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CSR: Small: Collaborative Research: Real-Time Computing Infrastructure for Integrated CPU-GPU SoC Platforms

CSR：小型：协作研究：集成 CPU-GPU SoC 平台的实时计算基础设施

基本信息

批准号：
1815891
负责人：
Lui Sha
金额：
$ 22万
依托单位：
University of Illinois at Urbana-Champaign
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815891&HistoricalAwards=false
关键词：
CSR Small Collaborative Research Real

项目摘要

Autonomous cars and drones demand high computational performance to process massive amount of real-time data while also keeping their size, weight, power and cost to an acceptable level. Graphics processing unit (GPU) is specially designed hardware to efficiently process such large data. Therefore, it is increasingly being integrated in new generations of computer chips. Unfortunately, such integrated chips often exhibit unpredictable timing behaviors due to unregulated use of shared hardware resources that can prevent timely execution of critical tasks. This project will create a new real-time computing infrastructure for GPU integrated computer chips to provide predictable timing and high-performance.The project will create new resource management algorithms, task models, real-time synchronization protocols, and schedulability analysis methodologies for GPU integrated computing platforms that significantly improve time predictability and efficiency, and reduce analysis pessimism, compared to the state-of-the art. The project has three research objectives. The first objective is to develop software mechanisms to bound worst-case memory interference to controllable limits with minimal programmer intervention. The second objective is to maximize system resource utilization without sacrificing timing predictability of critical real-time tasks. The third objective is to develop modeling and analysis methodologies for the proposed computing infrastructure.The project has several direct economic and societal impacts. This research will greatly improve temporal predictability and efficiency of GPU integrated computing platforms, which are used for safety-critical cyber-physical systems---particularly in automotive and aviation industries. Considering the market size of automotive industry and the high certification cost in aviation industry, the expected improvements of the project can be translated into multi-billion-dollar saving. The research outcomes will be disseminated via public code repositories and integrated into graduate and undergraduate courses. In particular, autonomous car and drone testbeds will be used to increase student engagement in the classes.Research artifacts, such as source code of modified Linux kernel, user-level library, and tools will be publicly available via open-source repositories at https://github.com/CSL-KU/igpu-rm for the duration of the project and beyond. Research findings will be reported via scientific journals and conferences.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.

自动驾驶汽车和无人机需要高计算性能来处理大量实时数据，同时将其尺寸，重量，功率和成本保持在可接受的水平。图形处理单元（GPU）是专门设计的硬件，可以有效地处理这样的大数据。因此，它越来越多地集成在新一代计算机芯片中。不幸的是，这样的集成芯片经常表现出不可预测的时序行为，由于不受管制的使用共享硬件资源，可以防止关键任务的及时执行。该项目将为GPU集成计算机芯片创建一个新的实时计算基础设施，以提供可预测的时序和高性能。该项目将为GPU集成计算平台创建新的资源管理算法，任务模型，实时同步协议和可扩展性分析方法，显著提高时间可预测性和效率，并减少分析悲观主义，该项目有三个研究目标。第一个目标是开发软件机制，将最坏情况下的内存干扰限制在可控范围内，并尽可能减少程序员的干预。第二个目标是最大限度地提高系统资源利用率，而不牺牲关键的实时任务的时间可预测性。第三个目标是为拟议的计算基础设施开发建模和分析方法。该项目有几个直接的经济和社会影响。这项研究将大大提高GPU集成计算平台的时间可预测性和效率，这些平台用于安全关键的网络物理系统，特别是在汽车和航空行业。考虑到汽车行业的市场规模和航空业的高认证成本，该项目的预期改进可以转化为数十亿美元的节省。研究成果将通过公共代码库传播，并纳入研究生和本科生课程。特别是，自动驾驶汽车和无人机测试平台将用于提高学生在课堂上的参与度。研究工件，如修改后的Linux内核的源代码，用户级库和工具将在项目期间及以后通过https://github.com/CSL-KU/igpu-rm的开源存储库公开提供。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

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Verifiable Obstacle Detection

DOI：
10.1109/issre55969.2022.00017
发表时间：
2022-08
期刊：
2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE)
影响因子：
0
作者：
Ayoosh Bansal;Hunmin Kim;Simon Yu;Bo-Yi Li;N. Hovakimyan;M. Caccamo;L. Sha
通讯作者：
Ayoosh Bansal;Hunmin Kim;Simon Yu;Bo-Yi Li;N. Hovakimyan;M. Caccamo;L. Sha

On Removing Algorithmic Priority Inversion from Mission-critical Machine Inference Pipelines

DOI：
10.1109/rtss49844.2020.00037
发表时间：
2020-12
期刊：
2020 IEEE Real-Time Systems Symposium (RTSS)
影响因子：
0
作者：
Shengzhong Liu;Shuochao Yao;Xinzhe Fu;Rohan Tabish;Simon Yu;Ayoosh Bansal;H. Yun;L. Sha;T. Abdelzaher
通讯作者：
Shengzhong Liu;Shuochao Yao;Xinzhe Fu;Rohan Tabish;Simon Yu;Ayoosh Bansal;H. Yun;L. Sha;T. Abdelzaher

Latency analysis of self-suspending task chains