Fast sparse matrix-vector multiplication?

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Salvatore Lo Bello
Salvatore Lo Bello 2016-9-18
编辑： Bjorn Gustavsson 2016-9-19
Hi,
I need to perform large sparse matrix-vector multiplications (matrix size up to 100mX100m). The matrix need of course to be sparse (otherwise I would have problems with the memory), and I noticed that the operator * does NOT support multithreading with sparse matrices. Hence, when I run my program on a cluster I do not get significant speedups. Can you help me? Thanks a lot!

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回答（1 个）

John D'Errico
John D'Errico 2016-9-19
编辑：John D'Errico 2016-9-19
As usual, people want huge operations to run in milliseconds, as if their computer was infinitely large and infinitely fast. No matter how fast you get it running, tomorrow or next week you will want to solve problems 10 times as big in the same amount of time.
Sorry, but get a faster computer. What else can be realistically said here?
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John D'Errico
John D'Errico 2016-9-19
Sparse matrix multiplies run in only one core in MATLAB, at least at the current time. That may change in the future, but I did recently verify that MATLAB uses one core only here.
Bjorn Gustavsson
Bjorn Gustavsson 2016-9-19
编辑：Bjorn Gustavsson 2016-9-19
What I had in mind was to instead of doing:
V = randn(9,1);
M = randn(9,9);
MV = M*V;
it might be possible to manually partition the calculations similar to:
M1 = M(:,1:3);
M2 = M(:,(1:3)+3);
M3 = M(:,(1:3)+6);
MV123 = M1*V(1:3) + M2*V(4:6) + M3*V(7:9);
If it is beneficial to do this "manual" parallelization or not depends on whether the increase of data-transfer and so on, but it is trivially possible to parallelize also sparse multiplications.

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