MRI: Acquisition of an Instrument for Research in Irregularly Parallel Big Data Computation

提案#:13-37884PI(s): Cook, Jonathan E. Cao, Huiping；珍宁·m·库克；Pontelli,恩里科;单位：新墨西哥州立大学职称：MRI/Acq：不规则并行大数据计算研究仪器项目建议：该项目，获取一种配置为支持数据驱动图计算（DDGC）的计算仪器，该仪器可能能够扩展并改进通常不规则且难以扩展和改进的并行计算。（非常常规的计算，例如流体动力学，已经有了很长的发展历史，并且在现代高性能（HPC）仪器上进行了高度优化。）该仪器具有320个核心和一个节点和系统架构，专为潜在的变革性DDGC研究而设计。本地节点存储器层次结构包括快速固态二级存储器和传统的机械磁盘存储器。研究项目探索利用快速固态存储的新层的方法，不只是作为一个独立的数据容器，而是在本地节点的内存层次结构中。该系统在配置中同时支持GPU和FPGA计算。一些研究项目利用非传统的，但潜在的强大的计算机制。该仪器特别支持以下项目：基因组组装和注释计算，大图上的数据挖掘，大知识推理，可扩展图计算的硬件加速，科学计算的数据驱动监测和分析。这些项目中的每一个都将使用该仪器的通用架构。GPU和FPGA功能将用于进一步探索和增强提高数据驱动图计算性能的可能性。该仪器架构旨在实现研究项目之间的相互作用，从而有助于开发能够有效执行DDG计算的新方法。更广泛的影响：由于数据分析在社会许多领域的发展，利用该工具开发的技术应该对整个社会（从经济能力到国土安全需求）都有价值。可以在许多大数据计算中改进DDG计算的技术（从改进的算法到新演示的硬件方法）是非常有用的。此外，该仪器丰富了EPSCoR管辖范围内为少数民族服务的机构的研究和教育活动。该项目为学生和教授提供了参与新颖计算机研究的机会。

Proposal #: 13-37884PI(s): Cook, Jonathan E. Cao, Huiping; Cook, Jeanine M.; Pontelli, Enrico; Song, MingzhouInstitution: New Mexico State University Title: MRI/Acq.: Instrument for Research in Irregularly Parallel Big Data ComputationProject Proposed:This project, acquiring a computational instrument configured to support data-driven graph computations (DDGC) that might enable-to-scale and improve parallel computation that is generally irregular and hard to scale and improve. (Very regular computations--for example, fluid dynamics--already have a long history of development and are highly optimized to run on modern high performance (HPC) instruments.) The instrument, with 320 cores and a node and system architecture, is designed specifically with potentially transformative DDGC research in mind. The local node memory hierarchy includes both fast solid state secondary storage and traditional mechanical disk storage. Research projects explore ways to exploit the new layer of fast solid state storage, not just as a standalone data container, but within the memory hierarchy of the local node. The system has in its configuration both GPU and FPGA computing support. Some of the research projects exploit non-traditional, yet potentially powerful computation mechanisms. In particular, the instrument supports the following projects:- Genome Assembly and Annotation Computations,- Data Mining over Large Graphs,- Reasoning with Big Knowledge,- Hardware Acceleration for Scalable Graph-Based Computations, and- Data Driven Monitoring and Analysis of Scientific Computations.Each of these projects will make use of the general architecture of the instrument. GPU and FPGA capabilities will be used to further explore and enhance the possibilities to improve performance of data-driven graph computations. The instrument architecture is intended to enable cross-fertilization between the research projects that will, in turn, contribute to the development of new approaches that can perform DDG computations efficiently.Broader Impacts: Due to the growth of data analytics in so many areas of society, the techniques developed utilizing the instrument should be valuable across society (from economic capacity to homeland security needs). Techniques that can improve the DDG computations within many of the big data computations (from improved algorithms to newly demonstrated hardware approaches) are immediately useful. Moreover, the instrumentation enriches the research and educational activities at a minority-serving institution within an EPSCoR jurisdiction. The project offers opportunities for students and professors to participate in novel computer research.