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Python Parallel Programming Cookbook

You're reading from   Python Parallel Programming Cookbook Master efficient parallel programming to build powerful applications using Python

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Product type Paperback
Published in Aug 2015
Publisher
ISBN-13 9781785289583
Length 286 pages
Edition 1st Edition
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Author (1):
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Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
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Table of Contents (8) Chapters Close

Preface 1. Getting Started with Parallel Computing and Python 2. Thread-based Parallelism FREE CHAPTER 3. Process-based Parallelism 4. Asynchronous Programming 5. Distributed Python 6. GPU Programming with Python Index

The MapReduce operation with PyCUDA


PyCUDA provides a functionality to perform reduction operations on the GPU. This is possible with the pycuda.reduction.ReductionKernel method:

ReductionKernel(dtype_out, arguments, map_expr ,reduce_expr, 
                name,optional_parameters)  

Here, we note that:

  • dtype_out: This is the output's data type. It must be specified by the numpy.dtype data type.

  • arguments: This is a C argument list of all the parameters involved in the reduction's operation.

  • map_expr: This is a string that represents the mapping operation. Each vector in this expression must be referenced with the variable i.

  • reduce_expr: This is a string that represents the reduction operation. The operands in this expression are indicated by lowercase letters, such as a, b, c, ..., z.

  • name: This is the name associated with ReductionKernel, with which the kernel is compiled.

  • optional_parameters: These are not important in this recipe as they are the compiler's directives.

The method executes...

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