[petsc-users] Varying TAO optimization solve iterations using BLMVM
Justin Chang
jychang48 at gmail.com
Thu Jun 25 11:42:53 CDT 2015
Jason,
I was experimenting with some smaller steady-state problems and I still get
the same issue: every time I run the same problem on the same number of
processors the number of iterations differs but the solutions remain the
same. I think this is the root to why I get such erratic behavior in
iterations in the transient case. Attached are the log files for the two
runs I did.
Is it possible to tell what's going on from these? Or should I reinstall
PETSc? Because I only noticed this problem once I switched over to the
master version of 3.6 (although it's possible I might have screwed up the
installation a bit because I had to scp everything due to the firewall). I
can send you the example code and working files if needed
Thanks,
Justin
On Thu, Jun 25, 2015 at 9:51 AM, Jason Sarich <jason.sarich at gmail.com>
wrote:
> Hi Justin,
>
> I don't see anything obviously wrong that would be causing this variation
> in iterations due to number of processors. Is it at all feasible to send be
> an example code that reproduces the problem (perhaps a smaller version)?
> I'm still guessing the problem lies in numerical precision, it would be
> nice to find a way to avoid them. I don't think the job scheduling or
> compute nodes would affect this.
>
>> Basically I call TaoSolve at each time level. What I find strange is
>> that the number of TAO solve iterations vary at each time level for a given
>> number of processors
>
> Just for clarification, do you mean that for a given problem, you run the
> same problem several times the only difference being the number of
> processors, and that on each time step you get (close to) the same solution
> for each run, just with a different number of TAO iterations?
>
> If you run the same problem twice using the same number of processors, is
> the output identical? No OpenMP threads or GPU's?
>
>
>> 2) Sometimes, I get Tao Termination reason of -5, and from what I see
>> from the online documentation, it means the number of function evaluations
>> exceeds the maximum number of function evaluations. I only get this at
>> certain time levels, and it also varies when I change the number of
>> processors.
>
>
> This looks like the same issue, unless the number of iterations is hugely
> different (say it converges on some number of processors after 200
> iterations, but still hasn't converged after 2000 on a different number).
>
>
> Jason
>
>
>
>
> On Tue, Jun 23, 2015 at 6:36 PM, Justin Chang <jychang48 at gmail.com> wrote:
>
>> I was unable to do quad precision or even with 64 bit integers
>> because my data files rely on intricate binary files that have been written
>> in 32 bit.
>>
>> However, I noticed a couple things which are puzzling to me:
>>
>> 1) I am solving a transient problem using my own backward euler
>> function. Basically I call TaoSolve at each time level. What I find strange
>> is that the number of TAO solve iterations vary at each time level for a
>> given number of processors. The solution is roughly the same when I change
>> the number of processors. Any idea why this is happening, or might this
>> have more to do with the job scheduling/compute nodes on my HPC machine?
>>
>> 2) Sometimes, I get Tao Termination reason of -5, and from what I see
>> from the online documentation, it means the number of function evaluations
>> exceeds the maximum number of function evaluations. I only get this at
>> certain time levels, and it also varies when I change the number of
>> processors.
>>
>> I can understand the number of iterations going down the further in time
>> i go (this is due to the nature of my problem), but I am not sure why the
>> above two observations are happening. Any thoughts?
>>
>> Thanks,
>> Justin
>>
>> On Fri, Jun 19, 2015 at 11:52 AM, Justin Chang <jychang48 at gmail.com>
>> wrote:
>>
>>> My code sort of requires HDF5 so installing quad precision might be a
>>> little difficult. I could try to work around this but that might take some
>>> effort.
>>>
>>> In the mean time, is there any other potential explanation or
>>> alternative to figuring this out?
>>>
>>> Thanks,
>>> Justin
>>>
>>>
>>> On Thursday, June 18, 2015, Matthew Knepley <knepley at gmail.com> wrote:
>>>
>>>> On Thu, Jun 18, 2015 at 1:52 PM, Jason Sarich <jason.sarich at gmail.com>
>>>> wrote:
>>>>
>>>>> BLMVM doesn't use a KSP or preconditioner, it updates using the
>>>>> L-BFGS-B formula
>>>>>
>>>>
>>>> Then this sounds like a bug, unless one of the constants is partition
>>>> dependent.
