This title appears in the Scientific Report :
2020
Workshop on Machine Learning on HPC Systems (MLHPC)
Workshop on Machine Learning on HPC Systems (MLHPC)
Over the last few years, Machine Learning (and in particular Deep Learning) (ML / DL) has become an important research topic in the High Performance Computing (HPC) community. Bringing new users and data intensive applications on HPC systems, ML / DL is increasingly affecting the design and operatio...
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Personal Name(s): | Durillo, Juan J. |
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Hoppe, Dennis / Jitsev, Jenia / Keuper, Janis (Corresponding author) / Torge, Sunna | |
Contributing Institute: |
Jülich Supercomputing Center; JSC |
Imprint: |
2020
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Conference: | Workshop on Machine Learning on HPC Systems (MLHPC), Frankfurt (Germany), 2020-06-22 - 2020-06-25 |
Document Type: |
Conference / Event |
Research Program: |
Data-Intensive Science and Federated Computing |
Publikationsportal JuSER |
Over the last few years, Machine Learning (and in particular Deep Learning) (ML / DL) has become an important research topic in the High Performance Computing (HPC) community. Bringing new users and data intensive applications on HPC systems, ML / DL is increasingly affecting the design and operation of compute infrastructures. On the other hand, the ML / DL community is just getting started to utilize the performance of HPC, leaving many opportunities for better parallelization and scalability. The intent of this workshop is to bring together researchers and practitioners to discuss three key topics in the context of High Performance Computing and Machine Learning / Deep Learning: parallelization and scaling of ML / DL algorithms, ML / DL applications on HPC systems, and HPC systems design and optimization for ML / DL workloads. |