Step-based checkpointing with high-level algorithmic differentiation

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science Pub Date : 2024-08-06 DOI:10.1016/j.jocs.2024.102405

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引用次数: 0

Abstract

Automated code generation allows for a separation between the development of a model, expressed via a domain specific language, and lower level implementation details. Algorithmic differentiation can be applied symbolically at the level of the domain specific language, and the code generator reused to implement code required for an adjoint calculation. However the adjoint calculations are complicated by the well-known problem of storing or recomputing the forward data required by the adjoint, and different checkpointing strategies have been developed to tackle this problem. This article considers the combination of high-level algorithmic differentiation with step-based checkpointing schedules, with the primary application being for solvers of time-dependent partial differential equations. The focus is on algorithmic differentiation using a dynamically constructed record of forward operations, where the precise structure of the original forward calculation is unknown ahead-of-time. In addition, high-level approaches provide a simplified view of the model itself. This allows data required to restart and advance the forward, and data required to advance the adjoint, to be identified. The difference between the two types of data is here leveraged to implement checkpointing strategies with improved performance.

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基于步骤的检查点与高级算法区分

通过自动代码生成，可以将通过特定领域语言表达的模型开发与较低级别的实施细节分离开来。算法微分可以在特定领域语言的层次上以符号方式应用，代码生成器可以重复使用，以实现邻接计算所需的代码。然而，众所周知的问题是，要存储或重新计算旁证计算所需的前向数据，这使得旁证计算变得复杂，为了解决这个问题，人们开发了不同的检查点策略。本文考虑将高级算法微分与基于步长的检查点计划相结合，主要应用于时变偏微分方程的求解器。重点在于使用动态构建的前向运算记录进行算法微分，在这种情况下，原始前向计算的精确结构是提前未知的。此外，高层方法提供了模型本身的简化视图。这样就可以确定重启和推进正演所需的数据，以及推进副运算所需的数据。利用这两类数据之间的差异，可以实施性能更高的检查点策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

5.50

自引率

3.00%

发文量

227

审稿时长

41 days

期刊介绍： Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).