MRI: Development of a GPU-Enabled, Petascale Active Storage Architecture for Data-Intensive Applications in HPC and Cloud Environments

MRI：为 HPC 和云环境中的数据密集型应用程序开发支持 GPU 的 Petascale 主动存储架构

• 批准号: 1229282
• 负责人: Purushotham Bangalore
• 金额: $ 30万
• 依托单位: University of Alabama at Birmingham
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1229282&HistoricalAwards=false
• 关键词: MRI; Development; GPU; Enabled; Petascale

Proposal #: 12-29282PI(s): Skjellum, Anthony Bangalore, Purushotham; Hasan, Ragib; Zhang, ChengcuiInstitution: University of Alabama at BirminghamTitle: MRI/Dev.: A GPU-Enabled, Petascale Active Storage Architecture for Data-Intensive Applications in HPC and Cloud EnvironmentsProject Proposed:This project, developing a 2.4 Petabytes (PB) of raw storage instrument to support a variety of research projects in experimental HPC and cloud storage, aims to both increase local resources for scientific computing and act as a testbed for GPU-enabled reliable storage. The instrument enables an increased virtualization of storage, the concurrent access to storage under fault scenarios (e.g., RAID), and a series of data intensive applications. Lessons learned will be leveraged from the existing system in place, whereby the existing system and the new system will be integrated in a way that supports cloud and disaster recovery modes of operation. The project enables the following studies and research projects: - Studying of effective rates of errors and reliability at highly refined levels and seeking means to identify and manage additional classes of errors (e.g., misdirected writes);- Creating semi-analytical models to allow tunable storage characteristics within a lifetime-reliability-performance cost space; - Running applications from data mining (including bioinformatics as drivers for proving the efficacy of the final system), to achieve new science in these data-intensive domains; and- Conducting computer science research aimed at simplifying use of active storage computation.Broader Impacts: This instrumentation increases the institution?s capacity to conduct cutting-edge research in an inexpensive, fast, practical, reliable petascale storage for data-intensive applications. Significant computational power logically close to that storage enables new science. Student training (including underrepresented groups) will be emphasized. The knowledge dissemination through this effort could be significant.

提案号：12-29282PI(s): Skjellum, Anthony Bangalore, Purushotham；哈桑,Ragib;单位：阿拉巴马大学伯明翰分校职称：MRI/Dev项目建议：该项目开发一个2.4 PB （PB）的原始存储仪器，以支持实验HPC和云存储的各种研究项目，旨在增加科学计算的本地资源，并作为gpu支持的可靠存储的测试平台。该仪器能够增加存储的虚拟化，在故障场景（例如RAID）下并发访问存储，以及一系列数据密集型应用。将从现有系统中吸取经验教训，从而将现有系统和新系统以支持云和灾难恢复操作模式的方式集成在一起。该项目能够进行下列研究和研究项目：-在高度精确的水平上研究误差率和可靠性，并寻求查明和管理其他错误类别的方法（例如，错误的写入）；-创建半分析模型，允许在终身可靠性性能成本空间内可调存储特性；-运行数据挖掘应用程序（包括生物信息学作为证明最终系统有效性的驱动程序），在这些数据密集型领域实现新科学；-进行旨在简化主动存储计算使用的计算机科学研究。更广泛的影响：这种工具增加了机构？为数据密集型应用提供廉价、快速、实用、可靠的千万亿级存储。逻辑上接近这种存储的强大计算能力使新科学得以实现。将强调学生培训（包括代表性不足的群体）。通过这一努力传播的知识可能是重要的。