茱莉安娜工具的评估：茱莉亚CUDA的翻译。将代码放入KernelAbstraction.jl中

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-04-04 DOI:10.1016/j.future.2025.107813

Enrique de la Calle, Carlos García

{"title":"茱莉安娜工具的评估：茱莉亚CUDA的翻译。将代码放入KernelAbstraction.jl中","authors":"Enrique de la Calle, Carlos García","doi":"10.1016/j.future.2025.107813","DOIUrl":null,"url":null,"abstract":"<div><div>Julia is a high-level language that supports the execution of parallel code through various packages. CUDA.jl is widely used for developing GPU-accelerated code in Julia and is integrated into many libraries and programs. In this paper, we present Juliana, a novel tool that automatically translates Julia code utilizing the CUDA.jl package into an abstract, multi-backend representation powered by the KernelAbstractions.jl package. To evaluate the tool’s viability and performance, we analyzed four Julia projects: Rodinia, miniBUDE, BabelStream, and Oceananigans.jl. The performance overhead of this approach was found to be relatively low (under 7% for the Rodinia suite), with performance portability metrics showing results nearly identical to the native implementations. By running the same code across multiple KernelAbstractions’ backends, we successfully executed these translated projects on GPUs from vendors such as NVIDIA, Intel, AMD, and Apple. This ensured compatibility across these platforms and enabled first-time execution on some devices.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"171 ","pages":"Article 107813"},"PeriodicalIF":6.2000,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluation of Juliana Tool: A translator for Julia’s CUDA.jl code into KernelAbstraction.jl\",\"authors\":\"Enrique de la Calle, Carlos García\",\"doi\":\"10.1016/j.future.2025.107813\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Julia is a high-level language that supports the execution of parallel code through various packages. CUDA.jl is widely used for developing GPU-accelerated code in Julia and is integrated into many libraries and programs. In this paper, we present Juliana, a novel tool that automatically translates Julia code utilizing the CUDA.jl package into an abstract, multi-backend representation powered by the KernelAbstractions.jl package. To evaluate the tool’s viability and performance, we analyzed four Julia projects: Rodinia, miniBUDE, BabelStream, and Oceananigans.jl. The performance overhead of this approach was found to be relatively low (under 7% for the Rodinia suite), with performance portability metrics showing results nearly identical to the native implementations. By running the same code across multiple KernelAbstractions’ backends, we successfully executed these translated projects on GPUs from vendors such as NVIDIA, Intel, AMD, and Apple. This ensured compatibility across these platforms and enabled first-time execution on some devices.</div></div>\",\"PeriodicalId\":55132,\"journal\":{\"name\":\"Future Generation Computer Systems-The International Journal of Escience\",\"volume\":\"171 \",\"pages\":\"Article 107813\"},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2025-04-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Future Generation Computer Systems-The International Journal of Escience\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167739X25001086\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future Generation Computer Systems-The International Journal of Escience","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167739X25001086","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}

引用次数: 0

摘要

Julia是一种高级语言，支持通过各种包执行并行代码。CUDA。jl广泛用于在Julia中开发gpu加速代码，并集成到许多库和程序中。在本文中，我们介绍了Juliana，一个利用CUDA自动翻译Julia代码的新工具。jl包转化为一个抽象的、由内核抽象支持的多后端表示。杰包。为了评估该工具的可行性和性能，我们分析了四个Julia项目：Rodinia、miniBUDE、BabelStream和Oceananigans.jl。这种方法的性能开销相对较低（在Rodinia套件中低于7%），性能可移植性指标显示的结果与本机实现几乎相同。通过在多个KernelAbstractions的后端运行相同的代码，我们成功地在NVIDIA、Intel、AMD和Apple等厂商的gpu上执行了这些翻译后的项目。这确保了这些平台之间的兼容性，并支持在某些设备上的首次执行。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Evaluation of Juliana Tool: A translator for Julia’s CUDA.jl code into KernelAbstraction.jl

Julia is a high-level language that supports the execution of parallel code through various packages. CUDA.jl is widely used for developing GPU-accelerated code in Julia and is integrated into many libraries and programs. In this paper, we present Juliana, a novel tool that automatically translates Julia code utilizing the CUDA.jl package into an abstract, multi-backend representation powered by the KernelAbstractions.jl package. To evaluate the tool’s viability and performance, we analyzed four Julia projects: Rodinia, miniBUDE, BabelStream, and Oceananigans.jl. The performance overhead of this approach was found to be relatively low (under 7% for the Rodinia suite), with performance portability metrics showing results nearly identical to the native implementations. By running the same code across multiple KernelAbstractions’ backends, we successfully executed these translated projects on GPUs from vendors such as NVIDIA, Intel, AMD, and Apple. This ensured compatibility across these platforms and enabled first-time execution on some devices.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Future Generation Computer Systems-The International Journal of Escience 工程技术-计算机：理论方法

CiteScore

19.90

自引率

2.70%

发文量

376

审稿时长

10.6 months

期刊介绍： Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.