Qameleon: Hardware/software Co-operative Automated Tuning for Heterogeneous Architectures

该奖项是根据2009年《美国复苏和再投资法案》(Public Law 111-5)资助的。推动异类架构以提高性能，同时降低能源消耗，给软件开发带来了巨大的挑战。例如，程序员必须在何时使用特殊加速器还是使用强大的核心CPU方面做出重大决定，还必须沉浸在复杂的体系结构细节中才能有效地进行调优。该研究项目的目标是使用一种新的框架来缓解这些挑战，该框架能够在高级别上表达广泛的计算，并随后针对底层的异类平台自动调优。更具体地说，PI提出了Qameleon，这是一个新的编程环境，可以使用统计机器学习技术自动和连续地协作调整程序和硬件配置。这项拟议的工作将是第一个在GPU编程中考虑在运行时自适应地划分异类平台上的计算的工作。这项工作还将提高对异类体系结构中编程功能、体系结构支持、性能和功能之间权衡的理解。这项研究还将根据统计建模的结果开发几个衡量标准来表征应用程序。这项拟议的研究汇集了来自体系结构、编译器、机器学习和应用程序的跨学科技术？以及来自学术界和工业界的研究人员构建新的通用编程接口，该接口可以向程序员隐藏异类体系结构的复杂性，同时仍然提供高性能和高能效的执行。Qameleon编程环境将通过将研究成果纳入针对计算机科学家和领域科学家的新本科课程而设计为在本科生水平上进行教学。

"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)."The push toward heterogeneous architectures to increase performance, while reducing energy consumption creates considerable challenges for software development. For example, programmers must make non-trivial decisions about when to use special accelerators vs. powerful core CPUs and also become steeped in complex architectural details to tune effectively. The goal of this research project is to alleviate these challenges using a novel framework that enables a wide-range of computations to be expressed at a high-level and subsequently tuned automatically for the underlying heterogeneous platform. More specifically, the PIs propose Qameleon, a new programming environment that can cooperatively tune the program and the hardware configuration automatically and continuously using statistical machine learning techniques. The proposed work will be the first in GPU programming to consider adaptively partitioning a computation on a heterogeneous platform at run-time. This work will also improve understanding of the trade-offs among programming features, architectural support, performance, and power in heterogeneous architectures. The research will also develop several metrics to characterize the application based on the outcome of the statistical modeling. The proposed research brings together cross-disciplinary techniques?from architectures, compilers, machine learning, and applications ? and researchers from both academia and industry to build new common programming interfaces that can hide the complexity of heterogeneous architectures from the programmers, while still providing high-performance and energy-efficient execution. The Qameleon programming environment will be designed to teach at the undergraduate level by incorporating research results into new undergraduate courses aimed at both computer scientists and domain scientists alike.