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OpenCL in Action: How to Accelerate Graphics and Computation by Matthew Scarpino

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Chapter 11. Reduction and sorting

 

This chapter covers
  • Implementing parallel processing tasks with MapReduce and OpenCL
  • Sorting data with the bitonic sort and radix sort algorithms

 

At long last, we’re going to stop talking about OpenCL’s structures and functions and start putting them to use. In particular, this chapter focuses on three practical applications of OpenCL: MapReduce, the bitonic sort, and the radix sort. These applications all use a divide-and-conquer methodology to process data in parallel, and they partition data so that each parallel process can operate independently.

The goal of MapReduce isn’t to solve a particular problem, but to provide a framework for solving a class of problems that involve distributed processing. ...

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