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

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。

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.