<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Dear Payam,<div><br></div><div> the solution might be simple: increase the number of nodes! The FFT-related quantities will be spread across more nodes, so that the memory per node will decrease (assuming that the number of pools stays the same).</div><div>It depends however on your system. If you have a very large one in hands (I mean, with a lot of electrons), you need to distribute also the matrix to be diagonalized (in cdiaghg). To achieve this, be sure to compile the scalapack enabled version and to use the -ndiag option properly. </div><div>If you don't have more nodes, then I'm afraid you'll be forced to reduce the size of your system or PW cutoffs/number of bands. Also, using 'cg' style diagonalization will save some memory, but if this will be effective it depends on your system (about which you don't say a word...). If you need more help please put an effort to be more specific.</div><div><br></div><div>GS</div><div><br><div><div>Il giorno 25/ott/2011, alle ore 19.43, Payam Norouzzadeh ha scritto:</div><br class="Apple-interchange-newline"><blockquote type="cite"><meta http-equiv="Content-Type" content="text/html; charset=utf-8"><div dir="ltr">Dear Quantum Espresso Users<br><br>I think the jobs run slow when they are swapping memory to disk.<br><span lang="en-US"><font size="2" face="Calibri,sans-serif"><span style="font-size:11pt;">Is there anything I can do to distribute the memory across more nodes so they don’t have to swap?</span></font></span><br>
I want to reduce RAM usage in practice. How it is possible?<br><br>Thanks in advance.<br>Payam Norouzzadeh<br></div>
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