Parallel applications often store data in multiple task-local files, for example, to remember checkpoints, to circumvent memory limitations, or to record performance data. When operating at very large processor configurations, such applications often experience scalability limitations when the simultaneous creation of thousands of files causes metadata contention or simply when large file counts complicate file management or operations on those files even destabilize the file system. SIONlib is a parallel I/O library that addresses this problem by transparently mapping a large number of task-local files onto a small number of physical files via internal metadata handling and block alignment to ensure high performance. Using SIONlib significantly reduces file creation overhead and simplifies file handling, while requiring only minimal source code changes and without penalizing read and write performance. We evaluate SIONlib's efficiency with up to 64K tasks and report significant performance improvements in two use cases.
Energy efficiency is a key design principle of the IBM Blue Gene series of supercomputers, and Blue Gene systems have consistently gained top GFlops/Watt rankings on the Green500 list. The Blue Gene hardware and management software provide built-in features to monitor power consumption at all levels of the machine's power distribution network. This paper presents the Blue Gene/P power measurement infrastructure and discusses the operational aspects of using this infrastructure on Petascale machines. We also describe the integration of Blue Gene power monitoring capabilities into system-level tools like LLview, and highlight some results of analyzing the production workload at Research Center Jülich (FZJ).
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