Tanya Amert, Nathan Otterness, Ming Yang, James H. Anderson, F. D. Smith
{"title":"GPU Scheduling on the NVIDIA TX2: Hidden Details Revealed","authors":"Tanya Amert, Nathan Otterness, Ming Yang, James H. Anderson, F. D. Smith","doi":"10.1109/RTSS.2017.00017","DOIUrl":null,"url":null,"abstract":"The push towards fielding autonomous-driving capabilities in vehicles is happening at breakneck speed. Semi-autonomous features are becoming increasingly common, and fully autonomous vehicles are optimistically forecast to be widely available in just a few years. Today, graphics processing units (GPUs) are seen as a key technology in this push towards greater autonomy. However, realizing full autonomy in mass-production vehicles will necessitate the use of stringent certification processes. Currently available GPUs pose challenges in this regard, as they tend to be closed-source “black boxes” that have features that are not publicly disclosed. For certification to be tenable, such features must be documented. This paper reports on such a documentation effort. This effort was directed at the NVIDIA TX2, which is one of the most prominent GPU-enabled platforms marketed today for autonomous systems. In this paper, important aspects of the TX2’s GPU scheduler are revealed as discerned through experimental testing and validation.","PeriodicalId":407932,"journal":{"name":"2017 IEEE Real-Time Systems Symposium (RTSS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"151","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Real-Time Systems Symposium (RTSS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RTSS.2017.00017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 151
Abstract
The push towards fielding autonomous-driving capabilities in vehicles is happening at breakneck speed. Semi-autonomous features are becoming increasingly common, and fully autonomous vehicles are optimistically forecast to be widely available in just a few years. Today, graphics processing units (GPUs) are seen as a key technology in this push towards greater autonomy. However, realizing full autonomy in mass-production vehicles will necessitate the use of stringent certification processes. Currently available GPUs pose challenges in this regard, as they tend to be closed-source “black boxes” that have features that are not publicly disclosed. For certification to be tenable, such features must be documented. This paper reports on such a documentation effort. This effort was directed at the NVIDIA TX2, which is one of the most prominent GPU-enabled platforms marketed today for autonomous systems. In this paper, important aspects of the TX2’s GPU scheduler are revealed as discerned through experimental testing and validation.