Hi all,
I have updated the design document for our GPU backend in the JIRA https://issues.apache.org/jira/browse/SYSTEMML-445. The implementation details are based on the prototype I created and is available in PR https://github.com/apache/incubator-systemml/pull/131. Once we are done with the discussion, I can clean up and separate out the GPU backend in a separate PR for easier review :) Here are key design points: A GPU backend would implement two abstract classes: 1. GPUContext 2. GPUObject The GPUContext is responsible for GPU memory management and gets call-backs from SystemML's bufferpool on following methods: 1. void acquireRead(MatrixObject mo) 2. void acquireModify(MatrixObject mo) 3. void release(MatrixObject mo, boolean isGPUCopyModified) 4. void exportData(MatrixObject mo) 5. void evict(MatrixObject mo) A GPUObject (like RDDObject and BroadcastObject) is stored in CacheableData object. It contains following methods that are called back from the corresponding GPUContext: 1. void allocateMemoryOnDevice() 2. void deallocateMemoryOnDevice() 3. long getSizeOnDevice() 4. void copyFromHostToDevice() 5. void copyFromDeviceToHost() In the initial implementation, we will add JCudaContext and JCudaPointer that will extend the above abstract classes respectively. The JCudaContext will be created by ExecutionContextFactory depending on the user-specified accelarator. Analgous to MR/SPARK/CP, we will add a new ExecType: GPU and implement GPU instructions. The above design is general enough so that other people can implement custom accelerators (for example: OpenCL) and also follows the design principles of our CP bufferpool. Thanks, Niketan Pansare IBM Almaden Research Center E-mail: npansar At us.ibm.com http://researcher.watson.ibm.com/researcher/view.php?person=us-npansar
