NGS: Scalable I/O Management and Access Optimizations for Scientific Applications for High-Performance Computing

西北大学面向高性能计算科学应用的可扩展I/O管理和访问优化本提案的主要目标是解决大规模存储、I/O性能管理、使用历史信息和访问模式的I/O自动性能优化、数据管理和分析等问题。使用简单的接口进行访问，这些接口允许访问信息流流向较低级别的软件，以利用较高级别的信息。此外，由于这种规模的分析如果手工完成（例如，单独的可视化或离线分析）是不可行的，因此在执行模拟和实验时，在线分析和特征提取的集成是非常重要的。我们的观察是，并行文件系统、运行时系统和数据库管理系统（DBMS）都不能完全解决大规模数据管理问题，因为它们缺乏关于应用程序访问模式的全局信息，而且大多数都不能有效地处理存储层次结构。我们相信，拟议研究的结果将使科学家能够解决计算模拟周期中最重要的瓶颈之一；即在高性能分布式计算环境（如网格）中分析和管理海量数据的瓶颈。

EIA-0103023Alok ChoudharyNorthwestern UniversityScalable I/O Management & Access Optimizations for Scientific Applications for High-Performance ComputingThe main objective of this proposal is to address the problem of large-scale storage, performance management of I/O, automatic performance optimizations of I/O using historical information and access patterns, data management, analysis, and access using simple interfaces which permit flow of access information to lower levels software for exploiting higher level information. Furthermore, since analysis at such a scale is simply not feasible if done manually (e.g., visualization alone or off-line analysis), integration of on-line analysis and feature extraction while simulations and experiments are executing is very important. Our observation is that neither parallel file systems nor runtime systems and database management systems (DBMS) fully-address the large-scale data management problem, as they lack global information about the applications access patterns and most of them are not effective in handling storage hierarchies.We believe that the results from the proposed research will enable scientists to address one of the most important bottlenecks in computational simulation cycles; namely, the bottleneck of analyzing and managing massive data in high-performance distributed computing environment (such as Grid).