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<div class="moz-cite-prefix">Hi Tim,<br>
<br>
I am not sure about the non-blocking type communication is
supported by ParaView, Catalyst or not but i think that assigning
an extra core for global reduction is possible. You could use MPI
communication for this purpose. So, look at following code of mine
for overloaded coprocessorinitializewithpython function. As you
can see, it also gets the MPI communicatior and it allows to use a
pool of processor (or cores) for co-processing. In my case, it is
running smoothly without any problem. I hope it helps.<br>
<br>
--ufuk<br>
<br>
extern "C" void my_coprocessorinitializewithpython_(int *fcomm,
const char* pythonScriptName, const char strarr[][255], int *size)
{<br>
if (pythonScriptName != NULL) {<br>
if (!g_coprocessor) {<br>
g_coprocessor = vtkCPProcessor::New();<br>
MPI_Comm handle = MPI_Comm_f2c(*fcomm);<br>
vtkMPICommunicatorOpaqueComm *Comm = new
vtkMPICommunicatorOpaqueComm(&handle);<br>
g_coprocessor->Initialize(*Comm);<br>
vtkSmartPointer<vtkCPPythonScriptPipeline> pipeline =
vtkSmartPointer<vtkCPPythonScriptPipeline>::New();<br>
pipeline->Initialize(pythonScriptName);<br>
g_coprocessor->AddPipeline(pipeline);<br>
//pipeline->FastDelete();<br>
}<br>
<br>
if (!g_coprocessorData) {<br>
g_coprocessorData = vtkCPDataDescription::New();<br>
// must be input port for all model components and for all
dimensions<br>
for (int i = 0; i < *size; i++) {<br>
g_coprocessorData->AddInput(strarr[i]);<br>
std::cout << "adding input port [" << i
<< "] = " << strarr[i] << std::endl;<br>
}<br>
}<br>
}<br>
}<br>
<br>
On 25/10/16 01:56, Gallagher, Timothy P wrote:<br>
</div>
<blockquote
cite="mid:BN6PR07MB3012906BFE87D11728551D119DA90@BN6PR07MB3012.namprd07.prod.outlook.com"
type="cite">
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<div id="divtagdefaultwrapper"
style="font-size:12pt;color:#000000;font-family:Calibri,Arial,Helvetica,sans-serif;">
<p>Hello again!</p>
<p><br>
</p>
<p>I'm looking at using coprocessing for something that may take
awhile to actually compute, so I would like to do it in a
non-blocking fashion. Essentially I am going to be extracting
data from the simulation into some numpy arrays (so once
copied, the original data in the pipeline can change) and then
send it to the root processor to do some global operations.
<br>
</p>
<p><br>
</p>
<p>The global operations may take some time (not minutes, but
longer than I want my simulation to wait for it to complete).
Is there a way to do part of the pipeline in a non-blocking
fashion, where the script calls a function that will write out
a data file when processing and then returns control to the
simulation prior to the function completion? Will I have to do
something in native-python, like spawn a new thread to do the
function call, or is there a way to do it with how Paraview
operates?</p>
<p><br>
</p>
<p>On a related note, I may not want to have the root processor
of the coprocessing to have any simulation code running on it.
If I am running my simulation on N cores, is it possible to
have N+1 cores running the coprocessor pipeline where the
extra core receives the global data reduction from the N cores
and does the crunching? Or am I starting to ask for too much
there?</p>
<p><br>
</p>
<p>Thanks as always,</p>
<p><br>
</p>
<p>Tim<br>
</p>
</div>
<br>
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