Data envelopment analysis (DEA) is an approach studying a high-dimensional data set and measuring the relative position of each data point to the “boundary” of the data set. Its implementation relies on solving linear programming (LP) programs. Although an LP is solvable in polynomial time and is considered an “easy” task, computing DEA problems becomes burdensome when analyzing big data. This talk presents recent developments in DEA computation, and introduces a high-performance, flexible, easy-to-use and low-cost computation tool to the DEA community, including academia and industry.