Adaptive Parallel-in-Time Integration with Dynamic Resource Management

2026-09-21

AI Summary

本文针对时空并行计算中资源动态调整的问题，提出了一种结合动态资源管理的PFASST新方法，实现了计算资源与迭代次数的自适应调整。

Abstract

As computational resources continue to grow, the strong-scaling limitations of spatial parallelism motivate the pursuit of additional concurrency in the temporal dimension, particularly for applications with hard time constraints, such as weather and climate simulations. The Parallel Full Approximation Scheme in Space and Time (PFASST) is a parallel-in-time method based on Spectral Deferred Corrections (SDC). It computes multiple timesteps concurrently by coupling fine- and coarse-grid SDC sweeps using multigrid Full Approximation Scheme (FAS) corrections. However, PFASST's convergence is often problem-dependent, demanding a variable number of parallel timesteps and, hence, computing resources at different times throughout the simulation. Dynamic Resource Management (DRM) provides a remedy for this challenge by enabling the adaptive adjustment of computational resources and algorithmic parameters at runtime. In this work, we present our novel approach to extending PFASST with DRM, which enables (a) dynamic adaptation of computing resources, (b) adaptive selection of the number of PFASST iterations based on local convergence behavior, and (c) coupling of these two adaptations into a single resizing strategy. With this approach, we demonstrate for the first time that optimal configurations can be identified in real time for each application, rather than relying on static allocation. Furthermore, we show that convergence-informed tuning of PFASST improves resource utilization and convergence efficiency.

Problem

• Parallel-in-Time
• Dynamic Resource Management
• PFASST
• Convergence

Innovation

• Dynamic Resource Management
• Parallel-in-Time
• Adaptive Adjustment
• Convergence Efficiency
• Real-time Optimization