Structuring data parallelism using categorical data types

Data parallelism is a powerful approach to parallel computation, particularly when it is used with complex data types. Categorical data types are extensions of abstract data types that structure computations in a way that is useful for parallel implementation. In particular, they decompose the search for good algorithms on a data type into subproblems, all homomorphisms can be implemented by a single recursive, and often parallel, schema, and they are equipped with an equational system that can be used for software development by transformation.<
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