Published May 4, 2014
| Version v1
Working paper
Open
Computational Throughput of Accelerator Units with Application to Neural Networks
Creators
- 1. High Performance Computing Section, IT Dept., Norwegian University of Science and Technology
Contributors
Other:
- 1. Faculty of Medicine, Kavli Institute for Systems Neuroscience / Centre for Neural Computation, Norwegian University of Science and Technology
Description
The size of data that can be fitted with a statistical model becomes restrictive when accounting for hidden
dynamical effects, but approximations can be computed using loosely coupled computations mainly limited by
computational throughput. This whitepaper describes scalability results attained by implementing one
approximate approach using accelerator technology identified in the PRACE deliverable D7.2.1 [1], with the aim
of adapting the technique to future exascale platforms.
Files
WP164.pdf
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(287.2 kB)
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