Error propagation analysis for GPGPU applications

由于特征尺寸缩小和制造差异，硬件错误正在增加。传统型 从软件中屏蔽硬件错误的技术，如双模块冗余，在 商用系统的电力消耗术语。因此，迫切需要软件辅助 用于处理硬件故障的技术。该项目将探索使用基于软件的技术来 为在图形处理单元(GPU)(也称为GPGPU)上运行的应用程序提供容错能力 申请。GPU在加速数据中心和 高性能计算(HPC)空间，其中能源效率至关重要。我们将重点关注 对可靠性有最大影响的错误，以实现能效和可靠性。 这项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)拥有研发 通过最近的一次收购(30名工程师)出现在多伦多，拟议的研究对 他们的长期业务需求。其次，该项目为保护通用软件奠定了基础 应用程序免受硬件错误的影响。由于硬件故障预计将在未来几代处理器中增加， 保护未来的软件系统不受硬件错误的影响变得至关重要。这项研究的结果可以 因此，可用于一些关键领域，如航空航天、国防和生物医学工程。最后， 参与此项目的学生(HQP)将接受以下方面的培训：开发错误恢复技术 GPGPU应用程序，这是越来越受欢迎的。

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.