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The increasing size of solar datasets demands highly efficient and robustanalysis methods. This paper presents an approach that can increase thecomputational efficiency of differential emission measure (DEM) inversions byan order of magnitude or higher, with the efficiency factor increasing with thesize of the input dataset. The method, named the Gridded Solar IterativeTemperature Emission Solver (Grid-SITES) is based on grouping pixels accordingto the similarity of their intensities in multiple channels, and solving forone DEM per group. This is shown to be a valid approach, given a sufficientlyhigh number of grid bins for each channel. The increase in uncertainty arisingfrom the quantisation of the input data is small compared to the generalmeasurement and calibration uncertainties. In this paper, we use the SolarIterative Temperature Emission Solver (SITES) as the core method for the DEMinversion, although Grid-SITES provides a general framework which may be usedwith any DEM inversion method, or indeed any large multi-dimensional datainversion problem. The method is particularly efficient for processing largerimages, offering a factor of 30 increase in speed for a 10 megapixel image. Fora time series of observations, the gridded results can be passed sequentiallyto each new image, with new populated bins added as required. This processleads to increasing efficiency with each new image, with potential for a$\approx$100 increase in efficiency dependent on the size of the images.

GRID-SITES: Gridded Solar Iterative Temperature Emission Solver for Fast DEM Inversion