Sparse modeling is widely used in image processing, signal processing, and machine learning recently. Thanks to the research and progress in statistical mathematics along with the evolution of computational power, the technique is applicable to the radio imaging for the data obtained with the ALMA (Atacama Large Millimeter-submillimeter Array). We’ve developed a new imaging tool based on the sparse modeling approach and it was experimentally implemented on the Common Astronomy Software Application (CASA) which is an official reduction software for the ALMA data. However, if the image size is large, e.g., 1K $\times$ 1K pixels, the data processing time gets longer, say several to ten hours, even with the latest mid-range server computers. Here we present a possible measure to greatly reduce the processing time.