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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\u00d7 improvement in performance (from about one month to 10\u2009min) on the CeNGEN scRNA-seq dataset when using multiple GPUs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>FastSCODE is publicly available on GitHub at https:\/\/github.com\/cxinsys\/fastscode.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf624","type":"journal-article","created":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T13:03:49Z","timestamp":1762952629000},"source":"Crossref","is-referenced-by-count":1,"title":["FastSCODE: an accelerated SCODE algorithm for inferring gene regulatory networks on manycore processors"],"prefix":"10.1093","volume":"41","author":[{"given":"Rakbin","family":"Sung","sequence":"first","affiliation":[{"name":"Department of Applied Art and 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