Error propagation analysis for GPGPU applications

GPGPU 应用程序的错误传播分析

• 批准号: 489003-2015
• 负责人: Pattabiraman, Karthik
• 金额: $ 1.68万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=586439
• 关键词: Error; propagation; analysis; GPGPU; applications

Hardware errors are increasing due to shrinking feature sizes and manufacturing variations. Conventional techniques to mask hardware errors from the software such as Dual Modular Redundancy are too expensive in terms of power consumption for commodity systems. Therefore, there is a critical need for software assisted techniques for handling hardware faults. This project will explore the use of software-based techniques for providing error resilience to applications running on Graphic Processing Units (GPUs), also known as GPGPU applications. GPUs are finding significant traction in accelerating applications in the data center and high-performance computing (HPC) space, where energy efficiency is vitally important. We will focus on errors that have the maximum impact on reliability to achieve both energy-efficiency and reliability. This research project will benefit Canada in three ways. First, NVIDIA (our industry partner) has an R&D presence in Toronto through a recent acquisition (30 engineers), and the proposed research is important for their long-term business needs. Second, this project lays the foundations of protecting general-purpose software applications from hardware errors. As hardware faults are expected to increase in future processor generations, it becomes essential to protect future software systems from hardware errors. The results of this research can thus be used in a number of critical domains such as aerospace, defence and bio-medical engineering. Finally, the students (HQP) involved in this project will be trained in the development of error resilience techniques for GPGPU applications, which are becoming more and more popular.

由于特征尺寸缩小和制造差异，硬件错误正在增加。常规 从软件中屏蔽硬件错误的技术（如Dual Modular Redundance）过于昂贵， 商品系统的功耗术语。因此，迫切需要软件辅助 处理硬件故障的技术。本项目将探索使用基于软件的技术， 为在图形处理单元（GPU）（也称为GPGPU）上运行的应用程序提供错误恢复能力 应用. GPU在加速数据中心的应用程序方面具有重要的吸引力， 高性能计算（HPC）空间，其中能效至关重要。我们将专注于 对可靠性有最大影响的错误，以实现能源效率和可靠性。 该研究项目将在三个方面使加拿大受益。首先，NVIDIA（我们的行业合作伙伴）拥有一个研发 通过最近的收购（30名工程师）在多伦多的存在，拟议的研究对于 他们的长期业务需求。第二，为通用软件的保护奠定了基础 应用程序的硬件错误。由于硬件故障预计在未来的处理器中会增加， 保护未来的软件系统免受硬件错误的影响变得至关重要。这项研究成果能 因此可用于航空航天、国防和生物医学工程等许多关键领域。最后， 参与该项目的学生（HQP）将接受错误恢复技术开发方面的培训， GPGPU应用程序，这是越来越受欢迎。