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I have just installed a Nvidia GT660 graphic card on my desktop and, after some struggle, I manage to interface it with R.

I have been playing with several R packages that use GPUs, especially gputools, and I was comparing the time taken by my GPU and CPU to perform some basic operations:

  • inverting matrices (CPU faster)
  • qr decomposition (CPU faster)
  • big correlation matrices (CPU faster)
  • matrix multiplication (GPU much faster!)

Notice that I have experimented mainly with gputools so maybe other packages perform better.

In broad terms my question is: what some routine statistical operations that might be worth executing on a GPU rather than a CPU?

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Anything involving lots of matrix multiplication? :) GPUs are quite popular in the neural nets community. – larsmans Feb 24 at 23:26
you need to provide the size of the matrices involved. For example, last i checked (admittedly 2 years ago) inversion and decomposition were only faster on GPU starting from large matrices (2^9 times 2^9 and upwards) – user189035 Feb 24 at 23:29
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I used matrices of around $10^3 \times 10^3$ for inversion, qr and matrix multiplication, while for correlations I have used around 10^4 observations of vectors of size 100. For matrix inversion the GPU was much slower, while for qr decomposition it was slower but comparable to the CPU. – Jugurtha Feb 24 at 23:38
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this is a very good question but i think you'll get better answers by having it migrated to stackoverflow (through i think similar questions have been asked there before) – user189035 Feb 25 at 0:06
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GPU's advantage of regular CPU's is the fact that they can be "massively" parallel, not that they are faster per core. As such, for jobs that require a lot of "housekeeping" like Cholesky factorization etc. you need to use block algorithms and so forth to get significant speed-up; this is not trivial and I assume it will take a while before GPU's take over such operations. What is definitely going the GPU way is MCMC-ing (and Random Number generation). Sampling from a posterior has "parallelization" written all over it... And sparse matrices computations; they are already "blocked" anyway... – user11852 Feb 25 at 7:13
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migrated from stats.stackexchange.com Feb 25 at 17:22

2 Answers

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For all of the applications you mentioned, GPUs should be more capable (from a hardware perspective) than CPUs for sufficiently large matrices. I don't know anything about R's implementation, but I've used cuBLAS and Magma with great success for inversions around $n = 2^{10}$ and multiplication/correlation for rectangular matrices with $n,m \approx 2^{10}, k \approx 2^{14}$. It is an especially big surprise to me that large correlation matrices would be faster on the CPU using R.

More broadly, I suspect most statistical operations that spend most of their time in dense linear algebra (BLAS, Lapack functionality) can be efficiently implemented on the GPU.

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Multiple Imputation methods for Missing Data? Like those in Alice-II (R).

I think those tend to be often embarrassingly parallel and hence suitable to a GPU architecture. Never tried it myself though.

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