The partitioned predictive vector quantization (PPVQ) algorithm is known for its high compression ratio for lossless compression of the ultraspectral sounder data with high spatial and spectral resolutions. With the advent of the multicore technologies, parallelization of several parts of the algorithm has been explored in previous work using a compute unified device architecture (CUDA) aided environment on the Graphics Processing Unit (GPU). Recently the Intel Many Integrated Core (MIC) architecture on a coprocessor is introduced which shows promise in handling more divergent workloads as needed in PPVQ. Therefore we will explore the parallel performance of the MIC-aided implementation. With parallelization of the two most time-consuming modules of linear prediction and vector quantization in PPVQ, the total processing time of an AIRS granule can be compressed in less than 7.5 seconds which is equivalent to a speedup of ~8.8x. The use of MIC for PPVQ compression is thus promising as a low-cost and effective compression solution for ultraspectral sounder data for ground rebroadcast use.
Proc. SPIE 9247, High-Performance Computing in Remote Sensing IV, 92470E