>>>>
>>>> Matt
>>>>
>>>>
>>>>> On Thu, Jun 18, 2015 at 1:45 PM, Matthew Knepley <knepley at gmail.com>
>>>>> wrote:
>>>>>
>>>>>> On Thu, Jun 18, 2015 at 12:15 PM, Jason Sarich <
>>>>>> jason.sarich at gmail.com> wrote:
>>>>>>
>>>>>>> Hi Justin,
>>>>>>>
>>>>>>> I can't tell for sure why this is happening, have you tried using
>>>>>>> quad precision to make sure that numerical cutoffs isn't the problem?
>>>>>>>
>>>>>>> 1 The Hessian being approximate and the resulting implicit
>>>>>>> computation is the source of the cutoff, but would not be causing different
>>>>>>> convergence rates in infinite precision.
>>>>>>>
>>>>>>> 2 the local size may affect load balancing but not the resulting
>>>>>>> norms/convergence rate.
>>>>>>>
>>>>>>
>>>>>> This sounds to be like the preconditioner is dependent on the
>>>>>> partition. Can you send -tao_view -snes_view
>>>>>>
>>>>>> Matt
>>>>>>
>>>>>>
>>>>>>> Jason
>>>>>>>
>>>>>>>
>>>>>>> On Thu, Jun 18, 2015 at 10:44 AM, Justin Chang <jychang48 at gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> I solved a transient diffusion across multiple cores using TAO
>>>>>>>> BLMVM. When I simulate the same problem but on different numbers of
>>>>>>>> processing cores, the number of solve iterations change quite drastically.
>>>>>>>> The numerical solution is the same, but these changes are quite vast. I
>>>>>>>> attached a PDF showing a comparison between KSP and TAO. KSP remains
>>>>>>>> largely invariant with number of processors but TAO (with bounded
>>>>>>>> constraints) fluctuates.
>>>>>>>>
>>>>>>>> My question is, why is this happening? I understand that
>>>>>>>> accumulation of numerical round-offs may attribute to this, but the
>>>>>>>> differences seem quite vast to me. My initial thought was that
>>>>>>>>
>>>>>>>> 1) the Hessian is only projected and not explicitly computed,
>>>>>>>> which may have something to do with the rate of convergence
>>>>>>>>
>>>>>>>> 2) local problem size. Certain regions of my domain have different
>>>>>>>> number of "violations" which need to be corrected by the bounded
>>>>>>>> constraints so the rate of convergence depends on how these regions are
>>>>>>>> partitioned?
>>>>>>>>
>>>>>>>> Any thoughts?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Justin
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>> --
>>>>>> What most experimenters take for granted before they begin their
>>>>>> experiments is infinitely more interesting than any results to which their
>>>>>> experiments lead.
>>>>>> -- Norbert Wiener
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>> --
>>>> What most experimenters take for granted before they begin their
>>>> experiments is infinitely more interesting than any results to which their
>>>> experiments lead.
