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.

提案编号：12- 29282 PI：Skjellum，Anthony 班加罗尔，Purushotham; Hasan，Ragib; Zhang，Chengcui机构：位于伯明翰的亚拉巴马大学标题： MRI/器械：一个支持GPU的Petascale主动存储架构，用于HPC和云环境中的数据密集型应用程序项目建议：该项目开发了一个2.4 PB的原始存储工具，以支持实验HPC和云存储中的各种研究项目，旨在增加科学计算的本地资源，并作为支持GPU的可靠存储的测试平台。该仪器能够增强存储的虚拟化，在故障情况下对存储的并发访问（例如，RAID），以及一系列数据密集型应用。将利用现有系统的经验教训，以支持云和灾后恢复运作模式的方式整合现有系统和新系统。该项目使以下研究和研究项目成为可能：-在高度精确的水平上研究有效的错误率和可靠性，并寻求识别和管理其他错误类别的方法（例如，错误定向的写入）;- 创建半分析模型，以允许在寿命-可靠性-性能成本空间内可调存储特性;（包括生物信息学作为证明最终系统有效性的驱动力），在这些数据密集型领域实现新的科学;以及-开展计算机科学研究，旨在简化主动存储计算的使用。这种仪器增加了机构？的能力进行前沿研究，在一个廉价，快速，实用，可靠的千万亿次存储数据密集型应用程序。在逻辑上接近这种存储的重要计算能力使新的科学成为可能。将强调学生培训（包括代表性不足的群体）。通过这一努力传播知识可能意义重大。