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I'm no CUDA expert (not yet, anyway), but, from what I've read, the default stream (stream 0) is (mostly) synchronous to host and device, so WaitForGPU() is not needed in that case. I don't know if there is any performance penalty in explicitly calling it in
that case, anyway.<br>
<br>
In any case, it looks like there are still some cases where potentially asynchronous CUDA library calls are being "timed" without a WaitForGPU() to ensure that the calls actually complete. I will make a pass through the aijcusparse and aijviennacl code looking
for these.<br>
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--Richard<br>
<br>
<div class="moz-cite-prefix">On 9/23/19 3:28 PM, Zhang, Junchao wrote:<br>
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<blockquote type="cite" cite="mid:CA+MQGp8QuFeAozs5T9xeCxg=cN8+NqGQjuXJaWChWtBtabzNWA@mail.gmail.com">
<div dir="ltr">It looks cusparsestruct->stream is always created (not NULL). I don't know logic of the "if (!cusparsestruct->stream)".<br clear="all">
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<div dir="ltr">--Junchao Zhang</div>
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<div dir="ltr" class="gmail_attr">On Mon, Sep 23, 2019 at 5:04 PM Mills, Richard Tran via petsc-dev <<a href="mailto:petsc-dev@mcs.anl.gov" moz-do-not-send="true">petsc-dev@mcs.anl.gov</a>> wrote:<br>
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<div bgcolor="#FFFFFF">In MatMultAdd_SeqAIJCUSPARSE, before Junchao's changes, towards the end of the function it had<br>
<br>
if (!yy) { /* MatMult */<br>
if (!cusparsestruct->stream) {<br>
ierr = WaitForGPU();CHKERRCUDA(ierr);<br>
}<br>
}<br>
<br>
I assume we don't need the logic to do this only in the MatMult() with no add case and should just do this all the time, for the purposes of timing if no other reason. Is there some reason to NOT do this because of worries the about effects that these WaitForGPU()
invocations might have on performance?<br>
<br>
I notice other problems in <a href="http://aijcusparse.cu" target="_blank" moz-do-not-send="true">
aijcusparse.cu</a>, now that I look closer. In MatMultTransposeAdd_SeqAIJCUSPARSE(), I see that we have GPU timing calls around the cusparse_csr_spmv() (but no WaitForGPU() inside the timed region). I believe this is another area in which we get a meaningless
timing. It looks like we need a WaitForGPU() there, and then maybe inside the timed region handling the scatter. (I don't know if this stuff happens asynchronously or not.) But do we potentially want two WaitForGPU() calls in one function, just to help with
getting timings? I don't have a good idea of how much overhead this adds.<br>
<br>
--Richard<br>
<br>
<div>On 9/21/19 12:03 PM, Zhang, Junchao via petsc-dev wrote:<br>
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<div>I made the following changes:</div>
<div>1) In MatMultAdd_SeqAIJCUSPARSE, use this code sequence at the end</div>
<div><font face="monospace"> ierr = WaitForGPU();CHKERRCUDA(ierr);<br>
ierr = PetscLogGpuTimeEnd();CHKERRQ(ierr);<br>
ierr = PetscLogGpuFlops(2.0*a->nz);CHKERRQ(ierr);<br>
PetscFunctionReturn(0);</font></div>
<div>2) In MatMult_MPIAIJCUSPARSE, use the following code sequence. The old code swapped the first two lines. Since with -log_view, MatMultAdd_SeqAIJCUSPARSE is blocking, I changed the order to have better overlap.</div>
