{"push_id":1407124556,"size":4,"distinct_size":4,"ref":"refs/heads/master","head":"f2344ff6fa0a39f5c426fc87c40b45cfc70dcd59","before":"dd8adccb31c8499c20856486a804c2c210844e3c","commits":[{"sha":"c94e86cfe6e78e00391df4967c83ac4ef2b259a0","author":{"name":"Niketan Pansare","email":"330b2f3620185332dbffeb3b6eaf48a02297cb10@us.ibm.com"},"message":"[SYSTEMML-1099] Mavenize the creation of python package\n\n- Also added the LICENSE and DISCLAIMER to the root of the python package\n  as per discussion in the mailing list.","distinct":true,"url":"https://api.github.com/repos/deroneriksson/incubator-systemml/commits/c94e86cfe6e78e00391df4967c83ac4ef2b259a0"},{"sha":"bfc0e0dc772d477bb29d0915c7e793f2e3361a22","author":{"name":"Niketan Pansare","email":"330b2f3620185332dbffeb3b6eaf48a02297cb10@us.ibm.com"},"message":"[SYSTEMML-446] Exploit cublas libraries for transpose and certain cases of binary operations + add support for invoking custom kernels\n\n1. Tanuj surveyed the CuBLAS libraries and implemented LibMatrixCUDA\nmethods. These methods map SystemML operations to CuBLAS calls similar\nto LibMatrixCUDA.matmult(). Here are the list of methods: transpose,\ncellwiseMatMatAddSub, matScalarElementwiseMultDiv and vectorScalarMult.\nIn subsequent PR, we will implement remaining binary operations using\ncustom kernels (i.e. phase 2).\n\n2. Also, added initial support for invoking custom kernels in\npreparation for phase 2. This relies of custom kernels compiled into ptx\nformat and included as resource file. Since ptx format are\ntarget-independent, the kernels can be supported for different GPU\nversions. The utility\norg.apache.sysml.runtime.instructions.gpu.context.JCudaKernels\nsimplifies the launching of the kernels. For example: to launch a kernel\ncopyUpperToLowerTriangleDense<<1,1,32,32>>(jcudaDenseMatrixPtr, dim,\ndim*dim), the user has to call:\nkernels.launchKernel(\"copyUpperToLowerTriangleDense\", new\nExecutionConfig(1,1,32,32), jcudaDenseMatrixPtr, dim, dim*dim).\n\n3. Also fixed a GPU bufferpool bug (setting of dirty flag).\n\n4. Implemented sparseToDense() and denseToSparse() methods in\nJCudaObject similar to MatrixBlock.","distinct":true,"url":"https://api.github.com/repos/deroneriksson/incubator-systemml/commits/bfc0e0dc772d477bb29d0915c7e793f2e3361a22"},{"sha":"70de5e4ad6d94d7dcf9c2f1c81d6fe63f0c44229","author":{"name":"Nakul Jindal","email":"5d981c85af77e761ae2547651fb6a6869b14859d@gmail.com"},"message":"[SYSTEMML-446] Bug fixes for GPU backend (custom kernel/binary op)","distinct":true,"url":"https://api.github.com/repos/deroneriksson/incubator-systemml/commits/70de5e4ad6d94d7dcf9c2f1c81d6fe63f0c44229"},{"sha":"f2344ff6fa0a39f5c426fc87c40b45cfc70dcd59","author":{"name":"Niketan Pansare","email":"330b2f3620185332dbffeb3b6eaf48a02297cb10@us.ibm.com"},"message":"[SYSTEMML-446] Added GPU sel+ (relu), bias_add (both CP and GPU).\n\n- Also removed redirecting of sparse GPU convolution instructions to CP.\n- Also supported custom kernel when input to ReLU are greater than 2GB.\n- Updating logic of number of threads for custom kernels.\n- Safe conversion of long to int and safe multiplication of int by SizeOf.DOUBLE and SizeOf.INT.\n- Synchronization bufferpool calls, taking care of inconsistent states (by removing isAllocated variable and always setting jcuda pointers through setter methods)\n- Added Apache license to the generated ptx.\n\nCloses #247.","distinct":true,"url":"https://api.github.com/repos/deroneriksson/incubator-systemml/commits/f2344ff6fa0a39f5c426fc87c40b45cfc70dcd59"}]}
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