<html><head><meta http-equiv="Content-Type" content="text/html charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class=""><div class="">Great! Then I have a follow-up question:</div><div class=""><br class=""></div><div class="">My goal is to be able to load the full matrix X from disk, while at the same time in parallel, performing computations on the submatrices that have already been loaded. Essentially, I want to think of X as a block matrix (where the blocks are horizontal, spanning the full width of the matrix), where I’m loading one block at a time, and all the blocks that have already been loaded are combined using MatCreateNest, so that I can make computations on that portion of the matrix.</div><div class=""><br class=""></div><div class="">In this scenario, every process needs to be simultaneously loading the next block of X, and perform computations on the previously loaded portion. My strategy is for each MPI process to spawn a thread for data loading (so that the memory between the process and the thread is shared), while the process does computations. My concern is that the data loading thread may be using up computational resources of the processor, even though it is mainly doing IO. Will this be an issue? What is the best way to minimize the cpu time of this parallel data loading scheme?</div><div class=""><br class=""></div><div class="">Thanks,</div><div class="">Michal.</div><div class=""><br class=""></div><br class=""><div><blockquote type="cite" class=""><div class="">Wiadomość napisana przez Matthew Knepley <<a href="mailto:knepley@gmail.com" class="">knepley@gmail.com</a>> w dniu 24.05.2017, o godz. 04:55:</div><br class="Apple-interchange-newline"><div class=""><div dir="ltr" class=""><div class="gmail_extra"><div class="gmail_quote">On Wed, May 24, 2017 at 1:09 AM, Michal Derezinski <span dir="ltr" class=""><<a href="mailto:mderezin@ucsc.edu" target="_blank" class="">mderezin@ucsc.edu</a>></span> wrote:<br class=""><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr" class="">Hi,<div class=""><br class=""></div><div class="">I want to be able to perform matrix operations on several contiguous submatrices of a full matrix, without allocating the memory redundantly for the submatrices (in addition to the memory that is already allocated for the full matrix).</div><div class="">I tried using MatGetSubMatrix, but this function appears to allocate the additional memory.</div><div class=""><br class=""></div><div class="">The other way I found to do this is to create the smallest submatrices I need first, then use MatCreateNest to combine them into bigger ones (including the full matrix).</div><div class="">The documentation of MatCreateNest seems to indicate that it does not allocate additional memory for storing the new matrix.</div><div class="">Is this the right approach, or is there a better one?</div></div></blockquote><div class=""><br class=""></div><div class="">Yes, that is the right approach.</div><div class=""><br class=""></div><div class=""> Thanks,</div><div class=""><br class=""></div><div class=""> Matt</div><div class=""> </div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr" class=""><div class="">Thanks,</div><div class="">Michal Derezinski.</div></div>
</blockquote></div><br class=""><br clear="all" class=""><div class=""><br class=""></div>-- <br class=""><div class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr" class=""><div class="">What most experimenters take for granted before they begin their experiments is infinitely more interesting than any results to which their experiments lead.<br class="">-- Norbert Wiener</div><div class=""><br class=""></div><div class=""><a href="http://www.caam.rice.edu/~mk51/" target="_blank" class="">http://www.caam.rice.edu/~mk51/</a><br class=""></div></div></div>
</div></div>
</div></blockquote></div><br class=""></body></html>