<div><font face="monospace"> ierr = VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr);<br>
ierr = (*a->A->ops->mult)(a->A,xx,yy);CHKERRQ(ierr);<br>
ierr = VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr);<br>
ierr = (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);CHKERRQ(ierr);</font></div>
<div>3) Log time directly in the test code so we can also know execution time without -log_view (hence cuda synchronization). I manually calculated the Total Mflop/s for these cases for easy comparison.</div>
<div><br>
</div>
<div><<Note the CPU versions are copied from yesterday's results>></div>
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<div>
<div><font face="monospace">------------------------------------------------------------------------------------------------------------------------<br>
Event Count Time (sec) Flop --- Global --- --- Stage ---- Total GPU - CpuToGpu - - GpuToCpu - GPU<br>
Max Ratio Max Ratio Max Ratio Mess AvgLen Reduct %T %F %M %L %R %T %F %M %L %R Mflop/s Mflop/s Count Size Count Size %F<br>
---------------------------------------------------------------------------------------------------------------------------------------------------------------<br>
</font></div>
<div><font face="monospace">6 MPI ranks,</font></div>
<div><font face="monospace">MatMult 100 1.0 1.1895e+01 1.0 9.63e+09 1.1 2.8e+03 2.2e+05 0.0e+00 24 99 97 18 0 100100100100 0 4743 0 0 0.00e+00 0 0.00e+00 0<br>
VecScatterBegin 100 1.0 4.9145e-02 3.0 0.00e+00 0.0 2.8e+03 2.2e+05 0.0e+00 0 0 97 18 0 0 0100100 0 0 0 0 0.00e+00 0 0.00e+00 0<br>
VecScatterEnd 100 1.0 2.9441e+00 133 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 3 0 0 0 0 13 0 0 0 0 0 0 0 0.00e+00 0 0.00e+00 0</font></div>
</div>
<div><br>
</div>
<div>
<div><span style="font-family:monospace">24 MPI ranks</span><br>
</div>
<div><font face="monospace">MatMult 100 1.0 3.1431e+00 1.0 2.63e+09 1.2 1.9e+04 5.9e+04 0.0e+00 8 99 97 25 0 100100100100 0 17948 0 0 0.00e+00 0 0.00e+00 0<br>
VecScatterBegin 100 1.0 2.0583e-02 2.3 0.00e+00 0.0 1.9e+04 5.9e+04 0.0e+00 0 0 97 25 0 0 0100100 0 0 0 0 0.00e+00 0 0.00e+00 0<br>
VecScatterEnd 100 1.0 1.0639e+0050.0 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 2 0 0 0 0 19 0 0 0 0 0 0 0 0.00e+00 0 0.00e+00 0<br>
</font></div>
<div><br>
</div>
<div><font face="monospace">42 MPI ranks</font></div>
<div><font face="monospace">MatMult 100 1.0 2.0519e+00 1.0 1.52e+09 1.3 3.5e+04 4.1e+04 0.0e+00 23 99 97 30 0 100100100100 0 27493 0 0 0.00e+00 0 0.00e+00 0<br>
VecScatterBegin 100 1.0 2.0971e-02 3.4 0.00e+00 0.0 3.5e+04 4.1e+04 0.0e+00 0 0 97 30 0 1 0100100 0 0 0 0 0.00e+00 0 0.00e+00 0<br>
VecScatterEnd 100 1.0 8.5184e-0162.0 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 6 0 0 0 0 24 0 0 0 0 0 0 0 0.00e+00 0 0.00e+00 0<span><font color="#888888"><br>
</font></span></font></div>
<div><font face="monospace"><br>
</font></div>
<div><span style="font-family:monospace">6 MPI ranks + 6 GPUs + regular SF + log_view</span><font face="monospace"><br>
</font></div>
<div><font face="monospace">MatMult 100 1.0 1.6863e-01 1.0 9.66e+09 1.1 2.8e+03 2.2e+05 0.0e+00 0 99 97 18 0 100100100100 0 335743 629278 100 1.02e+02 100 2.69e+02 100<br>
VecScatterBegin 100 1.0 5.0157e-02 1.6 0.00e+00 0.0 2.8e+03 2.2e+05 0.0e+00 0 0 97 18 0 24 0100100 0 0 0 0 0.00e+00 100 2.69e+02 0<br>
