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Implementation of the GraphBLAS spec using MLIR python bindings

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python-graphblas/python-mlir-graphblas

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Implementation of the GraphBLAS spec using MLIR python bindings

A full GraphBLAS implementation is possible in relatively few linesof Python code because we rely heavily on thelinalg.generic operationin MLIR, specifically its appropriate handling of different Sparse Tensorlayouts. Each GraphBLAS operation becomes essentially a singlelinalg.generic operation with some minor pre- and post-handling ofobject creation.

Usage

Create a Matrix or Vector

>>>frommlir_graphblasimporttypes,operations,operators,Matrix,Vector>>>m=Matrix.new(types.FP64,2,5)>>>m.build([0,0,1,1,1], [1,3,0,3,4], [1.,2.,3.,4.,5.75])>>>mMatrix<FP64,shape=(2,5),format=CSR>>>>v=Vector.new(types.FP64,5)>>>v.build([0,2,3,4], [3.,-2.,4.,1.5])>>>vVector<FP64,size=5>

Perform Operations

Each operation requires an output object to be passed in.

>>>frommlir_graphblas.operatorsimportSemiring>>>z=Vector.new(types.FP64,2)>>>operations.mxv(z,Semiring.plus_times,m,v)>>>zVector<FP64,size=2)

View Results

>>>indices,values=z.extract_tuples()>>>indicesarray([0,1],dtype=uint64)>>>valuesarray([8.   ,33.625])

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