Laboratory for Parallel Processing

基础设施奖用于购置设备 开发一个并行处理的实验室。 主要 采集是64处理器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，以及 数据库管理系统 新体系结构并行计算机的出现， 与有效利用这些资源有关的研究领域 电脑MIMD计算机允许个人 处理器对独立的数据流进行操作。 发现 有效地使用这种类型的架构的算法是一个 跨学科的主要研究问题。 的 佛罗里达大学的并行处理实验室 调查了几个研究课题，从实验性的 算法研究到计算机视觉应用。 在 具体如下： 1.软件工程组正在开发一个并行的 编程语言环境中使用的 开发并行程序， 基于知识的开发和维护环境。 2.并行算法组正在集中精力 通过实验确定瓶颈， 处理器通信和输入/输出设备。 实验结果将与 性能的理论模型。 3.网络和模拟组正在使用parrrket 计算机既作为负载的实验测试平台 平衡和其他性能增强算法 并作为计算资源来执行复杂的 模拟 4.计算机视觉小组正在开发算法， 利用并行性。 有一个自然的匹配之间 许多视觉应用和MIMD架构。 5.数据库管理组正在设计算法 实现面向对象的语义数据库模型 在MIMD架构的计算机上。 //

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. //