CPU / GPU tuning / multiple work units?

I have been following this thread and had the same questions.   #2 of WIYOSAY's can be most easily answered by going to project preferences and select something like "0.5" or less for GPU utilization.

Question 1 is still unanswered.  I would like to know if it is possible to set the CPU usage down so it is less than 1.  Why?  because some of my systems are core 2 quad and only have 4 cores and no hyperthread possibility.

My GTX1070 can run at least 4 apps but each app seems to require a full core.  Some projects like GPUGRID show ".9 cpu + 1 nvidia" others (eg seti) show way less like "0.1 cpu + 1 nvidia"

I tried running 2, then 4 apps simultaneously and it worked up until I tried to do something else like open a bunch of windows and run some non-boinc apps (I have another life besides boinc).  I have 16gb on this core 2 quad (Dell 730) but the system became so slow it was unusable, then I started getting nvidia driver resets.  When I closed the other windows and shut down some running apps the driver reset went away.  I even had to terminate chrome as it would not close using the [x].  System went back to normal.  Looking at the WUs  they didn't error out after the driver reset which was totally unexpected. 

If it is actually possible to assign only 1 cpu to handle, for example, 4 Einstein concurrent WUs please advise.

Thanks for looking! 

erm... No.

These parameters govern scheduling and task launch.  They do not govern how the tasks, once started, actually run on the machine.   Despite the sloppy language used commonly these forums, BOINC does not in fact "dedicate" CPU resources.

If you really want to make all of your GPU tasks share a single CPU core (Why?) you can do it, using something to set the CPU Affinity.  While one can do this for a single operating instance of these tasks by just using Process Explorer (for example), that affinity assignment evaporates as soon as the GPU task it serves terminates.

I think most of us who tinker with CPU affinity use Process Lasso.  One of the (many) options it gives is to require any task matching a name string you provide to run on a subset (down to 1) of cores as you specify.

I'll warn, strongly, that this is not likely to do just what you expect in terms of either task performance or system impact, but it is a powerful tool.

I, myself, currently run four GPU tasks on two GPUs on a four core system, and have used Process Lasso to restrain the resource appetite of the support tasks with the specific goal of preserving good interactive performance (I'm typing this on that system, which is my daily driver).  I did not, however (after tests) restrict CPU affinity of the GPU support task.

Just because the results of my trials usually surprise me, and some results are quite unfavorable, does not mean it is not fun, and sometimes significantly helpful.

I should mention that all my comments reflect experience and understanding from Windows systems.

The configuration options I currently employ in Process Lasso include:

CPU priority, I/O priority, Memory Priority

I don't have any recent generation nvidia GPUs.  The most recent are 750Tis which run fairly poorly and do suffer if the default allocation of 1 CPU core per GPU task is over-ridden.  It's a different story with AMD GPUs.  For example, I have a couple of machines with Pentium dual core (G3260) CPUs supporting 2xR7 370 GPUs.  Each host runs 1 CPU task and 4 GPU tasks.

I control these hosts using app_config.xml together with BOINC preferences.  I set BOINC to use 50% of the available cores.  This guarantees that only one CPU task can run and it also stops BOINC from over-fetching CPU work.  There can be real problems if BOINC decides to go into panic (high priority) mode because of excess CPU tasks.

For your 12 thread Xeon, if you left the default allocation of 1 CPU thread not able to crunch a CPU task for each GPU task, you would still have the ability to use 10 CPU threads if you were crunching 2 GPU tasks concurrently.  You could test that scenario by setting the GPU utilization factor for FGRPB1G tasks to 0.5.  As soon as you download new GPU tasks that setting would apply to all GPU tasks in your work cache.   You will know if things are working properly by the new crunch time.  It should be rather less than double what the single task crunch time was.  If it doesn't improve the overall output, you should try changing the BOINC preference for %cores to use to stop an extra CPU task or two until you find the optimum conditions.

For the quad core Phenom II, I would suggest putting it in a different location (generic, home, school, work) from the Xeon as you will likely need different settings for it.  If you use an app_config.xml file you can quickly change settings and get pretty quick feedback on whether or not a change is beneficial.  You could always start the the GPU utilization factor of 0.5 to get times while crunching 2xCPU tasks and 2xGPU tasks which would be the default outcome for that factor.  Then you could try setting up an app_config.xml file (link to documentation was in my previous message).  To try allowing 3 CPU tasks to run, you would use these settings:-

1. Set BOINC to use 75% of CPU cores.

2. Install this file (named app_config.xml) in the Einstein project directory.  Overrides GPU utilization factor.

<app_config>
    <app>
        <name>hsgamma_FGRPB1G</name>
        <gpu_versions>
            <gpu_usage>0.5</gpu_usage>
            <cpu_usage>0.2</cpu_usage>
        </gpu_versions>
    </app>
</app_config>

Please note that the purpose of the above scheme is to control the CPU tasks allowed to run with the BOINC %cores setting whilst making sure that running two GPU tasks will NOT cause a further core to be prevented from running a CPU task.  The setting of 0.2 just needs to be low enough so that twice the setting doesn't add up to a further full core.  This is just artificial fiddling to get the mix you want.  It does NOT control the actual support being used by GPU tasks.  They will take whatever they need.

If you don't provide enough support, performance will suffer as the GPU tasks will just have to fight for CPU cycles when they're needed.  If GPU performance is suffering, the only remedy is to restrict (through %cores setting) the number of running CPU tasks.  You will be able to get a benefit from running two concurrent GPU tasks.  You could try running three but I suspect you won't be able to improve on running two.  The only way to find out is to do proper experiments.