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（公法111-5）资助的。“推动异构体系结构以提高性能，同时降低能耗，为软件开发带来了相当大的挑战。例如，程序员必须就何时使用特殊加速器与强大的核心CPU做出重要决定，并且还要沉浸在复杂的架构细节中以进行有效的调优。本研究项目的目标是使用一种新的框架来缓解这些挑战，该框架使广泛的计算能够在高级别上表达，并随后针对底层异构平台自动进行调整。更具体地说，PI提出了Qameleon，这是一种新的编程环境，可以使用统计机器学习技术自动持续地协同调整程序和硬件配置。拟议的工作将是第一个在GPU编程考虑自适应分区的计算在运行时的异构平台上。这项工作还将提高对异构体系结构中编程特性、体系结构支持、性能和功能之间的权衡的理解。该研究还将开发几个指标，以根据统计建模的结果来表征应用程序。拟议的研究汇集了跨学科的技术？从架构、编译器、机器学习和应用程序？与学术界和工业界的研究人员合作，构建新的通用编程接口，可以向程序员隐藏异构体系结构的复杂性，同时仍然提供高性能和高能效的执行。Qameleon编程环境将被设计为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来教授本科水平。

"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.