CSR: Small: Collaborative Research: Comprehensive Algorithmic Resilience (CAR) for Big Data Analytics

大数据分析是从非常庞大的数据集中挖掘有用知识的过程，对许多研究和应用领域的进步至关重要。随着制造技术规模的缩小和功率密度的增加，现代计算机系统遭受潜在的硬件和软件故障，这可能会表现为错误。在系统上执行长期运行的大数据分析时会发生错误，导致崩溃或更糟，并以静默方式返回不正确的结果。虽然存在许多基于硬件的弹性方法，但它们往往以过度降低功率效率和大幅增加设计复杂性等为代价。该项目旨在通过采用一系列全面的算法弹性(CAR)软件技术来加强流行的大数据分析，这些技术包括并发错误检测、协调检查点和执行恢复，以实现高执行弹性。在分析过程中同时检测到潜在的硬件和软件错误后，CAR可以从检测到的错误中恢复执行，而不会像基于硬件的弹性方法那样产生高昂的开销。该项目的研究活动旨在实现六个主要目标，重点是解决实现CAR的几个技术挑战，根据调查人员？鼓励取得初步成果和开展前期工作。该项目的成功可以使广泛的科学和工业应用受益，因为它更好地支持大数据分析和处理。这项研究的研究进展将纳入本科生和研究生教育，通过技术演示和网站广泛传播和分享，并激发高中生对STEM的兴趣。

Big Data analytics is the process of mining useful knowledge in very large data sets, critical to the advancement of many research and application fields. With manufacturing technology downscaling coupled with increasing power densities, modern computer systems suffer from potential hardware and software failures, which can manifest themselves as errors. The errors are to happen when long-running Big Data analytics are executed on the systems, causing crash or worse, returning incorrect results silently. While numerous hardware-based resilience methods exist, they often come at the cost of excessive power efficiency reduction and substantial design complexity enlargement among others. The project aims to reinforce popular Big Data analytics by embracing a host of comprehensive algorithmic resilience (CAR) software techniques that include concurrent error detection, coordinated checkpointing, and execution recovery, for high execution resilience. Upon detecting potential hardware and software errors concurrently during analytics, CAR enables execution recovery from detected errors without lofty overhead common to hardware-based resilience methods. Research activities of the project aim to achieve six main objectives that focus on addressing several technical challenges to realize CAR, based on investigators? encouraging preliminary results and prior work. The success of this project can benefit wide scientific and industrial applications due to its better support of Big Data analytics and processing. Research advances from this research are to be incorporated into undergraduate and graduate education, to be disseminated and shared broadly through technical presentations and by a website, and to inspire high school students for their STEM interest.