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Posted to issues@systemml.apache.org by "Nakul Jindal (JIRA)" <ji...@apache.org> on 2017/03/24 20:57:41 UTC
[jira] [Updated] (SYSTEMML-1436) Improve Sparse matrix support for
GPU operations
[ https://issues.apache.org/jira/browse/SYSTEMML-1436?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Nakul Jindal updated SYSTEMML-1436:
-----------------------------------
Labels: cuda deeplearning gpu gsoc2017 mentor (was: gsoc2017 mentor)
> Improve Sparse matrix support for GPU operations
> ------------------------------------------------
>
> Key: SYSTEMML-1436
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1436
> Project: SystemML
> Issue Type: Task
> Components: Runtime
> Reporter: Nakul Jindal
> Labels: cuda, deeplearning, gpu, gsoc2017, mentor
>
> SystemML has a preliminary set of GPU implementation for its primitive operations (Matmult, reductions, neural net operations among others). Currently, these GPU operations work when SystemML is run on a single machine (either using the Standalone mode or Spark mode). Programs written in the external DSLs (DML & PyDML) and internal DSLs (Python and Scala) can enable the use of these GPUs.
> SystemML is aware of sparsity in matrix blocks and encodes them differently. It has 3 different types of Sparse formats (CSR, COO & a custom MCSR). A lot of the GPU operations are implemented for dense matrix blocks; for some GPU operations, when sparse matrices are encountered, they are first converted to dense and then sent to the GPU.
> - This project is to implement CUDA kernels for Sparse Matrix blocks
> - Operations to be implemented include reductions, element-wise operations, neural network operations among others
> This project is fairly isolated from the internal compiler & optimizer, therefore a thorough knowledge of the entire system will not be needed.
> Knowledge of CUDA programming is preferred.
> For a initial implementation, the most efficient CUDA kernel is not required.
> Rating - Medium
> Mentors - [~nakul02], (optionally [~niketanpansare])
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