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https://issues.apache.org/jira/browse/SYSTEMML-2041?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Janardhan updated SYSTEMML-2041:
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Description:
Sparsity enables, for example, training of neural networks that are much wider
and deeper than otherwise possible with a given parameter budget and
computational budget, such as LSTMs with tens of thousands of hidden units.
(The largest LSTMs trained today are only thousands of hidden units.)
*Resource:* TensorFlow implemented repo - https://github.com/openai/blocksparse
*Best Supported architectures:* Maxwell, Pascal with Kepler & Volta support for
limited functionality
was:
Sparsity enables, for example, training of neural networks that are much wider
and deeper than otherwise possible with a given parameter budget and
computational budget, such as LSTMs with tens of thousands of hidden units.
(The largest LSTMs trained today are only thousands of hidden units.)
*Resource:* TensorFlow implemented repo - https://github.com/openai/blocksparse
> Implement Block-Sparse GPU Kernels
> ----------------------------------
>
> Key: SYSTEMML-2041
> URL: https://issues.apache.org/jira/browse/SYSTEMML-2041
> Project: SystemML
> Issue Type: New Feature
> Components: Infrastructure
> Reporter: Janardhan
> Attachments: GPU Kernels for Block-Sparse Weights.pdf
>
>
> Sparsity enables, for example, training of neural networks that are much
> wider and deeper than otherwise possible with a given parameter budget and
> computational budget, such as LSTMs with tens of thousands of hidden units.
> (The largest LSTMs trained today are only thousands of hidden units.)
> *Resource:* TensorFlow implemented repo -
> https://github.com/openai/blocksparse
> *Best Supported architectures:* Maxwell, Pascal with Kepler & Volta support
> for limited functionality
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