From Business Analytics To Accelerator Clouds

该提案旨在为格鲁吉亚理工学院网站（牵头）进行的计算机系统研究实验研究中心提供资金。基础研究的资助申请由NSF批准的招标（NSF 10-507）授权。征集邀请I/UCRC提交支持行业定义的基础研究的提案。 加速器云正在不断发展，以应对业务数据的巨大增长以及数据中心和云系统中功耗的影响。该计划旨在开发配备高性能加速器的加速器云，例如用于通用计算的新一代图形处理单元（GPU）。加速器云在企业中的开发、部署和广泛采用具有智力和相关的工程挑战。该提案包括一个新的特定于域的中间表示，内核IR（中间表示）定制的业务数据分析应用程序。这种IR模型对于企业应用程序的编译是高效的，并且对于将优化的编译驱动到一系列加速器目标是强大的。它将确保跨各种平台的可移植性。此外，它还包括在线程序分析技术和转换，以优化在一系列硬件目标上的执行。利用云中的GPU资源的能力可以显著改善许多数据密集型和计算密集型应用程序，例如保险公司的风险评估。考虑到这项工作的实验性质，所开发的工具很可能会被参与的公司所利用。PI打算继续积极参与CRA-W和来自格鲁吉亚理工学院妇女资源中心的学生。该提案提供了令人信服的教育组成部分的细节，以及它们如何与拟议的研究相联系。提议者在将拟议的研究纳入教育方面有着良好的记录。工业合作伙伴令人信服地参与了拟议的研究（对概念感兴趣，综合教育，实习）。

This proposal seeks funding for the Center for Experimental Research in Computer Systems studies conducted by the Georgia Institute of Technology site (lead). Funding Requests for Fundamental Research are authorized by an NSF approved solicitation, NSF 10-507. The solicitation invites I/UCRCs to submit proposals for support of industry-defined fundamental research. Accelerator Clouds are evolving in response to enormous growth in business data and the impact of power consumption in data-centers and cloud systems. This program address the development of accelerator clouds equipped with high performance accelerators such as the new generation of graphics processing units (GPUs) used for general purpose computing. The development, deployment, and widespread adoption of accelerator clouds in the enterprise have intellectual and associated engineering challenges. This proposal includes a new domain specific intermediate representation, the kernel IR (intermediate representation) tailored to business data analytics applications. This model for IR is efficient for compilation of enterprise applications and powerful for driving the optimized compilation to a range of accelerator targets. It will ensure portability across a range of platforms. Also, it includes on-line program analysis techniques and transformations to optimize the execution on a range of hardware targets. The ability to leverage the GPU resources in the cloud can significantly improve many data-intensive and compute-intensive applications such as risk assessment for insurance companies. Given the experimental nature of the work, it is likely that the tools developed will be leveraged by the companies involved Logic Blox, IBM). The PIs intent to continue their active participation with CRA-W, and with students from Georgia Tech's Women Resource Center. The proposal gives convincing details of educational components and how they are linked to the proposed research. The proposers have a strong track record in integrating the proposed research in education. The industrial partners are convincingly involved in the proposed research (interest in the concepts, integrated education, internships).