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

EIA-0103023 Alok Choudhar西北大学可扩展的I/O管理和访问优化适用于高性能计算的科学应用程序本提案的主要目标是解决大规模存储、I/O的性能管理、使用历史信息和访问模式自动优化I/O的性能、数据管理、分析和使用简单界面进行访问的问题，这些接口允许访问信息流到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动完成(例如，单独进行可视化或离线分析)是根本不可行的，所以在执行模拟和实验时集成在线分析和特征提取是非常重要的。我们观察到，并行文件系统和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访问模式的全局信息，并且大多数DBMS在处理存储层次结构时都不有效。我们相信，所提出的研究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).