题目：An Adaptive Mixed Precision and Dynamically Scaled Preconditioned Conjugate Gradient Algorithm

报告人：郭奕晨 博士（弗吉尼亚理工大学）

地点：致远楼101室

时间：2015年11月15日 10:00-11:00

摘要：Solving large-scale linear systems is often the most computationally demanding component of numerical simulations. With modern hardware accelerators offering significantly higher arithmetic throughput at reduced precision, and lower-precision storage cutting both memory usage and data transfer costs, there is growing interest in algorithms that can use these advantages to accelerate sparse linear solvers.

In this talk, we present an Adaptive Mixed-Precision and Dynamically Scaled Preconditioned Conjugate Gradient algorithm (AMP-PCG) for solving symmetric positive definite systems. AMP-PCG dynamically adjusts the precision used for storing vectors and performing operations throughout the iteration process, exploiting low-precision arithmetic when appropriate, while maintaining a convergence rate comparable to that of double-precision PCG. We demonstrate the effectiveness of this strategy through numerical experiments that highlight both the robustness and performance gains of AMP-PCG compared to fully double-precision PCG.

线上报告：腾讯会议 609-707-510

线下讨论：致远楼 101 室

欢迎各位参加！