Implementing large linear regression models using CUDA
<p>For analysis of 10^6 genetic factors and their GeneXGene interactions (~5x10^11), I have numerous and independent linear regression problems which are probably suitable for analysis on GPUs. </p>

<p>The objective is to exhaustively search for GeneXGene interaction effects in modulating an outcome variable (a brain phenotype) using linear regression with the interaction term included. </p>

<p>As far as I know, the Householder QR factorization could be the solution for fitting regression models, however, given that each regression matrix in this particular work could easily approach the size of ~ 10'000x10, even each single regression matrix does not seem to fit in GPU on-chip memory (shared, registers etc.).</p>

<p>Should I accept this as a problem which is inherently bandwidth-limited and keep the matrices in GPU global memory during regression analysis, or are other strategies possible?</p>

<p><strong>EDIT</strong>
Here are more details about the problem:</p>

<p>There will be approximately 10'000 subjects, each with ~1M genetic parameters (genetic matrix:10'000x10^6). The algorithm in each iteration should select two columns of this genetic matrix (10'000x2) and also around 6 other variables unrelated to genetic data (age, gender etc) so the final regression model will be dealing with a matrix like the size of 10'000x[2(genetic factors)+6(covariates)+2(intercept&amp;interaction term)] and an outcome variable vector (10'000x1). This same process will be repeated ~5e11 times each time with a given pair of genetic factors. Those models passing a predefined statistical threshold should be saved as output.</p>

<p>The specific problem is that although there are ~5e11 separate regression models, even a single one does not seem to fit in on-chip memory.</p>

<p>I also guess that sticking with CUDA libraries may not be the solution here as this mandates most of the data manipulation to take place on the CPU side and only sending each QR decomposition to GPU?</p>