Accelerating the prediction-based lower triangular transform for data compression using Intel MIC

With the same decorrelation and coding gain capabilities as the Karhunen-Loeve transform, the prediction-based lower triangular transform (PLT) can apply its perfect reconstruction property for lossless compression of ultraspectral sounder data. As the compression process requires computation of the covariance matrix, the LDU decomposition, and the transform kernel and coefficient matrices, it will be beneficial to introduce parallel processing technology in the PLT implementation. In this work, the recent Intel Many Integrated Core (MIC) architecture will be used which can exploit up to 60 cores with 4 hardware threads per core. Both threading and vectorization techniques will be explored for performance improvement. In our experiment, the total processing time of an AIRS granule can have a speedup of similar to 4.6x. With the offload mode, the MIC architecture provides a convenient and efficient tool for parallelization of the PLT compression scheme.