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  1. SystemML
  2. SYSTEMML-1436

Improve Sparse matrix support for GPU operations

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      Description

      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 - Nakul Jindal, (optionally Niketan Pansare)

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            • Assignee:
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              Reporter:
              nakul02 Nakul Jindal
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