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Actually, that's a misunderstanding. The user functions are in C,
Python is just used as a top layer, outside of the optimization (but
I do initialize IPOPT through this interface).<br>
<br>
I'm now running on multiple cores through OpenBLAS, and from what I
understand the ma86 solver accomplishes this through OpenMP. I can
see on the system monitor that all cores are indeed being used,
though it again hasn't had a significant impact on the total
run-time...this does not seem to be where the hold-up was in the
first place.<br>
<br>
Are my expectations unreasonable, and would IPOPT only take a lower
fraction of the run-time for a system requiring more costly function
evaluations?<br>
And what do you mean by those processes taking so much time not
making sense? Is there any chance this is due to me incorrectly
utilizing IPOPT?<br>
<br>
<br>
<div class="moz-cite-prefix">On 09/08/2014 03:41 PM, Tony Kelman
wrote:<br>
</div>
<blockquote cite="mid:EA9948BCAAFF46B9ADFC41503ECF627A@TKsamsung"
type="cite">
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<div>If you’re using PyIpopt, then presumably you’re writing
your function callbacks in Python, which is not exactly a
recipe for speed. According to that timing they’re not
completely negligible, the gradient and Jacobian are taking
almost as much time as LinearSystemFactorization and
LinearSystemBacksolve. I’m surprised to see
UpdateBarrierParameter through CheckConvergence taking that
much time, that doesn’t make much sense.</div>
<div> </div>
<div>In what way are you running on 4 cores? Openblas? MA27
doesn’t even use Blas.</div>
<div> </div>
<div> </div>
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<div style="BACKGROUND: #f5f5f5">
<div style="font-color: black"><b>From:</b> <a
moz-do-not-send="true"
title="jon.herman@colorado.edu"
href="mailto:jon.herman@colorado.edu">Jon Herman</a>
</div>
<div><b>Sent:</b> Monday, September 08, 2014 2:24 PM</div>
<div><b>To:</b> <a moz-do-not-send="true"
title="gregmainland@gmail.com"
href="mailto:gregmainland@gmail.com">Greg Horn</a> ;
<a moz-do-not-send="true"
title="jon.herman@colorado.edu"
href="mailto:jon.herman@colorado.edu">Jon Herman</a>
</div>
<div><b>Cc:</b> <a moz-do-not-send="true"
title="ipopt@list.coin-or.org"
href="mailto:ipopt@list.coin-or.org">ipopt mailing
list</a> </div>
<div><b>Subject:</b> Re: [Ipopt] IPOPT performance (and
impact of BLAS library)</div>
</div>
</div>
<div> </div>
</div>
<div style="FONT-SIZE: small; TEXT-DECORATION: none;
FONT-FAMILY: "Calibri"; FONT-WEIGHT: normal;
COLOR: #000000; FONT-STYLE: normal; DISPLAY: inline">I've
copied below the timing output from one of the moderately
sized examples I've looked at, using ma27. I haven't taken a
look at these outputs before (thanks for the
recommendation!), so I'll study this a little more, but any
thoughts are welcome.<br>
This solves in 130 iterations (142 objective/constraint
evaluations, 131 gradient evaluations)<big>, so about 0.2
CPU seconds per iteration (this is running on 4 cores)</big>.<br>
<br>
Using metis ordering doesn't seem to significantly affect
performance. I haven't tried using ma86 or ma97 with OpenMP
enabled, I'll go and give that a shot.<br>
<br>
For Tony Kelman: what do you mean by "unless my function
evaluations are implemented inefficiently"? At this point
they are a minority of the run-time, so any efficiency there
does not seem to be the problem? Or are you getting at
something else?<br>
<br>
Thank you for the quick responses so far!<br>
<br>
Timing Statistics:<br>
<br>
OverallAlgorithm....................: 26.471 (sys:
0.922 wall: 6.861)<br>
PrintProblemStatistics.............: 0.001 (sys:
0.000 wall: 0.000)<br>
InitializeIterates.................: 0.175 (sys:
0.004 wall: 0.062)<br>
UpdateHessian......................: 0.467 (sys:
0.013 wall: 0.120)<br>
OutputIteration....................: 0.005 (sys:
0.001 wall: 0.002)<br>
UpdateBarrierParameter.............: 8.311 (sys:
0.309 wall: 2.153)<br>
ComputeSearchDirection.............: 6.042 (sys:
0.191 wall: 1.557)<br>
ComputeAcceptableTrialPoint........: 1.658 (sys:
0.059 wall: 0.429)<br>
AcceptTrialPoint...................: 1.943 (sys:
0.063 wall: 0.501)<br>
CheckConvergence...................: 7.860 (sys:
0.282 wall: 2.034)<br>
PDSystemSolverTotal.................: 12.647 (sys:
0.417 wall: 3.264)<br>
PDSystemSolverSolveOnce............: 11.446 (sys:
0.378 wall: 2.954)<br>
ComputeResiduals...................: 0.997 (sys:
0.030 wall: 0.257)<br>
StdAugSystemSolverMultiSolve.......: 10.953 (sys:
0.379 wall: 2.831)<br>
LinearSystemScaling................: 0.000 (sys:
0.000 wall: 0.000)<br>
