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https://issues.apache.org/jira/browse/SYSTEMML-1436?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15944137#comment-15944137
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Nakul Jindal edited comment on SYSTEMML-1436 at 3/27/17 10:21 PM:
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Thats great [~KrishnaKalyan3]!.
I am not very familiar with the process, but as I understand it, you'll have to
write a proposal and submit it . Use the description of this JIRA to get
started. I suspect you've already done this, but
[here|http://write.flossmanuals.net/gsocstudentguide/writing-a-proposal/] is
the student manual for GSoC.
To dig deeper, most of the code for the GPU backend is in
[this|https://github.com/apache/incubator-systemml/tree/master/src/main/java/org/apache/sysml/runtime/instructions/gpu]
directory.
To dig even deeped, familiarize yourself with the code, look at SYSTEMML jiras
which work on the GPU.
was (Author: nakul02):
Thats great.
I am not very familiar with the process, but as I understand it, you'll have to
write a proposal and submit it . Use the description of this JIRA to get
started. I suspect you've already done this, but
[here|http://write.flossmanuals.net/gsocstudentguide/writing-a-proposal/] is
the student manual for GSoC.
To dig deeper, most of the code for the GPU backend is in
[this|https://github.com/apache/incubator-systemml/tree/master/src/main/java/org/apache/sysml/runtime/instructions/gpu]
directory.
To dig even deeped, familiarize yourself with the code, look at SYSTEMML jiras
which work on the GPU.
> 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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