Collaborative Research: CNS Core: SMALL: DrGPU: Optimizing GPU Programs via Novel Profiling Techniques

合作研究：CNS Core：SMALL：DrGPU：通过新颖的分析技术优化 GPU 程序

• 批准号: 2125813
• 负责人: Jiajia Li
• 金额: $ 24.95万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125813&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; SMALL

Graphics Processing Units (GPUs) have become common in modern computing systems. However, it remains challenging to efficiently program GPUs due to the complexity of architectures, programming models, and algorithm designs. This project aims to develop a framework to identify performance inefficiencies in GPU applications and provide intuitive guidance for code optimization. The intellectual merits include three novel analysis techniques: (a) multi-scale analysis to understand inefficiencies inside individual GPU kernels, (b) coordinated analysis to measure inefficient data movement between devices, and (c) differential analysis to view inefficiencies across different runs with different execution configurations. The combination of these three analyses will give a complete performance picture of GPU applications and help developers obtain the bare-metal performance in modern and emerging GPUs.This project will bridge the knowledge gap between GPU hardware architectures and software developers. The success of this project will enable significant enhancement of computing efficiency for production code, and hence help maintain a sustained advancement of various domains, e.g., data analytics, high-performance computing, and artificial intelligence. The proposed framework will draw broad interest from industry, research institutes, and the Department of Energy national laboratories for improving their code bases and increasing system throughputs. Moreover, the outcome of this project will be integrated into curriculum planning and educational activities, from which students can directly benefit.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

图形处理单元（GPU）在现代计算系统中已经变得普遍。然而，由于架构、编程模型和算法设计的复杂性，高效地对GPU进行编程仍然具有挑战性。该项目旨在开发一个框架，以识别GPU应用程序中的性能低下，并为代码优化提供直观的指导。智能优势包括三种新颖的分析技术：（a）多尺度分析，以了解单个GPU内核内部的效率低下，（B）协调分析，以衡量设备之间的低效数据移动，以及（c）差异分析，以查看不同运行与不同执行配置之间的效率低下。这三个分析的结合将给出GPU应用程序的完整性能图，并帮助开发人员获得现代和新兴GPU的裸金属性能。该项目将弥合GPU硬件架构和软件开发人员之间的知识差距。这个项目的成功将大大提高生产代码的计算效率，从而有助于保持各个领域的持续发展，例如，数据分析、高性能计算和人工智能。拟议的框架将引起工业界、研究机构和能源部国家实验室的广泛兴趣，以改进其代码库并提高系统吞吐量。此外，该项目的成果将被整合到课程规划和教育活动中，学生可以直接从中受益。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

ValueExpert: exploring value patterns in GPU-accelerated applications

• DOI: 10.1145/3503222.3507708
• 发表时间: 2022-02
• 期刊: Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems
• 作者: K. Zhou;Yueming Hao;J. Mellor-Crummey;Xiaozhu Meng;Xu Liu

DrGPU: A Top-Down Profiler for GPU Applications

• DOI: 10.1145/3578244.3583736
• 发表时间: 2023-04
• 期刊: Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering
• 作者: Yueming Hao;Nikhil Jain;R. Van der Wijngaart;N. Saxena;Yuanbo Fan;Xu Liu