Abstract
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 multi-level 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 user-defined 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 object-centric 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 analysis-oriented approaches.