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024 7 _ |a 10.1109/HPCS48598.2019.9188104
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024 7 _ |a 2128/25761
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037 _ _ |a FZJ-2019-04310
100 1 _ |a Klijn, Wouter
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111 2 _ |a The 2019 International Conference on High Performance Computing & Simulation
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|d 2019-07-15 - 2019-07-19
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245 _ _ |a Staged deployment of interactive multi-application HPC workflows
260 _ _ |c 2020
|b IEEE
300 _ _ |a 305-311
336 7 _ |a CONFERENCE_PAPER
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520 _ _ |a Running scientific workflows on a supercomputer can be a daunting task for a scientific domain specialist. Workflow management solutions (WMS) are a standard method for reducing the complexity of application deployment on high performance computing (HPC) infrastructure. We introduce the design for a middleware system that extends and combines the functionality from existing solutions in order to create a high-level, staged user-centric operation/deployment model. This design addresses the requirements of several use cases in the life sciences, with a focus on neuroscience. In this manuscript we focus on two use cases: 1) three coupled neuronal simulators (for three different space/time scales) with in-transit visualization and 2) a closed-loop workflow optimized by machine learning, coupling a robot with a neural network simulation. We provide a detailed overview of the application-integrated monitoring in relationship with the HPC job. We present here a novel usage model for large scale interactive multi-application workflows running on HPC systems which aims at reducing the complexity of deployment and execution, thus enabling new science.
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700 1 _ |a Diaz, Sandra
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700 1 _ |a Morrison, Abigail
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700 1 _ |a Peyser, Alexander
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773 _ _ |a 10.1109/HPCS48598.2019.9188104
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