Analysis in the Data Path of an ObjectCentric Data Management System Emerging high performance computing (HPC) systems are expected to be deployed with an unprecedented level of complexity due to a deep system memory and storage hierarchy. Efficient and scalable methods of data management and movement through the multilevel storage hierarchy of upcoming HPC systems will be critical for scientific applications at exascale. In this paper, we propose in locus analysis that allows registering userdefined functions (UDFs) and running those functions automatically while the data is moving between levels of a storage hierarchy. We implement this analysis in the data path approach in our objectcentric data management system, called Proactive Data Containers (PDC). The transparent invocation of analysis functions as part of PDC object mapping is an optimized approach to minimize latency to access data as it moves within the storage hierarchy. Because a user defined analysis or transform function will be invoked automatically by the PDC runtime, the user simply registers their functions for PDC to identify the function name as well as the required list of actual parameters. To demonstrate the validity and flexibility of this analysis approach, we have implemented several scientific analysis kernels to compare against other HPC analysisoriented approaches.

Design and Implementation of the Tianhe2 Data Storage and Management System With the convergence of highperformance computing (HPC), big data and artificial intelligence (AI), the HPC community is pushing for xe2x80x9ctriple usexe2x80x9d systems to expedite scientific discoveries. However, supporting these converged applications on HPC systems presents formidable challenges in terms of storage and data management due to the explosive growth of scientific data and the fundamental differences in IO characteristics among HPC, big data and AI workloads. In this paper, we discuss the driving force behind the converging trend, highlight three data management challenges, and summarize our efforts in addressing these data management challenges on a typical HPC system at the parallel file system, data management middleware, and user application levels. As HPC systems are approaching the border of exascale computing, this paper sheds light on how to enable applicationdriven data management as a preliminary step toward the deep convergence of exascale computing ecosystems, big data, and AI.