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Available: https://docs.google.com/presentation/d/1FnuEW2ic5dcpSyVOUMfUSM7WxJlZtTAAWt2dZXJ52A/edit#slide=id.g16e6bdb9dc_1_0" }, { "key": "ref29", "unstructured": "“Optimize Analysis Performance with a GPU.” https://www.microfocus.com/documentation/idol/IDOL_12_0/MediaServer/Guides/html/English/Content/Getting_Started/ParallelPro cessingAnalysisGPU.htm" }, { "key": "ref30", "unstructured": "A. B. Sharma, “Parameters for Evaluating Deep Learning Program’s GPU Performance.” https://mc.ai/parameters-for-evaluatingdeep-learning-programs-gpu-performance/" }, { "key": "ref31", "unstructured": "E. Vaati, “Introduction to Multiprocessing in Python.” https://code.tutsplus.com/tutorials/introduction-to-multiprocessing-in-python-cms-30281" }, { "key": "ref32", "unstructured": "“multiprocessing Basics.” https://pymotw.com/2/multiprocessing/basics.html" }, { "key": "ref33", "unstructured": "E. Forbes, “Python Multiprocessing Tutorial.” https://tutorialedge.net/python/python-multiprocessing-tutorial/" }, { "key": "ref34", "unstructured": "“multiprocessing-Process-based ‘threading’ interface.” https://docs.python.org/2/library/multiprocessing.html" }, { "key": "ref35", "unstructured": "“Deque.” https://pymotw.com/2/collections/deque.html" }, { "key": "ref36", "unstructured": "“Deque in Python.” https://www.geeksforgeeks.org/deque-in-python/" }, { "key": "ref37", "unstructured": "“collections-High-performance container datatypes.” [Online]. Available: https://docs.python.org/2/library/collections.html" }, { "key": "ref38", "unstructured": "“NVML API Reference Guide.” https://docs.nvidia.com/deploy/nvml-api/index.html" }, { "key": "ref39", "unstructured": "“plotly Dash.” https://plotly.com/dash/" }, { "key": "ref40", "unstructured": "M. Makai, “Green Unicorn (Gunicorn).” https://www.fullstackpython.com/green-unicorngunicorn.html#:~:text=Green%20Unicorn%20(Gunicorn),to%20run%20Python%20web%20applications" }, { "key": "ref41", "unstructured": "N. Emirot, “Deploy a Flask app with Gunicorn and Docker.” https://blog.nolanemirot.com/2016/03/11/deploy-a-flask-app-withgunicorn-and-docker/" }, { "key": "ref42", "unstructured": "I. Turner-Trauring, “Configuring Gunicorn for Docker.” https://pythonspeed.com/articles/gunicorn-in-docker/" }, { "key": "ref43", "unstructured": "“Design.” [Online]. Available: https://docs.gunicorn.org/en/stable/design.html" } ], "record": { "registrant": "Longman Publishers", "registered": "2020-07-30", "updated": "2026-09-21", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "url_after": "https://www.jst.org.in/index.php/pub/article/view/602", "sha256_before": null, "sha256_after": "1ce4f1c6d140295fb2cf9cde494696a38a861c98ed16aaf0c91959cd22c0ae22" }, { "seq": 2, "kind": "update", "at": "2026-09-29 23:59:41", "by": "Administrator (admin)", "by_kind": "user", "note": "record re-read from api.crossref.org", "changes": [ { "path": "container.titles.0.value", "from": "Journal of Science & Technology", "to": "Journal of Science & Technology" } ], "record_after": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2020.v5.i4.pp230-237", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "Modular Microservice based GPU Utilization Manager with Gunicorn", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2020.v5.i4.pp230-237" } ], "agents": [ { "role": "author", "name": { "given": "", "family": "Remya A. R" }, "sequence": "first" }, { "role": "author", "name": { "given": "", "family": "Suraj Kamal" }, "sequence": "additional" }, { "role": "author", "name": { "given": "", "family": "Satheesh Chandran C" }, "sequence": "additional", "identifiers": [ { "type": "ORCID", "value": "0000-0003-0728-0941", "verified": false } ] }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2020-07-30", "date_type": "PublicationDate", "online": "2020-07-30" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "issue": "Volume 5", "pages": { "first": "230", "last": "237" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/602", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/602/535", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/602/3062", "purpose": "text-mining", "return_type": "application/xml" } ], "abstract": { "value": ":Graphics processing unit (GPU) is a computer programmable chip that could perform rapid mathematical operations that can be accelerated with massive parallelism. In the early days, central processing unit (CPU) was responsible for all computations irrespective of whether it is feasible for parallel computation. However, in recent years GPUs are increasingly used for massively parallel computing applications, such as training Deep Neural Networks. GPU’s performance monitoring plays a key role in this new era since GPUs serve an inevitable role in increasing the speed of analysis of the developed system. GPU administration comes in picture to efficiently utilize 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 system. This study,", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0", "start": "2020-07-30", "applies_to": "vor" }, "references": [ { "key": "ref1", "unstructured": "A. 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