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

提案#：13-37884PI(S)：库克，乔纳森·E·曹，惠平；库克，珍宁·M；庞泰利，Enrico；宋明州研究所：新墨西哥州州立大学标题：磁共振成像/Acq.：不规则并行大数据计算研究工具建议：该项目，获取一种配置为支持数据驱动图计算的计算工具，该计算工具可能实现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--已经有了很长的开发历史，并且经过了高度优化，可以在现代高性能(HPC)仪器上运行。)该仪器拥有320个核心、一个节点和系统架构，是专门为潜在的变革性DDGC研究而设计的。本地节点内存层次结构既包括快速固态二级存储，也包括传统的机械磁盘存储。研究项目探索了开发新的快速固态存储层的方法，不仅作为独立的数据容器，而且在本地节点的内存层次结构中。该系统在其配置中同时支持GPU和FPGA计算。一些研究项目利用了非传统的、但潜在强大的计算机制。特别是，该仪器支持以下项目：-基因组组装和注释计算，-大图数据挖掘，-大知识推理，-基于可扩展图的计算的硬件加速，以及-数据驱动的科学计算监测和分析。每个项目都将使用该仪器的通用架构。图形处理器和现场可编程门阵列的能力将被用来进一步探索和增强提高数据驱动的图形计算性能的可能性。仪器架构的目的是实现研究项目之间的相互促进，这反过来将有助于开发能够高效地执行DDG计算的新方法。广泛影响：由于数据分析在如此多的社会领域中的增长，利用仪器开发的技术应该在整个社会中具有价值(从经济能力到国土安全需求)。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)立即有用。此外，该工具丰富了EPSCoR管辖范围内为少数群体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.