Discussion Thread for the Continuous GW Search known as O2MD1 (now O2MDF - GPUs only)

Have done 330 GPU O2MD1 tasks, 200 pending 130 validated, no errors, no invalids.  3.2 minutes average. 

Must be a big difference in the OpenCL stack between Nvidia and AMD for the hardware to be that far apart. Or the compiler uses much better optimization for Nvidia hardware compared to AMD hardware.

We have had a situation here at Einstein for some time that modern AMD cards over-perform relative to modern Nvidia cards on the Gamma--Ray Pulsar tasks relative to their capabilities on widely published competitive reviews.

Now we seem to have a developing situation in which modern Nvidia cards over-perform relative to modern AMD cards on the current (and a previous few) GW tasks.

If this persists, one might speculate that it would be inviting to equip a PC with one each of suitable AMD and Nvidia cards, directing the GW tasks to the green side and the GRP tasks to the red side.

Is this easy to do? Hard to do but possible, or out of the question?

And, yes, I suspect that for people with large fleets it probably would make much more sense to build dedicated boxes of flavor GW and flavor GRP.  But there are plenty of us with one to four boxes who might find such a setup interesting.

Use an gpu_exclude statement in the cc_config.xml file for the gpu/appname combination.

https://boinc.berkeley.edu/wiki/Client_configuration#Application_configuration

<exclude_gpu>
   <url>project_URL</url>
   [<device_num>N</device_num>]
   [<type>NVIDIA|ATI|intel_gpu</type>]
   [<app>appname</app>]
</exclude_gpu>

You can set up different exclusions for AMD types and Nvidia types.

<exclude_gpu>
   <url>http://einstein.phys.uwm.edu/</url>
   <device_num>0</device_num>
   <type>NVIDIA</type>
   <app>hsgamma_FGRPB1G</app>
</exclude_gpu>

<exclude_gpu>
    <url>http://einstein.phys.uwm.edu/</url>
    <device_num>1</device_num>
    <type>ATI</type>
    <app>einstein_O2MD1</app>
</exclude_gpu>