Performance Modeling of SIMD and MIMD Parallel Computers using Neural Networks

使用神经网络对 SIMD 和 MIMD 并行计算机进行性能建模

• 批准号: 9110812
• 负责人: Sanjay Ranka
• 金额: $ 6.55万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-07-15 至 1993-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9110812&HistoricalAwards=false
• 关键词: Performance; Modeling; SIMD; MIMD; Parallel; Performance; Modeling; SIMD; MIMD; Parallel

The architecture of future teraflop computers is under intense study in universities, government and industrial laboratories. The objective of this research is to model parallel applications on some of the recently available commercial architectures. The goal is to develop performance modeling methods that will scale to future machines. Performance evaluation of parallel computer systems and applications running on these systems is a complex task because of the large number of execution parameters involved. This project investigates the use of robust modeling techniques based on neural networks to analyze the data and predict performance on current as well as future machines. This analysis will also be useful in providing information about the relevant measurements to be stored for useful prediction.

在大学，政府和工业实验室中，未来Teraflop计算机的建筑正在进行深入研究。 这项研究的目的是对一些最近可用的商业体系结构进行平行应用。 目标是开发将扩展到未来机器的性能建模方法。 在这些系统上运行的并行计算机系统和应用程序的性能评估是一项复杂的任务，因为涉及大量执行参数。 该项目研究了基于神经网络的强大建模技术的使用，以分析数据并预测当前和未来机器的性能。 该分析也将有助于提供有关要存储的相关测量的信息，以进行有用的预测。