When it comes to working with big data, one of the challenges is how to manage and store massive amounts of information. Argonne’s Franck Cappello, Sheng Di and Robert Underwood will discuss improving data compression—a method that reduces the size of data without losing important details—at a full-day tutorial. They also will present a technical paper on the use of lossy compressors on GPUs and another paper with intern Yafan Huang of the University of Iowa that has earned a nomination for the prestigious Best Student Paper Award at the conference.