Lab-led effort one of nine DOE-funded data reduction projects
An LLNL-led effort in data compression was one of nine projects recently funded by the DOE for research aimed at shrinking the amount of data needed to advance scientific discovery. Under the project—ComPRESS: Compression and Progressive Retrieval for Exascale Simulations and Sensors—LLNL scientists will seek better understanding of data-compression errors, develop models to increase trust in data compression for science and design a new hardware and software framework to drastically improve performance of compressed data. The team anticipates the improvements will enable higher-fidelity science runs and experiments, particularly on emerging exascale supercomputers capable of at least one quintillion operations per second. Read more at LLNL News.