>>>> -- Norbert Wiener
>>>>
>>>
>>
>
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MESHID = 4
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16 processors:
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TSTEP ANALYSIS TIME ITER FLOPS/s
iter = 0, Function value: -1.57703e-07, Residual: 0.239098
iter = 1, Function value: -4.53878e-07, Residual: 0.223304
iter = 2, Function value: -7.89606e-06, Residual: 0.221146
iter = 3, Function value: -0.0808297, Residual: 0.225876
iter = 4, Function value: -1.67847, Residual: 0.351701
iter = 5, Function value: -3.2307, Residual: 0.18377
iter = 6, Function value: -3.80321, Residual: 0.168797
iter = 7, Function value: -4.25223, Residual: 0.208515
iter = 8, Function value: -4.54546, Residual: 0.211522
iter = 9, Function value: -4.76263, Residual: 0.121566
iter = 10, Function value: -4.93278, Residual: 0.11454
iter = 11, Function value: -5.1385, Residual: 0.149913
iter = 12, Function value: -5.3172, Residual: 0.188699
iter = 13, Function value: -5.35404, Residual: 0.414234
iter = 14, Function value: -5.46775, Residual: 0.166872
iter = 15, Function value: -5.5088, Residual: 0.109606
iter = 16, Function value: -5.55735, Residual: 0.137697
iter = 17, Function value: -5.61175, Residual: 0.20436
iter = 18, Function value: -5.67254, Residual: 0.145789
iter = 19, Function value: -5.7428, Residual: 0.131282
iter = 20, Function value: -5.78678, Residual: 0.168675
iter = 21, Function value: -5.83482, Residual: 0.107592
iter = 22, Function value: -5.88877, Residual: 0.120863
iter = 23, Function value: -5.94162, Residual: 0.170501
iter = 24, Function value: -5.99103, Residual: 0.137338
iter = 25, Function value: -6.03433, Residual: 0.11123
iter = 26, Function value: -6.08494, Residual: 0.123001
iter = 27, Function value: -6.12432, Residual: 0.158457
iter = 28, Function value: -6.16801, Residual: 0.100757
iter = 29, Function value: -6.21675, Residual: 0.100482
iter = 30, Function value: -6.25439, Residual: 0.150756
iter = 31, Function value: -6.29508, Residual: 0.10085
iter = 32, Function value: -6.33253, Residual: 0.0966374
iter = 33, Function value: -6.36431, Residual: 0.103262
iter = 34, Function value: -6.40181, Residual: 0.102187
iter = 35, Function value: -6.44427, Residual: 0.121893
iter = 36, Function value: -6.48004, Residual: 0.0983186
iter = 37, Function value: -6.51506, Residual: 0.092832
iter = 38, Function value: -6.5519, Residual: 0.116683
iter = 39, Function value: -6.58292, Residual: 0.116904
iter = 40, Function value: -6.60971, Residual: 0.0771122
iter = 41, Function value: -6.63585, Residual: 0.0757516
iter = 42, Function value: -6.6586, Residual: 0.10885
iter = 43, Function value: -6.68587, Residual: 0.0752019
iter = 44, Function value: -6.7155, Residual: 0.0769993
iter = 45, Function value: -6.7459, Residual: 0.1128
iter = 46, Function value: -6.77436, Residual: 0.0874185
iter = 47, Function value: -6.8015, Residual: 0.0772962
iter = 48, Function value: -6.82934, Residual: 0.0894704
iter = 49, Function value: -6.85383, Residual: 0.0930334
iter = 50, Function value: -6.87712, Residual: 0.0707267
iter = 51, Function value: -6.90227, Residual: 0.0704031
iter = 52, Function value: -6.92095, Residual: 0.11036
iter = 53, Function value: -6.94187, Residual: 0.0625794
iter = 54, Function value: -6.96158, Residual: 0.0585309
iter = 55, Function value: -6.98111, Residual: 0.07778
iter = 56, Function value: -7.00425, Residual: 0.0917501
iter = 57, Function value: -7.02495, Residual: 0.0680073
iter = 58, Function value: -7.04571, Residual: 0.0632846
iter = 59, Function value: -7.06812, Residual: 0.067345
iter = 60, Function value: -7.08734, Residual: 0.111259
iter = 61, Function value: -7.10785, Residual: 0.062663
iter = 62, Function value: -7.12178, Residual: 0.0546556
iter = 63, Function value: -7.13834, Residual: 0.0661359
iter = 64, Function value: -7.15288, Residual: 0.0977186
iter = 65, Function value: -7.16979, Residual: 0.0558807
iter = 66, Function value: -7.18517, Residual: 0.0514965
iter = 67, Function value: -7.19956, Residual: 0.0566699
iter = 68, Function value: -7.21503, Residual: 0.102862
iter = 69, Function value: -7.23295, Residual: 0.0520616