VecScatterEnd 100 1.0 4.9155e-02 2.5 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 20 0 0 0 0 0 0 0 0.00e+00 0 0.00e+00 0<br>
VecCUDACopyTo 100 1.0 9.5078e-03 2.0 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 4 0 0 0 0 0 0 100 1.02e+02 0 0.00e+00 0<br>
VecCopyFromSome 100 1.0 2.8485e-02 1.4 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 14 0 0 0 0 0 0 0 0.00e+00 100 2.69e+02 0<br>
</font></div>
<div><font face="monospace"><br>
</font></div>
<div><font face="monospace">6 MPI ranks + 6 GPUs + regular SF + No log_view<br>
</font></div>
<div><font face="monospace">MatMult: 100 1.0 1.4180e-01 399268 </font></div>
<div><br>
</div>
<div><span style="font-family:monospace">6 MPI ranks + 6 GPUs + CUDA-aware SF + log_view</span><br>
</div>
<div><font face="monospace">MatMult 100 1.0 1.1053e-01 1.0 9.66e+09 1.1 2.8e+03 2.2e+05 0.0e+00 1 99 97 18 0 100100100100 0 512224 642075 0 0.00e+00 0 0.00e+00 100<br>
VecScatterBegin 100 1.0 8.3418e-03 1.5 0.00e+00 0.0 2.8e+03 2.2e+05 0.0e+00 0 0 97 18 0 6 0100100 0 0 0 0 0.00e+00 0 0.00e+00 0<br>
VecScatterEnd 100 1.0 2.2619e-02 1.6 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 16 0 0 0 0 0 0 0 0.00e+00 0 0.00e+00 0<br>
</font></div>
<div><br>
</div>
<div><font face="monospace">6 MPI ranks + 6 GPUs + CUDA-aware SF + No log_view<br>
</font></div>
<div><font face="monospace">MatMult: 100 1.0 9.8344e-02 575717</font><br>
</div>
<div><br>
</div>
<div><font face="monospace">24 MPI ranks + 6 GPUs + regular SF + log_view<br>
</font></div>
<div><font face="monospace">MatMult 100 1.0 1.1572e-01 1.0 2.63e+09 1.2 1.9e+04 5.9e+04 0.0e+00 0 99 97 25 0 100100100100 0 489223 708601 100 4.61e+01 100 6.72e+01 100<br>
VecScatterBegin 100 1.0 2.0641e-02 2.0 0.00e+00 0.0 1.9e+04 5.9e+04 0.0e+00 0 0 97 25 0 13 0100100 0 0 0 0 0.00e+00 100 6.72e+01 0<br>
VecScatterEnd 100 1.0 6.8114e-02 5.6 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 38 0 0 0 0 0 0 0 0.00e+00 0 0.00e+00 0<br>
VecCUDACopyTo 100 1.0 6.6646e-03 2.5 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 3 0 0 0 0 0 0 100 4.61e+01 0 0.00e+00 0<br>
VecCopyFromSome 100 1.0 1.0546e-02 1.7 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 7 0 0 0 0 0 0 0 0.00e+00 100 6.72e+01 0<br>
</font></div>
<div><font face="monospace"><br>
</font></div>
<div><font face="monospace">24 MPI ranks + 6 GPUs + regular SF + No log_view<br>
</font></div>
<div><font face="monospace">MatMult: 100 1.0 9.8254e-02 576201<br>
</font></div>
<div><br>
</div>
<div><font face="monospace">24 MPI ranks + 6 GPUs + CUDA-aware SF + log_view<br>
</font></div>
<div><font face="monospace">MatMult 100 1.0 1.1602e-01 1.0 2.63e+09 1.2 1.9e+04 5.9e+04 0.0e+00 1 99 97 25 0 100100100100 0 487956 707524 0 0.00e+00 0 0.00e+00 100<br>
VecScatterBegin 100 1.0 2.7088e-02 7.0 0.00e+00 0.0 1.9e+04 5.9e+04 0.0e+00 0 0 97 25 0 8 0100100 0 0 0 0 0.00e+00 0 0.00e+00 0<br>
VecScatterEnd 100 1.0 8.4262e-02 3.0 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 1 0 0 0 0 52 0 0 0 0 0 0 0 0.00e+00 0 0.00e+00 0<br>
</font></div>
<div><font face="monospace"><br>
</font></div>
<div>
<div><font face="monospace">24 MPI ranks + 6 GPUs + CUDA-aware SF + No log_view<br>
</font></div>
<div><font face="monospace">MatMult: 100 1.0 1.0397e-01 544510</font></div>
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