Dot product of 2D matrices

[jling]

@jpmorr

dot(transpose(x), y)

Numpy incorrectly calls a bunch of things “dot”, in Julia, matrix multiplication is just *:

x' * y #or transpose(x) * y

notice you’re unlikely to get speed up for this because everyone is just calling OpenBLAS routine anyway.

Why matrix multiplication is much slower than PyTorch

also, eh, pytorch default type is float32… In [8]: torch.set_num_threads(1) ...: A = torch.randn(1000, 1000, dtype=torch.float64) ...: B = torch.randn(1000, 1000, dtype=torch.float64) ...: %timeit -n 5 torch.matmul(A, B) 43.1 ms ± 810 µs per loop (mean ± std. dev. of 7 runs, 5 loops each) In [9]: torch.set_num_threads(1) ...: A = torch.randn(1000, 1000) ...: B = torch.randn(1000, 1000) ...: %timeit -n 5 torch.matmul(A, B) 22.1 ms ± 245 µs per loop (mean ± std. dev. of 7 runs,…

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Also checkout the very fast einsum pkg Tullio.jl (still, not gonna be faster for dense CPU matrix, OpenBLAS is super optimized, partly by hand written architecture specific assembly)