Using GPUs for runtime monitoring and trace analysis

计算机软件在规模和复杂性上呈指数级增长。尽管复杂性增加了，但软件仍然必须是正确的，这意味着软件必须符合给定的规范，因此没有软件缺陷。运行时监视是一种在已部署系统上的运行时检查软件正确性的技术。随着图形处理单元(GPU)上通用计算的出现，问题变成了如何将这一新技术用于运行时监控系统，特别是与轨迹分析相结合的组合方法。 该项目将研究使用GPU进行运行时监控和跟踪分析的加速算法，并利用GPU系统与运行在主处理器上的应用程序的独立性开发新的恢复技术。这些结果将提高加拿大在系统研究中的知名度，并加强加拿大在研究中的作用。除了预期的科学出版物，行业赞助商AMD同意帮助推广开发的库和工具，如果它们满足AMD的要求，这将进一步提高项目的影响。

Computer software is growing exponentially in size and thus complexity. Despite this growth in complexity, software still must be correct, which means that the software must comply with a given specification and is thus free of software defects. Runtime monitoring is a technique that checks the correctness of the software at run time on the deployed system. With the advent of general purpose computation on graphics processing units (GPUs), the question becomes how this new technology can be leveraged for runtime monitoring systems, especially in combination with trace analysis a combined approach. This project will investigate the use of GPU for runtime monitoring and accelerating algorithms for trace analysis, and develop novel recovery techniques using the independence of GPU systems from applications running on the main processor. These results will raise Canada's visibility in systems research and strength Canada's role in research. Besides the expected scientific publications, the industrial sponsor AMD agreed to help promote the developed library and tools if they meet AMD's requirements, that will further raise the impact of the project.