Laboratory for Parallel Processing

基础设施合同是为开发并行处理实验室而购买的设备。主要收购是64处理器的MIMD（多指令，多数据）并行计算机和工作站。研究将在软件工程、并行算法和数据结构、网络和仿真、计算机视觉和数据库管理系统方面进行。新架构并行计算机的出现为有效使用这些计算机开辟了新的研究领域。MIMD计算机的工作原理是允许单个处理器在独立的数据流上运行。有效地使用这种类型的架构的算法的发现是一个跨多个学科的主要研究问题。佛罗里达大学的并行处理实验室正在研究几个研究课题，从实验算法研究到计算机视觉应用。特别是：1；软件工程组正在开发一个并行编程语言环境，用于与基于知识的开发和维护环境一起开发并行程序。2. 并行算法组专注于通过实验确定处理器通信和输入/输出设备的瓶颈。实验结果将与理论模型的性能进行比较。3. 网络和仿真组正在使用并行计算机作为负载平衡和其他性能增强算法的实验测试平台，并作为执行复杂模拟的计算资源。4. 计算机视觉小组正在开发利用并行性的算法。在许多视觉应用程序和MIMD体系结构之间存在着一种自然的匹配。5. 数据库管理组正在设计算法，以便在MIMD架构计算机上实现面向对象的语义数据库模型。//

The infrastructure award is for the acquisition of equipment to develop a laboratory for parallel processing. The major acquisition is a 64 processor MIMD (Multiple Instruction, Multiple Data) parallel computer and workstations. Research will be performed in software engineering, parallel algorithms and data structures, networks and simulation, computer vision, and database management systems. The advent of new architecture parallel computers has opened new areas of research related to the effective use of these computers. The MIMD computers work by allowing individual processors to operate on independent data streams. The discovery of algorithms to effectively use this type of architecture is a major research question crossing several disciplines. The parallel processing laboratory at the University of Florida is investigating several research topics ranging from experimental algorithm studies to computer vision applications. In particular: 1. The software engineering group is developing a parallel programming language environment to be used in the development of parallel programs in conjunction with a knowledge based development and maintenance environment. 2. The parallel algorithms group is concentrating on experimentally determining bottlenecks with both processor communication and input/output devices. The experimental results will be compared with theoretical models of performance. 3. The network and simulation group is using the parrallel computer as both an experimental testbed for load balancing and other performance enhancing algorithms and as a computational resource to perform complex simulations. 4. The computer vision group is developing algorithms to utilize parallelism. There is a natural match between many vision applications and the MIMD architecture. 5. The database management group is designing algorithms to implement an object oriented semantic database model on a MIMD architecture computer. //