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组件。它们将软件开发人员编写的应用程序翻译成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，使其有效运行。 开发优化编译器的传统方法是临时的，劳动密集型的，并且效率低下。 因此，为新处理器优化编译器通常只能产生一小部分代码。的可用性能。这对于当今的多核架构尤其如此，多核架构是单个芯片上的并行处理器。 这项研究将涉及调查来自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到新的架构。 实际上，这项研究将取代手动调优与自调优编译器，自动适应软件，以匹配每个目标architecture.In这个项目的性能特点，PI建议探讨开发自适应编译器的多核环境（ACME），使应用程序的可移植性，同时仍然实现高性能的可行性。 PI将创建一个统计自动调优框架，以支持以下功能的概率表示：优化的效益分析，识别和预测不同优化的适当运行时环境，以及生成有效结合联合收割机多个优化代码版本的可执行文件。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希望发现技术来取代？传统？利用强大的机器学习模型进行优化效益分析。这些模型将解决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收益。

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.