<meta charset="utf-8">Dear Satish,<div> my workstation has 3 TESLA C1060 and 1 GeForce 9500 GT.</div><div><br></div><div>On Thu, Jan 13, 2011 at 3:22 AM, Satish Balay <span dir="ltr"><<a href="mailto:balay@mcs.anl.gov">balay@mcs.anl.gov</a>></span> wrote:<blockquote class="gmail_quote" style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0.8ex; border-left-width: 1px; border-left-color: rgb(204, 204, 204); border-left-style: solid; padding-left: 1ex; ">
Potential issues:<br>- the machine has 2 nvidia cards with different version<br>- the card is locked by a different user - running cuda code [at configure time]<br>- the compile node doesn't have the nvidia cards [like on a cluster..]<font class="Apple-style-span" color="#888888"><br>
</font></blockquote><div><br></div><div>I solved the first two issues in this way</div><div>1. I disabled the GeForce card setting the 'compute mode' as 'Prohibited'. In this way, no CUDA kernel can run on that card</div>
<div>2. I set the 'compute mode' of every TESLA card as 'Exclusive'. In this way, only one kernel per time can run on the "selected" device.</div><div><br></div><div>Disabling the GeForce, when I run a CUDA kernel only the 3 TESLA card are potentially selected to run my code and, as soon as the driver assign me a specific card, that card will be only mine until the kernel is finished.</div>
<div><br></div><div>About the 3 issue, the Makefile can check if the compiler exists...</div><div><br></div><div>I never used directly CUDA on my macbook, but I am quite sure that on a Linux system these tricks work well.</div>
<div><br></div><div>Cheers</div><div><br></div>--<br>Filippo SPIGA, MSc Computer Science<br><div>~ homepage: <a href="http://tinyurl.com/fspiga" target="_blank">http://tinyurl.com/fspiga</a> ~<br> <br>«Nobody will drive us out of Cantor's paradise.»<br>
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