Estimating taxonomic content constitutes a key problem in metagenomic sequencing data analysis.However,extracting such content from high-throughput data of next-generation sequencing is very time-consuming with the currently available software.Here,we present CloudLCA,a parallel LCA algorithm that significantly improves the efficiency of determining taxonomic composition in metagenomic data analysis.Results show that CloudLCA (1) has a running time nearly linear with the increase of dataset magnitude,(2) displays linear speedup as the number of processors grows,especially for large datasets,and (3) reaches a speed of nearly 215 million reads each minute on a cluster with ten thin nodes.In comparison with MEGAN,a well-known metagenome analyzer,the speed of CloudLCA is up to 5 more times faster,and its peak memory usage is approximately 18.5％ that of MEGAN,running on a fat node.CloudLCA can be run on one multiprocessor node or a cluster.It is expected to be part of MEGAN to accelerate analyzing reads,with the same output generated as MEGAN,which can be import into MEGAN in a direct way to finish the following analysis.Moreover,CIoudLCA is a universal solution for finding the lowest common ancestor,and it can be applied in other fields requiring an LCA algorithm.