Functional Programming and Scientific Computing

本研究的目标是设计，实现和实际应用的功能性编程语言支持科学代码的发展。 函数式语言和科学计算似乎非常不匹配。 问题不在于这些语言的表现力，这是优秀的，但他们的效率，这是穷人。 最近对函数式语言的研究表明，如果在整个语言设计中注意的话，赋值实际上可能与引用透明性一致。 本研究使用这样一种语言，命令式Lambda演算（ILC），在随机过程建模领域开发程序。 虽然在顺序处理器上的效率低下一直是函数式语言的缺点，但它们在并行处理器上的效率一直是一个强大的吸引力。 本研究的一个重要目标是获得一个高效的并行实现迭代学习控制。 这种奋进的成功潜力很高，因为ILC中赋值的存在避免了向命令式形式的转换，使编译器只处理高阶函数和惰性求值本身的（很好理解的）问题。 为了充分利用现代并行体系结构，特别是非均匀存储器访问机器，控制数据的位置和任务的调度需要通过ILC的并行语言扩展来提供。

The goal of this research is the design, implementation, and realistic application of a functional programming language supporting the development of scientific codes. Functional languages and scientific computing appear to be singularly mismatched. The problem is not with the expressiveness of these languages, which is excellent, but their efficiency, which is poor. Recent research in functional languages suggests that assignment may in fact be consistent with referential transparency, if care is taken in the overall language design. This research uses one such language, the Imperative Lambda Calculus (ILC), to develop programs in the domain of random process modelling. Although inefficiency on sequential processors has long been a drawback of functional languages, their efficiency on parallel processors has been a strong attraction. An important goal of this research is to obtain an efficient parallel implementation of ILC. The potential for success in this endeavor is high because the presence of assignment in ILC obviates the transformation to imperative form, leaving the compiler to handle only the (well understood) problems of higher-order functions and lazy evaluation themselves. To fully exploit modern parallel architectures, especially non-uniform memory access machines, control over the placement of data and scheduling of tasks needs to be provided by way of parallel language extensions to ILC.