ITR: Distributed Hybrid Optimization Techniques with Applications to Proteomics and Genomics

许多有实际意义的计算问题都可以从搜索的角度提出。例子包括在科学、工程和商业的许多领域中自然出现的离散优化问题。健壮的网络基础设施的发展，加上价格合理的计算设备的出现，有助于将注意力集中在使用多个处理器并行操作以提高搜索性能的技术上。我们的研究重点是可扩展、容错、分布式搜索技术的设计、开发、实现和评估及其应用，通常与其他优化技术相结合，构建混合优化系统。这里描述的大部分工作依赖于我们自己设计的一种独特的潜在并行化范式，称为唠叨。与更传统的问题划分技术相比，唠叨会对一个问题（或问题的一部分）进行多次重构，以相互对抗，从而提高性能。完善和完善对唠叨的理解，包括研究唠叨与分割相结合的适应性策略；改进和完善我们的分布式搜索基础设施，包括，例如，使用加密协议来支持跨管理边界的认证和新应用程序的分发；并继续我们的跨学科合作努力，将分布式搜索与其他优化技术应用于科学计算问题。

Many computational problems of practical interest can be posed in terms of search. Examples include discrete optimization problem that arise naturally in many areas of science, engineering and business. The development of a robust network infrastructure coupled with the advent of reasonably-priced computing equipment has helped focus attention on techniques that use multiple processors operating in parallel to improve search performance. Our research focuses on the design, development, implementation and evaluation of scalable, fault-tolerant, distributed search technique and their application, generally in concert with other optimization techniques, to construct hybrid optimization systems. Much of the work described here relies on a unique underlying parallelization paradigm of our own design called nagging. It contrast to more traditional problem partitioning techniques, nagging plays mutiple reformations of a problem --- or portions of a problem --- against each other to improve performance. We will improve and refine our understanding of nagging, including research on adaptive strategies for combining nagging and partitioning strategies; to improve and refine our distributed search infrastructure, including, for example, the use of cryptographic protocols to support authentication and distribution of new applications across management boundaries; and to continue our interdisciplinary collaborative efforts to apply distributed search concert with other optimization techniques to scientific computing problems.