论文标题
银杏 - 一个用于平台可移植性的数学库
Ginkgo -- A Math Library designed for Platform Portability
论文作者
论文摘要
软件可持续性的第一个关联可能是存在连续集成（CI）框架的存在。存在由单位测试，集成测试和端到端测试组成的测试框架的存在；以及软件文档的存在。但是，当询问科学软件产品的常见死亡阳光时，通常缺乏平台和性能可移植性。在此背景下，我们设计了Ginkgo库，主要关注平台可移植性以及不仅可以端口到新硬件体系结构的能力，还可以实现良好的性能。在本文中，我们介绍了Ginkgo库设计，从根本上将算法与形成不同硬件执行者的硬件特定内核分开，并在添加NVIDIA，AMD和Intel GPU的执行后端时报告我们的经验。我们还评论性能可移植性的不同级别以及在不同硬件后端的性能。
The first associations to software sustainability might be the existence of a continuous integration (CI) framework; the existence of a testing framework composed of unit tests, integration tests, and end-to-end tests; and also the existence of software documentation. However, when asking what is a common deathblow for a scientific software product, it is often the lack of platform and performance portability. Against this background, we designed the Ginkgo library with the primary focus on platform portability and the ability to not only port to new hardware architectures, but also achieve good performance. In this paper we present the Ginkgo library design, radically separating algorithms from hardware-specific kernels forming the distinct hardware executors, and report our experience when adding execution backends for NVIDIA, AMD, and Intel GPUs. We also comment on the different levels of performance portability, and the performance we achieved on the distinct hardware backends.
