Career: An Adaptive Compiler for Multi-core Environments

编译器是软件开发人员和计算机之间的关键部件。他们将软件开发人员编写的应用程序翻译成由计算机处理的机器代码。编译器的一项重要任务是优化应用程序，使其高效运行。开发优化编译器的传统方法是特别的、劳动密集型的和无效的。因此，为新处理器优化编译器通常产生的代码只能实现机器的一小部分功能。S可用性能。对于今天的多核架构来说尤其如此，即在单个芯片上并行处理处理器。这项研究将涉及调查人工智能社区的技术，这些技术将允许编译器自动适应和调整新的体系结构。实际上，这项研究将用自调优编译器取代手动调优，自动调整软件以匹配每个目标体系结构的性能特征。在这个项目中，PI建议探索为多核环境（ACME）开发自适应编译器的可行性，以在实现高性能的同时允许应用程序可移植性。PI将创建一个统计自动调优框架，以支持以下特性的概率表示：优化的效益分析，不同优化的适当运行时环境的识别和预测，以及有效地组合几个优化代码版本的可执行文件的生成。他将发明组件来精确测量应用程序和目标计算系统的特性。PI希望找到替代“传统”的技术。利用强大的机器学习模型进行优化效益分析。这些模型将处理广泛的并行应用程序和多核环境，它们将能够分析和预测不同动态环境下的效益。

Compilers are a critical component between the software developer and the computer. They translate application written by software developers into machine code that is processed by the computer. An important task of a compiler is to optimize applications so that they run efficiently. Traditional methods to develop optimizing compilers are ad-hoc, labor-intensive, and ineffective. As a consequence, optimizing compilers for a new processor often produces code that achieves only a fraction of the machine?s available performance. This is especially true for today's multi-core architectures, which are parallel processors on a single chip. This research will involve investigating techniques from the artificial intelligence community that will allow a compiler to automatically adapt and tune to new architectures. In effect, this research will replace hand-tuning with self-tuning compilers that adapt software automatically to match the performance characteristics of each target architecture.In this project, the PI proposes to explore the viability of developing adaptive compilers for multi-core environments (ACME) to allow application portability while still achieving high performance. The PI will create a statistical auto-tuning framework to support the probabilistic representation of the following features: the benefit analysis of optimizations, the identification and prediction of the appropriate runtime environment for different optimizations, and the generation of executables that efficiently combine several optimized code versions. He will invent components to measure accurately the characteristics of applications and targeted computing systems. The PI hopes to discover techniques to replace ?traditional? optimization benefit analysis with powerful machine learning models. These models will address the broad spectrum of parallel applications and multi-core environments, and they will be able to analyze and predict benefit under different dynamic contexts.