S. Carlo, Giulio Gambardella, Marco Indaco, Ippazio Martella, P. Prinetto, Daniele Rolfo, Pascal Trotta
{"title":"基于软件的CUDA费米图形处理器自测试","authors":"S. Carlo, Giulio Gambardella, Marco Indaco, Ippazio Martella, P. Prinetto, Daniele Rolfo, Pascal Trotta","doi":"10.1109/ETS.2013.6569353","DOIUrl":null,"url":null,"abstract":"Nowadays, Graphical Processing Units (GPUs) have become increasingly popular due to their high computational power and low prices. This makes them particularly suitable for high-performance computing applications, like data elaboration and financial computation. In these fields, high efficient test methodologies are mandatory. One of the most effective ways to detect and localize hardware faults in GPUs is a Software-Based-Self-Test methodology (SBST). In this paper a fully comprehensive SBST and fault localization methodology for GPUs is presented. This novel approach exploits different custom test strategies for each component inside the GPU architecture. Such strategies guarantee both permanent fault detection and accurate fault localization.","PeriodicalId":118063,"journal":{"name":"2013 18th IEEE European Test Symposium (ETS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"36","resultStr":"{\"title\":\"A software-based self test of CUDA Fermi GPUs\",\"authors\":\"S. Carlo, Giulio Gambardella, Marco Indaco, Ippazio Martella, P. Prinetto, Daniele Rolfo, Pascal Trotta\",\"doi\":\"10.1109/ETS.2013.6569353\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowadays, Graphical Processing Units (GPUs) have become increasingly popular due to their high computational power and low prices. This makes them particularly suitable for high-performance computing applications, like data elaboration and financial computation. In these fields, high efficient test methodologies are mandatory. One of the most effective ways to detect and localize hardware faults in GPUs is a Software-Based-Self-Test methodology (SBST). In this paper a fully comprehensive SBST and fault localization methodology for GPUs is presented. This novel approach exploits different custom test strategies for each component inside the GPU architecture. Such strategies guarantee both permanent fault detection and accurate fault localization.\",\"PeriodicalId\":118063,\"journal\":{\"name\":\"2013 18th IEEE European Test Symposium (ETS)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"36\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 18th IEEE European Test Symposium (ETS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ETS.2013.6569353\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 18th IEEE European Test Symposium (ETS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETS.2013.6569353","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Nowadays, Graphical Processing Units (GPUs) have become increasingly popular due to their high computational power and low prices. This makes them particularly suitable for high-performance computing applications, like data elaboration and financial computation. In these fields, high efficient test methodologies are mandatory. One of the most effective ways to detect and localize hardware faults in GPUs is a Software-Based-Self-Test methodology (SBST). In this paper a fully comprehensive SBST and fault localization methodology for GPUs is presented. This novel approach exploits different custom test strategies for each component inside the GPU architecture. Such strategies guarantee both permanent fault detection and accurate fault localization.