{"title":"半精度浮点运算的数值基准","authors":"P. Luszczek, J. Kurzak, I. Yamazaki, J. Dongarra","doi":"10.1109/HPEC.2017.8091031","DOIUrl":null,"url":null,"abstract":"With NVIDA Tegra Jetson X1 and Pascal P100 GPUs, NVIDIA introduced hardware-based computation on FP16 numbers also called half-precision arithmetic. In this talk, we will introduce the steps required to build a viable benchmark for this new arithmetic format. This will include the connections to established IEEE floating point standards and existing HPC benchmarks. The discussion will focus on performance and numerical stability issues that are important for this kind of benchmarking and how they relate to NVIDIA platforms.","PeriodicalId":364903,"journal":{"name":"2017 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Towards numerical benchmark for half-precision floating point arithmetic\",\"authors\":\"P. Luszczek, J. Kurzak, I. Yamazaki, J. Dongarra\",\"doi\":\"10.1109/HPEC.2017.8091031\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With NVIDA Tegra Jetson X1 and Pascal P100 GPUs, NVIDIA introduced hardware-based computation on FP16 numbers also called half-precision arithmetic. In this talk, we will introduce the steps required to build a viable benchmark for this new arithmetic format. This will include the connections to established IEEE floating point standards and existing HPC benchmarks. The discussion will focus on performance and numerical stability issues that are important for this kind of benchmarking and how they relate to NVIDIA platforms.\",\"PeriodicalId\":364903,\"journal\":{\"name\":\"2017 IEEE High Performance Extreme Computing Conference (HPEC)\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE High Performance Extreme Computing Conference (HPEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HPEC.2017.8091031\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE High Performance Extreme Computing Conference (HPEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HPEC.2017.8091031","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Towards numerical benchmark for half-precision floating point arithmetic
With NVIDA Tegra Jetson X1 and Pascal P100 GPUs, NVIDIA introduced hardware-based computation on FP16 numbers also called half-precision arithmetic. In this talk, we will introduce the steps required to build a viable benchmark for this new arithmetic format. This will include the connections to established IEEE floating point standards and existing HPC benchmarks. The discussion will focus on performance and numerical stability issues that are important for this kind of benchmarking and how they relate to NVIDIA platforms.