Workshop:DRBSD-6: The 6th International Workshop on Data Analysis and Reduction for Big Scientific Data
Authors: Scott Klasky (Oak Ridge National Laboratory (ORNL)); Qing Liu (New Jersey Institute of Technology); Ian Foster (Argonne National Laboratory (ANL), University of Chicago); and Mark Ainsworth (Brown University)
Abstract: A growing disparity between simulation speeds and I/O rates makes it increasingly infeasible for applications to save all results for analysis. In this new world, applications must increasingly perform online data analysis and reduction; tasks that introduce algorithmic, implementation and programming model challenges that are unfamiliar to many scientists and that have major implications for the design of various elements of exascale systems.
This trend has spurred interest in online data analysis and reduction methods, motivated by a desire to conserve I/O bandwidth, storage and/or power; increase accuracy of data analysis results; and/or make optimal use of parallel platforms, among other factors. This requires our community to understand clear yet complex relationships among application design, data analysis and reduction methods, programming models, system software, hardware and other elements of a next-generation high-performance computer, particularly given constraints such as applicability, fidelity, performance portability and power efficiency.