摘要：With the convergence of high-performance computing(HPC),big data and artificial intelligence(AI),the HPC community is pushing for"triple use"systems to expedite scientific ***,supporting these converged applications on HPC systems presents formidable challenges in terms of storage and data management due to the explosive growth of scientific data and the fundamental differences in I/O characteristics among HPC,big data and AI *** this paper,we discuss the driving force behind the converging trend,highlight three data management challenges,and summarize our efforts in addressing these data management challenges on a typical HPC system at the parallel file system,data management middleware,and user application *** HPC systems are approaching the border of exascale computing,this paper sheds light on how to enable application-driven data management as a preliminary step toward the deep convergence of exascale computing ecosystems,big data,and AI.