版)

Preface Chapter 1: Getting Started with Parallel Computing and PythonIntroductionThe parallel computing memory architectureMemory organizationParallel programming modelsHow to design a parallel programHow to evaluate the performance of a parallel programIntroducing PythonPython in a parallel worldIntroducing processes and threadsStart working with processes in PythonStart working with threads in Python Chapter 2: Thread-based ParallelismIntroductionUsing the Python threading moduleHow to define a threadHow to determine the current threadHow to use a thread in a subclassThread synchronization with Lock and RLockThread synchronization with RLockThread synchronization with semaphoresThread synchronization with a conditionThread synchronization with an eventUsing the with statementThread communication using a queueEvaluating the performance of multithread applications Chapter 3: Process-based ParallelismIntroductionHow to spawn a processHow to name a processHow to run a process in the backgroundHow to kill a processHow to use a process in a subclassHow to exchange objects between processesHow to synchronize processesHow to manage a state between processesHow to use a process poolUsing the mpi4py Python modulePoint-to-point communicationAvoiding deadlock problemsCollective communication using broadcastCollective communication using scatterCollective communication using gatherCollective communication using AIItoallThe reduction operationHow to optimize communication Chapter 4: Asynchronous ProgrammingIntroductionUsing the concurrent.futures Python modulesEvent loop management with AsyncioHandling coroutines with AsyncioTask manipulation with AsyncioDealing with Asyncio and Futures Chapter 5: Distributed PythonIntroductionUsing Celery to distribute tasksHow to create a task with CeleryScientific computing with SCOOPHandling map functions with SCOOPRemote Method Invocation with Pyro4Chaining objects with Pyro4Developing a client-server application with Pyro4Communicating sequential processes with PyCSPUsing MapReduce with DiscoA remote procedure call with RPyC Chapter 6: GPU Programming with PythonIntroductionUsing the PyCUDA moduleHow to build a PyCUDA applicationUnderstanding the PyCUDA memory model with matrix manipulationKernel invocations with GPUArrayEvaluating element-wise expressions with PyCUDAThe MapReduce operation with PyCUDAGPU programming with NumbaProUsing GPU-accelerated libraries with NumbaProUsing the PyOpenCL moduleHow to build a PyOpenCL applicationEvaluating element-wise expressions with PyOpenCITesting your GPU application with PyOpenCL Index