Sparse matrix multiplication is widely used in various practical applications. Different accelerators have been proposed to speed up sparse matrix-dense vector multiplication (SpMV), sparse ...

The Fibonacci hash, on the other hand, uses the golden ratio rather than the modulo function to map the final location of the data, resulting in many fewer instances of collisions like these while ...

A young computer scientist and two colleagues show that searches within data structures called hash tables can be much faster than previously deemed possible.

After looking at the recent GoLang update for curiosity, I noticed one standout item. GoLang now uses Swiss Tables for mapping. I'd not heard of this algorithm, so it took a bit of searching to ...

A young computer scientist and two colleagues show that searches within data structures called hash tables can be much faster than previously deemed possible.

Improving on this has been an open problem even for sparse linear systems with poly (n) condition number. In this paper, we present an algorithm that solves linear systems in sparse matrices ...

Are all sparse matrix multiplications reduced to pytorch native sparse matrix multiplications? torch-sparse implements its own sparse-dense matrix multiplication, which is usually quite a bit faster ...

I would like to know the reason why you finally choose torch.sparse.mm (), since by experimentally verified, torch.spmm () seems to be faster. In addition, I know that there is another way to ...

While adding two sparse matrices is a common operation in Matlab, Python, Intel MKL, and various GraphBLAS libraries, these implementations do not perform well when adding a large collection of sparse ...

However, it might be more reasonable to require both W and H are sparse when trying to learn useful features from a database of images. In this paper, we propose a co-sparse non-negative matrix ...