Volume 5, Issue 4Pub Date : 2020-07-30DOI: 10.46243/jst.2020.v5.i4.pp230-237
Arun A. Balakrishnan, S. Kamal, C. SatheeshChandran
{"title":"Modular Microservice based GPU Utilization Manager with\u0000Gunicorn","authors":"Arun A. Balakrishnan, S. Kamal, C. SatheeshChandran","doi":"10.46243/jst.2020.v5.i4.pp230-237","DOIUrl":"https://doi.org/10.46243/jst.2020.v5.i4.pp230-237","url":null,"abstract":":Graphics processing unit (GPU) is a computer programmable chip that could perform rapid\u0000mathematical operations that can be accelerated with massive parallelism. In the early days, central processing unit\u0000(CPU) was responsible for all computations irrespective of whether it is feasible for parallel computation. However,\u0000in recent years GPUs are increasingly used for massively parallel computing applications, such as training Deep\u0000Neural Networks. GPU’s performance monitoring plays a key role in this new era since GPUs serve an inevitable\u0000role in increasing the speed of analysis of the developed system. GPU administration comes in picture to efficiently\u0000utilize the GPU when we deal with multiple workloads to run on the same hardware. In this study, various GPUparameters are monitored and help to keep them in safe levels and also to keep the improved performance of the\u0000system. This study,","PeriodicalId":23534,"journal":{"name":"Volume 5, Issue 4","volume":"83 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83808466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}