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Data decomposition techniques
This document discusses different techniques for decomposing data and computations into parallel tasks, including: output data partitioning, input data partitioning, partitioning intermediate data, exploratory decomposition of search spaces, speculative decomposition, and hybrid approaches. It provides examples and diagrams to illustrate how to apply these techniques to problems like matrix multiplication, counting item frequencies, and the 15-puzzle problem. Key characteristics of derived tasks like task generation, sizes, data associations are also covered.
