Data-flow Reversal and Garbage Collection

IF 2.7 1区 数学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Laurent Hascoët
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引用次数: 0

Abstract

Data-flow reversal is at the heart of source-transformation reverse algorithmic differentiation (reverse ST-AD), arguably the most efficient way to obtain gradients of numerical models. However, when the model implementation language uses garbage collection (GC), for instance in Java or Python, the notion of address that is needed for data-flow reversal disappears. Moreover, GC is asynchronous and does not appear explicitly in the source. This paper presents an extension to the model of reverse ST-AD suitable for a language with GC. The approach is validated on a Java implementation of a simple Navier-Stokes solver. Performance is compared with existing AD tools ADOL-C and Tapenade on an equivalent implementation in C and Fortran.

数据流反转和垃圾收集
数据流反转是源变换反向算法微分(reverse ST-AD)的核心,可以说是获得数值模型梯度的最有效方法。然而,当模型实现语言使用垃圾收集(GC)时,例如在Java或Python中,数据流反转所需的地址概念就消失了。此外,GC是异步的,不会显式地出现在源代码中。本文提出了一种适用于GC语言的逆ST-AD模型的扩展。在一个简单的Navier-Stokes求解器的Java实现上验证了该方法。在C和Fortran的等效实现上,将性能与现有AD工具ADOL-C和Tapenade进行了比较。
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来源期刊
ACM Transactions on Mathematical Software
ACM Transactions on Mathematical Software 工程技术-计算机:软件工程
CiteScore
5.00
自引率
3.70%
发文量
50
审稿时长
>12 weeks
期刊介绍: As a scientific journal, ACM Transactions on Mathematical Software (TOMS) documents the theoretical underpinnings of numeric, symbolic, algebraic, and geometric computing applications. It focuses on analysis and construction of algorithms and programs, and the interaction of programs and architecture. Algorithms documented in TOMS are available as the Collected Algorithms of the ACM at calgo.acm.org.
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