Structuring data parallelism using categorical data types

D. Skillicorn
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引用次数: 14

Abstract

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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使用分类数据类型构建数据并行性
数据并行是一种强大的并行计算方法,尤其是在使用复杂数据类型时。分类数据类型是抽象数据类型的扩展,它以有助于并行执行的方式构造计算。特别是,分类数据类型将在数据类型上寻找好算法的过程分解为多个子问题,所有同态性都可以通过单一递归模式(通常是并行模式)来实现,而且分类数据类型还配备了一个等式系统,可以通过转换用于软件开发。
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