面向过程代码机械转换的启发式自动学习
Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code
摘要：
下一代艾级系统的当前趋势是将各种专用（协）处理器集成到传统的超级计算机中。然而，不同专用设备的集成增加了对这种类型的系统进行编程的异质性和复杂性的程度。由于异构系统在每表面单元的瓦特和FLOPS方面的效率，向更广泛的用户开放异构平台的访问是一个需要解决的重要问题。为了弥合异构系统和程序员之间的差距，在本文中，我们提出了一个基于机器学习的方法来学习语法定义的程序转换系统的转换策略。我们的方法提出了一种强化学习和分类方法的新组合，以有效地解决这类系统固有的问题。初步的结果表明，适合缓解异构系统的可编程性的方法。
英文摘要：
The current trend in next-generation exascale systems goes towards integrating a wide range of specialized (co-)processors into traditional supercomputers. However, the integration of different specialized devices increases the degree of heterogeneity and the complexity in programming such type of systems. Due to the efficiency of heterogeneous systems in terms of Watt and FLOPS per surface unit, opening the access of heterogeneous platforms to a wider range of users is an important problem to be tackled. In order to bridge the gap between heterogeneous systems and programmers, in this paper we propose a machine learning-based approach to learn heuristics for defining transformation strategies of a program transformation system. Our approach proposes a novel combination of reinforcement learning and classification methods to efficiently tackle the problems inherent to this type of systems. Preliminary results demonstrate the suitability of the approach for easing the programmability of heterogeneous systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
