SUMMARY: SCODE reconstructs gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data using an ordinary differential equation (ODE) model, and has been successfully applied to a wide range of scRNA-seq datasets, including mouse, human, and plant cells. However, its computational performance is limited when processing large datasets due to its sequential execution flow and repeated optimization loops. To overcome this limitation, we have developed FastSCODE, a batch computing version of the SCODE algorithm optimized for acceleration on manycore processors such as GPUs. FastSCODE performs batch computation on multiple gene expression profiles and optimizes the parameters of a linear ODE model using manycore computing. Compared to the original implementation, FastSCODE achieves up to 6000× improvement in performance (from about one month to 10 min) on the CeNGEN scRNA-seq dataset when using multiple GPUs. AVAILABILITY AND IMPLEMENTATION: FastSCODE is publicly available on GitHub at https://github.com/cxinsys/fastscode.