I would like to load and run more than one job on my 1080. I've read that some BOINC projects support this operation and can be configured in the app_config.xml file. Is this possible with Gpugrid?

If the goal is to maximize the utilization of your GPU, try SWAN_SYNC

If the goal is to maximize the utilization of your GPU, try SWAN_SYNC

Under Linux - I would like to add.

Hi Itgobis,

I'm not an expert and do not know whether GPUGrid supports this, but I suspect that running more than a single GPUGrid job on your graphics card might not increase the utilisation and might even cause the jobs to run less efficiently than had they been run sequentially.

From your profile I see that, like me, one of your machines is running Windows 10 Professional, albeit mine is Win 10 Home and I am running a GTX 1070. I was surprised to see that my Windows Task Manager typically reports the GPU load at less than 5% when running GPUGrid Tasks, however, on viewing greater detail ie Task Manager (More Detail) -> Performance -> GPU X (where GPU X is the relevant card) if you then click on the title of one of the GPU X charts in the panel to the right and select the Compute_0 option, in my case, a continous Compute_0 loading normally between 85% and 95% is reported when running GPUGrid tasks. At this level, I presume there is little headroom to run a second task effectively and, I am speculating here but, doing so might introduce an overhead in managing the two tasks?

Perhaps someone with expertise in graphics card loading could comment.

Try using GPU-Z and click on the Sensors tab. Make its window as wide as your screen. Watch the GPU Load under different scenarios. I wish GPU-Z worked for Linux.

This is an example of an app_config.xml file to put in your Project folder. Each GPU WU requires a logical CPU thread of its own:
<app_config>
<app>
<name>acemdlong</name>
<gpu_versions>
<cpu_usage>1.0</cpu_usage>
<gpu_usage>0.5</gpu_usage>
</gpu_versions>
<fraction_done_exact>1</fraction_done_exact>
</app>
<app>
<name>acemdshort</name>
<gpu_versions>
<cpu_usage>1.0</cpu_usage>
<gpu_usage>0.5</gpu_usage>
</gpu_versions>
<fraction_done_exact>1</fraction_done_exact>
</app>
<app>
<name>QC</name>
<max_concurrent>8</max_concurrent>
<fraction_done_exact>1</fraction_done_exact>
</app>
<app_version>
<app_name>QC</app_name>
<plan_class>mt</plan_class>
<avg_ncpus>4</avg_ncpus>
</app_version>
<app>
<name>QC_beta</name>
<max_concurrent>1</max_concurrent>
<fraction_done_exact>1</fraction_done_exact>
</app>
<app_version>
<app_name>QC_beta</app_name>
<plan_class>mt</plan_class>
<avg_ncpus>4</avg_ncpus>
</app_version>
</app_config>[/quote]

This is an example of an app_config.xml file to put in your Project folder. Each GPU WU requires a logical CPU thread of its own:
<app_config>
<app>
<name>acemdlong</name>
<gpu_versions>
<cpu_usage>1.0</cpu_usage>
<gpu_usage>0.5</gpu_usage>
</gpu_versions>
<fraction_done_exact>1</fraction_done_exact>
</app>
<app>
<name>acemdshort</name>
<gpu_versions>
<cpu_usage>1.0</cpu_usage>
<gpu_usage>0.5</gpu_usage>
</gpu_versions>
<fraction_done_exact>1</fraction_done_exact>
</app>
<app>
<name>QC</name>
<max_concurrent>8</max_concurrent>
<fraction_done_exact>1</fraction_done_exact>
</app>
<app_version>
<app_name>QC</app_name>
<plan_class>mt</plan_class>
<avg_ncpus>4</avg_ncpus>
</app_version>
<app>
<name>QC_beta</name>
<max_concurrent>1</max_concurrent>
<fraction_done_exact>1</fraction_done_exact>
</app>
<app_version>
<app_name>QC_beta</app_name>
<plan_class>mt</plan_class>
<avg_ncpus>4</avg_ncpus>
</app_version>
</app_config>

This is an example of an app_config.xml file to put in your Project folder. Each GPU WU requires a logical CPU thread of its own:
<app_config>
<app>
<name>acemdlong</name>
<gpu_versions>
<cpu_usage>1.0</cpu_usage>
<gpu_usage>0.5</gpu_usage>
</gpu_versions>
<fraction_done_exact>1</fraction_done_exact>
</app>
<app>
<name>acemdshort</name>
<gpu_versions>
<cpu_usage>1.0</cpu_usage>
<gpu_usage>0.5</gpu_usage>
</gpu_versions>
<fraction_done_exact>1</fraction_done_exact>
</app>
<app>
<name>QC</name>
<max_concurrent>8</max_concurrent>
<fraction_done_exact>1</fraction_done_exact>
</app>
<app_version>
<app_name>QC</app_name>
<plan_class>mt</plan_class>
<avg_ncpus>4</avg_ncpus>
</app_version>
<app>
<name>QC_beta</name>
<max_concurrent>1</max_concurrent>
<fraction_done_exact>1</fraction_done_exact>
</app>
<app_version>
<app_name>QC_beta</app_name>
<plan_class>mt</plan_class>
<avg_ncpus>4</avg_ncpus>
</app_version>
</app_config>

What is the use of multiple tasks? My Afterburner is reporting 85% to 92% GPU utilization. Except in the case for short runs, my GPU utilization is still 65%.

The idea is do you get more work done in the long run. For you is???

Time{WU1 in parallel with WU2} < Time{WU1 in series with WUs}

I don't see a app_config.xml in my C:\ProgramData\BOINC directory. Is this file hidden?

I don't see a app_config.xml in my C:\ProgramData\BOINC directory. Is this file hidden?

There isn't one. You need to construct one yourself if you want to do that. However, GPUGrid provides bonus for work units returned before 24 hours after being sent. By running more than 1 work unit at a time on your GPU, it's highly doubtful you will finish it in order to get the bonus. Some work units work your GPU hard enough with only 1 on them (example High Temps and throttling) All these need to be considered if you are planning on running more than 1 at a time.

---

A GTX 970 can complete all tasks easily within 24 hours at 2x. A GTX 1080 is over double the performance. It'll be fine.

I used to play around with multiple tasks on a single card, since projects like MilkyWay or some of PG's sieving apps benefit nicely from it, but I never really conclusively figured out if it actually makes a worthwhile difference. Combined with the 24h-bonus and the fact that each task hogs an entire CPU thread due to SWAN_SYNC I decided to forego any potential minute gains and just concentrate on one task per card.