Performance Modeling of SIMD and MIMD Parallel Computers using Neural Networks

未来万亿次计算机的体系结构正在紧张的研究中 在大学，政府和工业实验室。 的 这项研究的目的是模拟并行应用程序在一些 最新的商业架构。 目标是 开发可扩展到未来的性能建模方法 机械. 并行计算机系统的性能评价及 在这些系统上运行的应用程序是一项复杂的任务， 涉及大量的执行参数。 这个项目 研究了基于神经网络的鲁棒建模技术的使用， 网络来分析数据并预测当前作为 未来的机器。 这一分析也将有助于 提供关于要存储的相关测量的信息， 有用的预测

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.