Performance-Based Numerical Solver Selection in the Lighthouse Framework
Performance-Based Numerical Solver Selection in the Lighthouse Framework
复制标题
Lighthouse 框架中基于性能的数值求解器选择
DOI：
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发表时间：
2016
影响因子：
3.1
通讯作者：
Kanika Sood
中科院分区：
文献类型：
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作者：
E. Jessup;Pate Motter;Boyana Norris;Kanika Sood
科学和工程计算在很大程度上依赖于线性代数来进行大规模数据分析、建模和仿真、机器学习等应用问题。稀疏线性系统的解往往支配着这类应用程序的执行时间，促使人们不断开发高度优化的迭代算法和高性能的并行实现。在灯塔项目中，我们使具有不同背景的应用程序开发人员能够容易地发现并有效地应用最佳可用的数值软件来解决他们的问题，旨在最大化开发人员的工作效率和应用程序的性能。灯塔是一个基于搜索的专家系统，它建立在软件分类的基础上，结合了专家知识、基于机器学习的现有数值软件集合分类以及自动代码生成和优化。本文介绍了稀疏线性系统的PETSC和Trilinos迭代求解器在LighTower框架中的集成。此外..。
Scientific and engineering computing rely heavily on linear algebra for large-scale data analysis, modeling and simulation, machine learning, and other applied problems. Sparse linear system solution often dominates the execution time of such applications, prompting the ongoing development of highly optimized iterative algorithms and high-performance parallel implementations. In the Lighthouse project, we enable application developers with varied backgrounds to readily discover and effectively apply the best available numerical software for their problems, aiming to maximize both developer productivity and application performance. Lighthouse is a search-based expert system built on a software taxonomy that combines expert knowledge, machine learning--based classification of existing numerical software collections, and automated code generation and optimization. In this paper we present the integration of PETSc and Trilinos iterative solvers for sparse linear systems into the Lighthouse framework. In addit...