LinearSystemSymbolicFactorization..: 0.018 (sys:
0.000 wall: 0.005)<br>
LinearSystemFactorization..........: 5.611 (sys:
0.195 wall: 1.451)<br>
LinearSystemBackSolve..............: 4.692 (sys:
0.169 wall: 1.215)<br>
LinearSystemStructureConverter.....: 0.000 (sys:
0.000 wall: 0.000)<br>
LinearSystemStructureConverterInit: 0.000 (sys:
0.000 wall: 0.000)<br>
QualityFunctionSearch...............: 1.581 (sys:
0.077 wall: 0.414)<br>
TryCorrector........................: 0.000 (sys:
0.000 wall: 0.000)<br>
Task1...............................: 0.363 (sys:
0.018 wall: 0.096)<br>
Task2...............................: 0.567 (sys:
0.022 wall: 0.147)<br>
Task3...............................: 0.076 (sys:
0.005 wall: 0.020)<br>
Task4...............................: 0.000 (sys:
0.000 wall: 0.000)<br>
Task5...............................: 0.507 (sys:
0.020 wall: 0.132)<br>
Function Evaluations................: 9.348 (sys:
0.328 wall: 2.417)<br>
Objective function.................: 0.240 (sys:
0.009 wall: 0.062)<br>
Objective function gradient........: 4.316 (sys:
0.150 wall: 1.116)<br>
Equality constraints...............: 0.316 (sys:
0.012 wall: 0.082)<br>
Inequality constraints.............: 0.000 (sys:
0.000 wall: 0.000)<br>
Equality constraint Jacobian.......: 4.477 (sys:
0.157 wall: 1.157)<br>
Inequality constraint Jacobian.....: 0.000 (sys:
0.000 wall: 0.000)<br>
Lagrangian Hessian.................: 0.000 (sys:
0.000 wall: 0.000)<br>
<br>
<br>
<br>
<div class="moz-cite-prefix">On 09/08/2014 03:02 PM, Greg
Horn wrote:<br>
</div>
<blockquote
cite="mid:CAAr-h4um4EE5L8fFw7bsARAiEZte29MYuKjO+nntvYLMSiYdmg@mail.gmail.com"
type="cite">
<div dir="ltr">My usual answer to increasing efficiency is
using HSL (ma86/ma97) with metis ordering and openmp.
How expensive are your function evaluations? What is
your normal time per iteration, and how many iterations
does it take to solve? What sort of problem are you
solving?</div>
<div class="gmail_extra">
<div> </div>
<div class="gmail_quote">On Mon, Sep 8, 2014 at 10:53
PM, Jon Herman <span dir="ltr"><<a
href="mailto:jon.herman@colorado.edu"
target="_blank" moz-do-not-send="true">jon.herman@colorado.edu</a>></span>
wrote:<br>
<blockquote class="gmail_quote" style="PADDING-LEFT:
1ex; MARGIN: 0px 0px 0px 0.8ex; BORDER-LEFT: #ccc
1px solid">
<div text="#000000" bgcolor="#FFFFFF">Hello,<br>
<br>
I am working on implementing IPOPT in a piece of
software that has a need for very good
performance. Unfortunately, it seems that right
now my total run-time is about 80% in IPOPT (that
number excludes the function evaluations, as well
as any time setting up the problem, etc.). For me
to put IPOPT to good use, I'm hoping to make it
run more efficiently, and even out the workload
between IPOPT and the function evaluations,
preferably shifting the work to the function
evaluations as much as possible.<br>
<br>
Originally, I was using the BLAS/LAPACK that can
be installed with IPOPT. In an attempt to improve
performance, I switched to OpenBLAS. To my
confusion, performance did not change at all. This
is leading me to believe that something other than
the BLAS library is dominating the cost. (I am
certain I properly removed the old libraries when
switching BLAS implementation) I'm not sure how to
effectively narrow down where IPOPT is spending
most of it's time, and how to subsequently improve
that performance.<br>
<br>
I've made sure to try the ma27, ma57, ma77, ma86,
ma97, and mumps solvers. Performance varies among
them, but 80% of the time spent in IPOPT is the
best result I achieve (which is typically with
ma27 or ma57, the other solvers are closer to
90%). I've also made sure to try problems as small
as 500 variables and 400 constraints, to as large
as 110 000 variables and 80 000 constraints (and
many points in between those extremes).
Performance is very consistent across that range
(for a given solver), again regardless of the BLAS
library being used. I've been doing this using the
quasi-Newton approximation for the Hessian, which
I was hoping to get away with, but I suppose this
may put a lot of work into IPOPT's side of the
court. I'll also mention that I'm calling IPOPT
through the PyIPOPT module (though I'm expecting
this to create only a small, fixed overhead). <br>
<br>
If you have any thoughts on why IPOPT might be
hogging such a large fraction of my total
run-time, and/or how I could improve this (or
determining if this might be entirely
unavoidable), I would greatly appreciate it! (and
of course I'd be happy to provide additional
information if that would be useful)<br>
<br>
Best regards,<br>
<br>
Jon<br>
</div>
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