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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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As HPC resources evolve into petascale resources and beyond, substantial mismatches in scale between the computation resources and the storage resources demand rethinking how to manage generated data for scientific discoveries. Process counts of 100,000s, 1,000,000 or more overwhelm storage resources causing IO to consume too large a percentage of total runtime. Shared scratch file systems that facilitate end-to-end processing by using multiple HPC resources compound the problem as much as they help. While it is tempting to perform micro optimizations to aid either writing, reading, or some analysis tasks, only optimizations that address the entire end-to-end science process will ultimately be useful. By carefully managing data output techniques mindful of later data analysis tasks, both write and read performance can be improved. Further, by incorporating `in transit' processing, data generation runtimes can be reduced even when considering the additional resources employed while adjusting the data to be better annotated, filtered, or processed aiding the analysis scientific discovery process.
