大规模计算系统的鲁棒异步牛顿法
A Robust Asynchronous Newton Method for Massive Scale Computing Systems
摘要：
志愿者计算网格以相对较低的服务器运行成本提供超级计算级别的计算能力。在以前的工作中，作者已经表明，它是可以采取传统的迭代进化算法，并执行他们的志愿者计算网格异步执行。异步实现极大地提高了可伸缩性，并减少了收敛到解决方案所需的时间。迭代和异步优化算法实现集群和超级计算机上使用MPI，和BOINC志愿者计算网格已打包在一起的通用分布式优化（FGDO）的框架。本文提出了一种新的扩展FGDO的异步牛顿法（ANM）的局部优化。ANM对异构、故障和不可靠的计算节点具有弹性，并且具有极高的可扩展性。初步结果表明，它可以收敛到局部最优显着快于共轭梯度下降。
英文摘要：
Volunteer computing grids offer super-computing levels of computing power at the relatively low cost of operating a server. In previous work, the authors have shown that it is possible to take traditionally iterative evolutionary algorithms and execute them on volunteer computing grids by performing them asynchronously. The asynchronous implementations dramatically increase scalability and decrease the time taken to converge to a solution. Iterative and asynchronous optimization algorithms implemented using MPI on clusters and supercomputers, and BOINC on volunteer computing grids have been packaged together in a framework for generic distributed optimization (FGDO). This paper presents a new extension to FGDO for an asynchronous Newton method (ANM) for local optimization. ANM is resilient to heterogeneous, faulty and unreliable computing nodes and is extremely scalable. Preliminary results show that it can converge to a local optimum significantly faster than conjugate gradient descent does.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
