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rparallel-processingsparse-matrix

Parallelize Solve() for Ax=b?

Crossposted with STATS.se since this problem could straddle both STATs.se/SO https://stats.stackexchange.com/questions/17712/parallelize-solve-for-ax-b

I have some extremely large sparse matrices created using spMatrix function from the matrix package.

Using the solve() function works for my Ax=b issue, but it takes a very long time. Several days.

I noticed that http://cran.r-project.org/web/packages/RScaLAPACK/RScaLAPACK.pdf appears to have a function that can parallelize the solve function, however, it can take several weeks to get new packages installed on this particular server.

The server already has the snow package installed it.

So

  1. Is there a way of using snow to parallelize this operation?
  2. If not, are there other ways to speed up this type of operation?
  3. Are there other packages like RScaLAPACK? My search on RScaLAPACK seemed to suggest people had a lot of issues with it.

Thanks.

[EDIT] -- Additional details

The matrices are about 370,000 x 370,000. I'm using it to solve for alpha centrality, http://en.wikipedia.org/wiki/Alpha_centrality. I was originally using the alpha centrality function in the igraph package, but it would crash R.

More details

  • This is on a single machine with 12 cores and 96 gigs of memory (I believe)
  • It's a directed graph along the lines of paper citation relationships.
  • Calculating condition number and density will take awhile. Will post as it comes available.
  • Will crosspost on stat.SE and will add a link back to here
Solution

  • [Update 1: For those just tuning in: The original question involved parallelizing computations to solving a regression problem; given that the underlying problem is related to alpha centrality, some of the issues, such as bagging and regularized regression may not be as immediately applicable, though that leads down the path of further statistical discussions.]

    There are a bundle of issues to address here, from the infrastructural to the statistical.

    Infrastructure [Updated - also see Update #2 below.]

    Regarding parallelized linear solvers, you can replace R's BLAS / LAPACK library with one that supports multithreaded computations, such as ATLAS, Goto BLAS, Intel's MKL, or AMD's ACML. Personally, I use AMD's version. ATLAS is irritating, because one fixes the number of cores at compilation, not at run-time. MKL is commercial. Goto is not well supported anymore, but is often the fastest, but only by a slight margin. It's under the BSD license. You can also look at Revolution Analytics's R, which includes, I think, the Intel libraries.

    So, you can start using all of the cores right away, with a simple back-end change. This could give you a 12X speedup (b/c of the number of cores) or potentially much more (b/c of better implementation). If that brings down the time to an acceptable range, then you're done. :) But, changing the statistical methods could be even better.

    You've not mentioned the amount of RAM available (or the distribution of it per core or machine), but A sparse solver should be pretty smart about managing RAM accesses and not try to chew on too much data at once. Nonetheless, if it is on one machine and if things are being done naively, then you may encounter a lot of swapping. In that case, take a look at packages like biglm, bigmemory, ff, and others. The former addresses solving linear equations (or GLMs, rather) in limited memory, the latter two address shared memory (i.e. memory mapping and file-based storage), which is handy for very large objects. More packages (e.g. speedglm and others) can be found at the CRAN Task View for HPC.

    A semi-statistical, semi-computational issue is to address visualization of your matrix. Try sorting by the support per row & column (identical if graph is undirected, else do one then the other, or try a reordering method like reverse Cuthill-McKee), and then use image() to plot the matrix. It would be interesting to see how this is shaped, and that affects which computational and statistical methods one could try.

    Another suggestion: Can you migrate to Amazon's EC2? It is inexpensive, and you can manage your own installation. If nothing else, you can prototype what you need and migrate it in-house once you have tested the speedups. JD Long has a package called segue that apparently makes life easier for distributing jobs on Amazon's Elastic MapReduce infrastructure. No need to migrate to EC2 if you have 96GB of RAM and 12 cores - distributing it could speed things up, but that's not the issue here. Just getting 100% utilization on this machine would be a good improvement.

    Statistical

    Next up are multiple simple statistical issues:

    1. BAGGING You could consider sampling subsets of your data in order to fit the models and then bag your models. This can give you a speedup. This can allow you to distribute your computations on as many machines & cores as you have available. You can use SNOW, along with foreach.

    2. REGULARIZATION The glmnet supports sparse matrices and is very fast. You would be wise to test it out. Be careful about ill-conditioned matrices and very small values of lambda.

    3. RANK Your matrices are sparse: are they full rank? If they are not, that could be part of the issue you're facing. When matrices are either singular or very nearly so (check your estimated condition number, or at least look at how your 1st and Nth eigenvalues compare - if there's a steep drop off, you're in trouble - you might check eval1 versus ev2,...,ev10,...). Again, if you have nearly singular matrices, then you need to go back to something like glmnet to shrink out the variables are either collinear or have very low support.

    4. BOUNDING Can you reduce the bandwidth of your matrix? If you can block diagonalize it, that's great, but you'll likely have cliques and members of multiple cliques. If you can trim the most poorly connected members, then you may be able to estimate their alpha centrality as being upper bounded by the lowest value in the same clique. There are some packages in R that are good for this sort of thing (check out Reverse Cuthill-McKee; or simply look to see how you'd convert it into rectangles, often relating to cliques or much smaller groups). If you have multiple disconnected components, then, by all means, separate the data into separate matrices.

    5. ALTERNATIVES Are you wedded to the Alpha Centrality? There may be other measures that are monotonically correlated (i.e. have high rank correlation) with the same value that could be calculated more cheaply or at least implemented quite efficiently. If those will work, then your analyses could proceed with a lot less effort. I have a few ideas, but SO isn't really the place to go about that discussion.

    For more statistical perspectives, appropriate Q&A should occur on the stats.stackexchange.com, Cross-Validated.

    Update 2: I was a bit too quick in answering and didn't address this from the long-term perspective. If you are planning to do research on such systems for the long-term, you should look at other solvers that may be more applicable to your type of data and computing infrastructure. Here is a very nice directory of the options for both solvers and pre-conditioners. It seems this doesn't include IBM's "Watson" solver suite. Although it may take weeks to get software installed, it's quite possible that one of the packages is already installed if you have a good HPC administrator.

    Also, keep in mind that R packages can be installed to the user directory - you need not have a package installed in the general directory. If you need to execute something as a user other than yourself, you could also download a package to the scratch or temporary space (if you're running within just 1 R instance, but using multiple cores, check out tempdir).