Catalog Poster for ASCR CS PI Meeting 2017

Department of Energy (DOE) leadership computing facilities are in the process of deploying extreme-scale high-performance computing (HPC) systems with the long-range goal of building exascale systems that perform more than a quintillion (a billion billion) operations per second. More powerful computers mean researchers can simulate biological, chemical, and other physical interactions with an unprecedented amount of realism. However, as HPC systems become more complex, system integrators, component manufacturers as well as computing facilities have to and are preparing for unique computing challenges. Of particular concern are occurrences of unfamiliar or more frequent faults in both hardware technologies and software applications that can lead to computational errors or system failures.

This project will help DOE computing facilities protect extreme-scale systems by characterizing potential faults and creating models that predict their propagation and impact. The Collaboration of Oak Ridge, Argonne and Lawrence Livermore National Laboratories (CORAL) is a private/public partnership that will stand up three extreme-scale systems in 2017/2018, each operating at about 150 to 200 petaflops, or nearly 10 times more power than the 27-petaflop Titan at Oak Ridge National Laboratory (currently the fastest system in the United States) and about a tenth of exascale power.

By monitoring hardware and software performance on current DOE systems, such as Titan, and applying the data to fault analysis and vulnerability studies, this effort will capture observed and inferred fault conditions and extrapolate this knowledge to CORAL and other extreme-scale systems. Using these analyses, the project team will create assessment tools, including a fault taxonomy and catalog as well as fault models, to provide computing facilities with a clear picture of the fault characteristics in DOE computing environments and inform technical and operational decisions to improve resilience. The catalog, models, and the software resulting from this project, will be made publicly available.