Temperature-driven Thread mapping and Shadowing in Hybrid Multi-cores (SMASH)

本项目旨在研究新的温度驱动运行时技术，以促进由可编程和可重构核心组成的未来混合多核的可靠运行。在此之前，我们将线程动态映射到软件和硬件核心，以优化基于传感器采样核心温度的热平衡和梯度。我们允许模态内线程迁移，即分别在软件内核或硬件内核之间迁移线程。我们现在建议甚至跨软件/硬件边界迁移线程，这被称为跨模式迁移。此外，我们之前介绍了线程遮蔽来检测核心故障。操作系统自主地将影子线程附加到应用程序线程上，并比较它们的操作系统调用签名。我们现在还建议对内存访问进行影子处理，以便能够检测更多类型的故障。此外，我们将开发错误注入方法，这将为研究错误模型和评估我们的阴影技术的有效性奠定基础。我们提出了一个热敏图，该图捕获了有关岩心上的热应力线以及岩心在项目最后阶段对热应力的敏感性的信息。除此之外，我们现在将应用在线学习技术来确定热敏图的静态和动态参数，这使我们能够开发基于预测的热驱动制图策略。

This project aims at the investigation of novel temperature-driven runtime techniques to facilitate dependable operation of future hybrid multi-cores comprising programmable and reconfigurable cores. Previously, we mapped threads dynamically to software and hardware cores to optimize for thermal balance and gradients based on sensors sampling core temperatures. We allowed for intra-modal thread migration where we migrate threads between software cores or hardware cores, respectively. We now propose to migrate threads even across the software/hardware boundary which is denoted as trans-modal migration. Furthermore, we previously introduced thread shadowing to detect core failures. The operating system autonomously attaches shadow threads to application threads and compares their signatures of operating system calls. We now propose to also shadow memory accesses to be able to detect more types of failures. Furthermore, we will develop error injection methods which will form a basis for studying error models and to evaluate the effectiveness of our shadowing techniques. We proposed a thermal sensitivity map that captures information about the thermal stress threads pose on cores as well as the cores’ susceptibility for thermal stress in the last project phase. Beyond that, we will now apply on-line learning techniques to determine static and dynamic parameters of the thermal sensitivity map which enables us to develop prediction-based thermally-driven mapping strategies.