Abstract: To address the issues of long computation time and difficulty in parallel acceleration when using the original Kriging algorithm for interpolation of carbon satellite data at a large regional scale, the Kriging algorithm and its key parts are restructured and optimized. The whole interpolation process is broken up into several fine-grained operations and then organized into a distributed DAG workflow based on dependency relationship and data features. Finally, a distributed computing framework based on the double-tier scheduling structure is designed to accelerate the interpolation workflow on the distributed computing cluster. Experiments show that methods and framework described above can perform Kriging interpolation of different regional scales with high efficiency, and the efficiency advantages are more significantly than Spark at the large regional scale.

Key words: carbon satellite, distributed interpolation, Kriging algorithm acceleration, workflow scheduling