Cite this DOI

Remya A. R, Suraj Kamal, & Satheesh Chandran C (2020). Modular Microservice based GPU Utilization Manager with Gunicorn. *Journal of Science & Technology*, 230–237. https://doi.org/10.46243/jst.2020.v5.i4.pp230-237

@article{remyaar2020modular,
  author    = {Remya A. R and Suraj Kamal and Satheesh Chandran C},
  title     = {{Modular Microservice based GPU Utilization Manager with Gunicorn}},
  journal   = {Journal of Science \& Technology},
  year      = {2020},
  month     = {jul},
  number    = {Volume 5},
  pages     = {230--237},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2020.v5.i4.pp230-237},
  url       = {https://doi.org/10.46243/jst.2020.v5.i4.pp230-237},
  language  = {en},
  abstract  = {: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,}
}

TY  - JOUR
TI  - Modular Microservice based GPU Utilization Manager with Gunicorn
AU  - Remya A. R
AU  - Suraj Kamal
AU  - Satheesh Chandran C
JO  - Journal of Science & Technology
PY  - 2020
DA  - 2020/07/30/
IS  - Volume 5
SP  - 230
EP  - 237
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - :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,
DO  - 10.46243/jst.2020.v5.i4.pp230-237
UR  - https://doi.org/10.46243/jst.2020.v5.i4.pp230-237
ER  -

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    "URL": "https://doi.org/10.46243/jst.2020.v5.i4.pp230-237",
    "title": "Modular Microservice based GPU Utilization Manager with Gunicorn",
    "source": "Smart Scholars DOI Registry",
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    "author": [
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            "family": "Remya A. R"
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            "family": "Suraj Kamal"
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            "family": "Satheesh Chandran C",
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    "language": "en",
    "abstract": ":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,",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2020.v5.i4.pp230-237 gives all four in one JSON answer.