Devito：使用符号Python实现通用有限差分DSL
Devito: Towards a generic Finite Difference DSL using Symbolic Python
摘要：
领域特定语言（DSL）已经在各种领域中使用，以简洁的方式表达复杂的科学问题，并为一系列计算架构提供自动化的性能优化。因此，DSL提供了一种强大的机制来加速科学Python计算，超越了传统的向量化和预编译方法，同时允许领域科学家在Python软件生态系统的舒适环境中构建应用程序。在本文中，我们提出了Devito，一个新的有限差分DSL，提供了优化的模板计算，从高层次的问题规范的基础上，符号的Python表达式。我们展示了Devito的符号API和性能优势，传统的Python加速方法之前，突出其在地震反演问题的科学背景下使用。
英文摘要：
Domain specific languages (DSL) have been used in a variety of fields to express complex scientific problems in a concise manner and provide automated performance optimization for a range of computational architectures. As such DSLs provide a powerful mechanism to speed up scientific Python computation that goes beyond traditional vectorization and pre-compilation approaches, while allowing domain scientists to build applications within the comforts of the Python software ecosystem. In this paper we present Devito, a new finite difference DSL that provides optimized stencil computation from high-level problem specifications based on symbolic Python expressions. We demonstrate Devito's symbolic API and performance advantages over traditional Python acceleration methods before highlighting its use in the scientific context of seismic inversion problems.
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