数学函数的代码生成器

Nicolas Brunie, F. D. Dinechin, O. Kupriianova, C. Lauter
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引用次数: 33

摘要

典型的浮点环境包括对大约30个数学函数的一小部分支持,例如指数函数、对数函数、三角函数和双曲函数。这些函数由数学软件库(libm)提供,通常在IEEE754中提供单精度、双精度和四精度。本文建议用一种更一般的方法来取代这种libm范式:在任意域上以任意精度按需生成数值函数代码。首先,这样的代码生成为程序员打开了可用的libm函数空间。它可以捕获更广泛的函数集,甚至可以捕获非标准域和准确性/性能点上的标准函数。其次,编写libm代码需要对指令选择和调度进行微调以提高性能,还需要使用复杂的浮点技术来提高精度。通过代码生成自动化此任务可以提高对代码的信心,同时支持更好的设计空间探索,从而缩短上市时间,甚至对于libm函数也是如此。本文讨论了这种范式转换的新挑战,并介绍了http://www.metalibm.org/上可用的开源函数代码生成器的当前状态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Code Generators for Mathematical Functions
A typical floating-point environment includes support for a small set of about 30 mathematical functions such as exponential, logarithm, trigonometric and hyperbolic functions. These functions are provided by mathematical software libraries (libm), typically in IEEE754 single, double and quad precision. This article suggests to replace this libm paradigm by a more general approach: the on-demand generation of numerical function code, on arbitrary domains and with arbitrary accuracies. First, such code generation opens up the libm function space available to programmers. It may capture a much wider set of functions, and may capture even standard functions on non-standard domains and accuracy/performance points. Second, writing libm code requires fine-tuned instruction selection and scheduling for performance, and sophisticated floating-point techniques for accuracy. Automating this task through code generation improves confidence in the code while enabling better design space exploration, and therefore better time to market, even for the libm functions. This article discusses the new challenges of this paradigm shift, and presents the current state of open-source function code generators available on http://www.metalibm.org/.
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