{"title":"Speed Up Weather Prediction on QCT Developer Cloud: A Case Study on Knights Landing Platform","authors":"Gong-Do Hwang, Stephen Chang","doi":"10.1109/CSCloud.2017.48","DOIUrl":null,"url":null,"abstract":"We present the direct performance measurements of two popular weather forecast models, Weather Research and Forecast Model (WRF) and Models for Predictions Across Scales (MPAS) on Intel's Knight Landing Platform (KNL). WRF is widely evaluated over different platforms while the benchmarks of MPAS are still scarce. In this study we measured the running time of WRF and MPAS on the QCT Developer Cloud, both on its KNL-based nodes and Xeon Broadwell-based nodes. We found that for WRF its performance on single KNL node is 1.55 times faster than Broadwell one, while for MPAS is 1.1 times faster. Generally the scalability of two models on a single node is linear, and drops when across multiple nodes. Further optimization might be needed for those two models","PeriodicalId":436299,"journal":{"name":"2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSCloud.2017.48","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
We present the direct performance measurements of two popular weather forecast models, Weather Research and Forecast Model (WRF) and Models for Predictions Across Scales (MPAS) on Intel's Knight Landing Platform (KNL). WRF is widely evaluated over different platforms while the benchmarks of MPAS are still scarce. In this study we measured the running time of WRF and MPAS on the QCT Developer Cloud, both on its KNL-based nodes and Xeon Broadwell-based nodes. We found that for WRF its performance on single KNL node is 1.55 times faster than Broadwell one, while for MPAS is 1.1 times faster. Generally the scalability of two models on a single node is linear, and drops when across multiple nodes. Further optimization might be needed for those two models
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
