DescriptionMGARD (MultiGrid Adaptive Reduction of Data) is a recently developed technique for multilevel lossy compression and reduction of scientific data. The technique is based on the theory of multigrid methods, which have found widespread application in the computational solution of partial differential equations. MGARD allows the user to specify either a lossiness tolerance level or a size constraint and produces a quasioptimal reduction. Moreover, the data is stored in a hierarchical decomposition well-suited to heterogeneous storage systems.
In this study, we outline algorithms implementing MGARD and perform computational experiments demonstrating its effectiveness. We apply MGARD and state of the art compression tools to two datasets, one generated to prescribed smoothness and another obtained from power grid monitoring micro-phasor measurement units installed at Lawrence Berkeley National Laboratory. The results indicate that in its current preliminary state MGARD offers a competitive alternative to existing state of the art compression tools.