Nakul Jindal created SYSTEMML-1436:
--------------------------------------
Summary: 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
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])
--
This message was sent by Atlassian JIRA
(v6.3.15#6346)
