$FgText1 = "DarkGreen"
$FgText2 = "DarkGray"
$FgError="Red"

Write-Host("==============================================================================") -foreground $FgText1;
Write-Host("BOINC checker...") -foreground $FgText1;
Write-Host("==============================================================================") -foreground $FgText1;
Write-Host("");
$ProcessToCheckFor = "'boinc.exe'";
$TotalProcessesCount = 0;

# Create a list of process handles which are running on the system
$PidList = @(Get-WmiObject win32_process -Filter "name=$ProcessToCheckFor");
$TotalProcessesCount = $PidList.length;

$PidHandleList = $PidList | Foreach-Object { $_.Handle };

Write-Host("Total {0} Processes Count: {1}" -f $ProcessToCheckFor, $TotalProcessesCount) -foreground $FgText1;

Error Message:
Not enough quota is available to process this command.

User Action:
Close some applications and try again. If you still get this message, choose System from Control Panel, then choose Virtual Memory and increase the size of your paging file.

1. Log on to your system with an administrator account, for example, owner or administrator.
   
2. Click Start, and then click My Computer.
   
3. Right-click on the system drive (usually drive C), and then click Properties. *Click the Quota tab.
   
4. Either increase the amount of disk space available to users by changing the "Limit disk space to" option or disable the Disk quota.
   
5. Don't let kids play on the growed-up's computer
   

Let us know how you get on with that.

1. Move data to the GPU.
   
2. Perform calculation on GPU.
   
3. Move result(s) from the GPU to host.
   

With the release of CUDA 2.2, however, that paradigm has changed whereby new APIs allow the mapping of host memory into device memory via cudaHostAlloc (or cuMemHostAlloc in the CUDA driver API).

These functions provide a new memory type that supports:

• "Portable" pinned buffers that are available to all GPUs.
  
• "Mapped" pinned buffers that map host memory into the CUDA address space and provide asynchronous transparent access to the data without requiring an explicit programmer initiated copy.
  
  Setting aside integrated CUDA capable graphic adaptors that supplement their VRAM with system memory, for discrete GPUs mapped pinned buffers are only a performance win in certain cases though. Since the memory is not cached by the GPU:
  
  
  
  • It should be read or written exactly once.
    
  • The global loads and stores that read or write the memory must be coalesced to avoid a 2x-7x PCIe performance penalty.
    
  • At best, it will only deliver PCIe bandwidth performance, but this can be 2x faster than cudaMemcpy because mapped memory is able exploit the full duplex capability of the PCIe bus by reading and writing at the same time. A call to cudaMemcpy can only move data in one direction at a time (i.e., half duplex).
    
    Moreover, a drawback of the current CUDA 2.2 release is that all pinned allocations are mapped into the GPU's 32-bit linear address space, regardless of whether the device pointer is needed or not. (NVIDIA indicates this will be changed to a per-allocation basis in a later release.)
    

  
  

• Write-Combined (WC) memory that can provide higher performance:
  
  
  
  • Due to WC memory being neither cached, nor cache coherent, it isn't 'snooped' during transfers across the PCI Express bus. According to NVIDIA's notes - "CUDA 2.2 Pinned Memory APIs" - WC memory may perform as much as 40% faster on certain PCI Express 2.0 implementations.
    
    
  • It may increase the host processor(s) write performance to host memory because individual writes are first combined (via an internal processor write-buffer) so that only a single burst write containing many aggregated individual writes need be issued. (Intel claims they have observed actual performance increases of over 10x but this is not typical). For more information, please see the Intel publication: Combining Memory Implementation Guidelines
    

  
  

SOURCE: CUDA 2.2 Changes the Data Movement Paradigm

GPU applications are always removed from GPU RAM when suspended, but for when BOINC is running benchmarks. The "Leave applications in memory" switch doesn't have any effect on this. That one is for CPU applications using system RAM only.

There is a problem when you have your preferences set to Leave applications in memory while suspended. Any task still in the video card's RAM will stay there until you resume BOINC. Although it will not use your GPU, the amount of memory that a Seti CUDA tasks takes up (200MB+ of the VRAM) may still have impact on your other activities.

1. Do GPU projects utilize Graphic Aperture virtual address space that normally is utilized by the graphics adapter for rendering of textures that exceed the limitation imposed by insufficient discrete VRAM
   
2. If so, does the memory constraint specified in preferences include shared system memory, i.e., the virtual address pool utilized by the video subsystem as an adjunct to discrete on-board VRAM?
   

1. Terminate the 2 BOINC processes: boincmgr & boinctray in Process manager, i.e., <ctrl>+<Shift>+<Esc> (click image name to sort alphabetically)
   
2. Navigate to C:\Program Files\Boinc (or where you've installed BOINC) in Windows Explorer
   
3. right click and select Properties for BOINC Programs folder
   
4. remove Read only and Apply to all subsdirectory.
   
5. Restart boinc and it run again
   

Use at most X% of memory when computer is in use

Limits the amount of RAM used by BOINC applications when your computer is in use (i.e. when there has been recent mouse or keyboard activity). Setting this to a low value lets you run BOINC all the time without impacting your computer's performance. The Default is 50%. - [emphasise mine]