Parallel Computing and Complex Systems

并行计算和复杂系统

• 批准号: 9303150
• 负责人: Jacques Cohen
• 金额: $ 100万
• 依托单位: Brandeis University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-09-01 至 1999-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9303150&HistoricalAwards=false
• 关键词: Parallel; Computing; Complex; Systems

9303150 Cohen This award is for the acquisition of a parallel computer and several high performance work stations to support research in the Computer Science Department of Brandeis University. The department is engaged in three major areas of research: Parallelism and Languages, Data Compression, and Artificial Intelligence. The department is part of the Brandeis Center for Complex Systems and the theme of parallelism and the study of large complex systems is common to all three groups. The research topics to be explored by the parallel programming group include the design and analysis of parallel algorithms; and the design and implementation of high level parallel languages which facilitate the rapid construction of programs that can easily be verified to be correct and which can be compiled to run efficiently on MIMD and SIMD machines. Research topics of the data compression group include adaptive vector quantization with variable size vectors, adaptive video compression, issues in coding theory that include error resilient communication, the design of high speed data compression hardware, and context prediction for lexicography. Research topics for the AI group include data extraction from existing databases and text corpora, the role of memory in storing extracted data, and the construction of integrated agents that are data driven and exhibit goal directed behavior.

小行星9303150 该奖项是为了收购一台并行计算机和几个高性能工作站，以支持布兰代斯大学计算机科学系的研究。 该部门从事三个主要研究领域：语言学和语言，数据压缩和人工智能。 该部门是布兰迪斯复杂系统中心的一部分，并行性的主题和大型复杂系统的研究是所有三个组的共同点。 并行编程小组将探讨的研究课题包括并行算法的设计和分析;以及高级并行语言的设计和实现，这些语言有助于程序的快速构建，这些程序可以很容易地被验证为正确的，并且可以被编译为在MIMD和SIMD机器上有效地运行。 数据压缩组的研究课题包括可变大小矢量的自适应矢量量化，自适应视频压缩，编码理论中的问题，包括错误弹性通信，高速数据压缩硬件的设计，以及字典的上下文预测。 人工智能组的研究主题包括从现有数据库和文本语料库中提取数据，存储提取数据的内存的作用，以及数据驱动和表现出目标导向行为的集成代理的构建。