def bench_pydata_ufunc_fused(tacoBench, dim):
loader = RandomPydataSparseTensorLoader()
matrix = safeCastPydataTensorToInts(loader.random((dim, dim), 0.01))
matrix1 = safeCastPydataTensorToInts(loader.random((dim, dim), 0.01, variant=1))
matrix2 = safeCastPydataTensorToInts(loader.random((dim, dim), 0.01, variant=2))
def bench():
result = numpy.logical_and(numpy.logical_xor(matrix, matrix1), matrix2)
return result
tacoBench(bench)
def bench_slice_pydata_sparse_window(tacoBench, dim, sparsity, config):
loader = RandomPydataSparseTensorLoader()
matrix = loader.random((dim, dim), sparsity).astype('float64')
matrix2 = loader.random((dim, dim), sparsity, variant=1).astype('float64')
def bench():
x = sliceTensor(matrix, dim, config)
x2 = sliceTensor(matrix2, dim, config)
return (x, x2)
tacoBench(bench)
def bench_add_pydata_sparse_index_set(tacoBench, dim, fraction):
loader = RandomPydataSparseTensorLoader()
indexes = [i * fraction for i in range(0, dim // fraction)]
matrix = loader.random((dim, dim), 0.01)
matrix2 = loader.random((dim, dim), 0.01, variant=1)
def bench():
x = matrix[:, indexes]
x2 = matrix2[:, indexes]
res = x + x2
tacoBench(bench)
def bench_add_pydata_sparse_strided_window(tacoBench, dim, sparsity,
strideWidth):
loader = RandomPydataSparseTensorLoader()
matrix = loader.random((dim, dim), sparsity).astype('float64')
matrix2 = loader.random((dim, dim), sparsity, variant=1).astype('float64')
def bench():
x = matrix[0:dim:strideWidth, 0:dim:strideWidth]
x2 = matrix2[0:dim:strideWidth, 0:dim:strideWidth]
res = x + x2
tacoBench(bench)