Scientific applications at exascale generate and analyze massive amounts of data.A critical requirement of these applications is the capability to access and manage this data efficiently on exascale systems.Parallel I...Scientific applications at exascale generate and analyze massive amounts of data.A critical requirement of these applications is the capability to access and manage this data efficiently on exascale systems.Parallel I/O,the key technology enables moving data between compute nodes and storage,faces monumental challenges from new applications,memory,and storage architectures considered in the designs of exascale systems.As the storage hierarchy is expanding to include node-local persistent memory,burst buffers,etc.,as well as disk-based storage,data movement among these layers must be efficient.Parallel I/O libraries of the future should be capable of handling file sizes of many terabytes and beyond.In this paper,we describe new capabilities we have developed in Hierarchical Data Format version 5(HDF5),the most popular parallel I/O library for scientific applications.HDF5 is one of the most used libraries at the leadership computing facilities for performing parallel I/O on existing HPC systems.The state-of-the-art features we describe include:Virtual Object Layer(VOL),Data Elevator,asynchronous I/O,full-featured single-writer and multiple-reader(Full SWMR),and parallel querying.In this paper,we introduce these features,their implementations,and the performance and feature benefits to applications and other libraries.展开更多
基金This research was supported by the Exascale Computing Project under Grant No.17-SC-20-SCa joint project of the U.S.Department of Energy's Office of Science and National Nuclear Security Administration,responsible for delivering a capable exascale ecosystem,including software,applications,and hardware technology,to support the nation's exascale computing imperative+4 种基金This work is also supported by the Director,Office of Science,Office of Advanced Scientific Computing Research,of the U.S.Department of Energy under Contract Nos.DE-AC02-05CH11231 and DE-AC02-06CH11357This research was funded in part by the Argonne Leadership Computing Facilitywhich is a DOE Office of Science User Facility supported under Contract No.DE-AC02-06CH11357This research used resources of the National Energy Research Scientific Computing Centerwhich is DOE Office of Science User Facilities supported by the Office of Science of the U.S.Department of Energy under Contract No.DE-AC02-05CH11231.
文摘Scientific applications at exascale generate and analyze massive amounts of data.A critical requirement of these applications is the capability to access and manage this data efficiently on exascale systems.Parallel I/O,the key technology enables moving data between compute nodes and storage,faces monumental challenges from new applications,memory,and storage architectures considered in the designs of exascale systems.As the storage hierarchy is expanding to include node-local persistent memory,burst buffers,etc.,as well as disk-based storage,data movement among these layers must be efficient.Parallel I/O libraries of the future should be capable of handling file sizes of many terabytes and beyond.In this paper,we describe new capabilities we have developed in Hierarchical Data Format version 5(HDF5),the most popular parallel I/O library for scientific applications.HDF5 is one of the most used libraries at the leadership computing facilities for performing parallel I/O on existing HPC systems.The state-of-the-art features we describe include:Virtual Object Layer(VOL),Data Elevator,asynchronous I/O,full-featured single-writer and multiple-reader(Full SWMR),and parallel querying.In this paper,we introduce these features,their implementations,and the performance and feature benefits to applications and other libraries.
