LLNL’s HPC and data science capabilities play a significant role in international science research and innovation, and Lab researchers have won 10 R&D 100 Awards in the Software–Services category in the past decade. The latest issue of Science & Technology Review features several award-winning projects, including ZFP and CANDLE: (1) ZFP introduces a new method of compressing large data sets while maintaining high-speed, on-demand access to the compressed data for both reading and writing applications—a capability not found in any other compressor. Researchers can continue to work with the data in real time while it remains compressed, whether they are analyzing it or producing visualizations. ZFP is downloaded more than 1.5 million times per year by users from across the DOE and other government and nongovernment agencies, and its scientific applications include geographic information systems, climate science, seismology, and tornado simulations, among others. (2) An early adopter of using ML for scientific applications, the Cancer Distributed Learning Environment (CANDLE) provides machine learning capabilities for applications related to cancer research. In particular, CANDLE enables capabilities for extracting key information and finding relationships within large, disconnected data sets to help solve cancer-specific drug challenges. CANDLE is a collaboration among Lawrence Livermore, Los Alamos, Oak Ridge, and Argonne national laboratories; the Frederick National Laboratory for Cancer Research; the National Institutes of Health; and the National Cancer Institute. Read more on the S&TR website.
Anashe Bandari/LLNL
