billx - 1 year ago 191

C++ Question

I would like to speed up a library I'm working on. Most of the matrices are of rather small size (say up to 10x40). Most are block sparse, with a sparsity pattern known at run time. I want to make use of sparsity to speed up linear algebra operations.

Additionally to the basic linear algebra operations, I use an SVD decomposition. Block sparse matrix would help detecting columns / rows of zero and block diagonal matrix, which can decrease decomposition time.

Is there a specific reason why sparse matrix are implemented only coefficient wise and not block wise?

I mean that the current implementation is efficient for large matrices with a few non-zero elements, but not for matrices with comparable number of non-zero and zero.

I had a look at the so-bogus library which implements sparse block matrix using Eigen library.

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Answer Source

There is not much to expect for such small matrices as this would decrease vectorization opportunities and instruction pipelining. You check by yourself by comparing the performance of a `triangular matrix * vector`

versus `full matrix * vector`

for a 10x10 matrix.

Then, regarding SVD, the situation is even worse because for such a small matrix `JacobiSVD`

is preferred and the structure of the zeros will likely be completely lost during the first sweep unless it has a very special structure which could be exploited, like a block diagonal structure.

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