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

Qameleon：异构架构的硬件/软件协同自动调优

• 批准号: 0903447
• 负责人: Hyesoon Kim
• 金额: $ 26.78万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0903447&HistoricalAwards=false
• 关键词: Qameleon; Hardware; software; Co; operative

"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.

“该奖项是根据 2009 年美国复苏和再投资法案（公法 111-5）提供资金的。”推动异构架构以提高性能，同时降低能耗给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，并且还必须深入研究复杂的架构细节才能有效地进行调整。该研究项目的目标是使用一种新颖的框架来缓解这些挑战，该框架能够在高层表达各种计算，并随后针对底层异构平台自动调整。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连续地协同调整程序和硬件配置。所提出的工作将是 GPU 编程中第一个考虑在运行时在异构平台上自适应分区计算的工作。这项工作还将增进对异构架构中编程功能、架构支持、性能和功耗之间权衡的理解。该研究还将开发一些指标来根据统计建模的结果来表征应用程序。拟议的研究汇集了来自架构、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构架构的复杂性，同时仍然提供高性能和节能的执行。 Qameleon 编程环境将设计为本科生教学，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中。