iter = 70, Function value: -7.24472, Residual: 0.0514462
iter = 71, Function value: -7.2607, Residual: 0.0603313
iter = 72, Function value: -7.27322, Residual: 0.114154
iter = 73, Function value: -7.29073, Residual: 0.0553255
iter = 74, Function value: -7.30301, Residual: 0.0468704
iter = 75, Function value: -7.31535, Residual: 0.0503862
iter = 76, Function value: -7.32483, Residual: 0.121667
iter = 77, Function value: -7.34239, Residual: 0.0473323
iter = 78, Function value: -7.35051, Residual: 0.0402786
iter = 79, Function value: -7.36252, Residual: 0.0513142
iter = 80, Function value: -7.37578, Residual: 0.0980691
iter = 81, Function value: -7.39103, Residual: 0.0561799
iter = 82, Function value: -7.40188, Residual: 0.0420525
iter = 83, Function value: -7.41352, Residual: 0.0506624
iter = 84, Function value: -7.42311, Residual: 0.0745317
iter = 85, Function value: -7.43349, Residual: 0.0418963
iter = 86, Function value: -7.44304, Residual: 0.0427164
iter = 87, Function value: -7.45309, Residual: 0.050941
iter = 88, Function value: -7.46557, Residual: 0.0855721
iter = 89, Function value: -7.47897, Residual: 0.0443531
iter = 90, Function value: -7.48731, Residual: 0.038289
iter = 91, Function value: -7.49667, Residual: 0.0433084
iter = 92, Function value: -7.50404, Residual: 0.0748006
iter = 93, Function value: -7.51295, Residual: 0.0379092
iter = 94, Function value: -7.52065, Residual: 0.0360034
iter = 95, Function value: -7.52815, Residual: 0.0407615
iter = 96, Function value: -7.53624, Residual: 0.0871539
iter = 97, Function value: -7.54733, Residual: 0.0370463
iter = 98, Function value: -7.55263, Residual: 0.0321752
iter = 99, Function value: -7.5611, Residual: 0.0380701
iter = 100, Function value: -7.56636, Residual: 0.0828749
iter = 101, Function value: -7.57523, Residual: 0.0364468
iter = 102, Function value: -7.58065, Residual: 0.0296879
iter = 103, Function value: -7.58617, Residual: 0.0331854
iter = 104, Function value: -7.59286, Residual: 0.0653273
iter = 105, Function value: -7.60073, Residual: 0.0311378
iter = 106, Function value: -7.605, Residual: 0.0271273
iter = 107, Function value: -7.61161, Residual: 0.0352389
iter = 108, Function value: -7.61636, Residual: 0.0633698
iter = 109, Function value: -7.62264, Residual: 0.0323803
iter = 110, Function value: -7.62822, Residual: 0.0286247
iter = 111, Function value: -7.63278, Residual: 0.0310643
iter = 112, Function value: -7.63766, Residual: 0.0695965
iter = 113, Function value: -7.64471, Residual: 0.0273006
iter = 114, Function value: -7.6477, Residual: 0.0241679
iter = 115, Function value: -7.65299, Residual: 0.0287846
iter = 116, Function value: -7.65608, Residual: 0.0686328
iter = 117, Function value: -7.66196, Residual: 0.029546
iter = 118, Function value: -7.66562, Residual: 0.022733
iter = 119, Function value: -7.6691, Residual: 0.0260237
iter = 120, Function value: -7.67366, Residual: 0.0518384
iter = 121, Function value: -7.67882, Residual: 0.0250519
iter = 122, Function value: -7.68159, Residual: 0.0208822
iter = 123, Function value: -7.68604, Residual: 0.0283777
iter = 124, Function value: -7.68918, Residual: 0.0502209
iter = 125, Function value: -7.69338, Residual: 0.026466
iter = 126, Function value: -7.69725, Residual: 0.0232737
iter = 127, Function value: -7.70029, Residual: 0.0246071
iter = 128, Function value: -7.70333, Residual: 0.0560219
iter = 129, Function value: -7.70795, Residual: 0.0219018
iter = 130, Function value: -7.70988, Residual: 0.0192943
iter = 131, Function value: -7.71333, Residual: 0.0223829
iter = 132, Function value: -7.71539, Residual: 0.0571394
iter = 133, Function value: -7.71939, Residual: 0.0241063
iter = 134, Function value: -7.72174, Residual: 0.0183287
iter = 135, Function value: -7.72395, Residual: 0.0206927
iter = 136, Function value: -7.7272, Residual: 0.0366094
iter = 137, Function value: -7.73034, Residual: 0.0229168
iter = 138, Function value: -7.73217, Residual: 0.0165316
iter = 139, Function value: -7.73523, Residual: 0.0207809
iter = 140, Function value: -7.7372, Residual: 0.0344397
iter = 141, Function value: -7.73964, Residual: 0.0197955
iter = 142, Function value: -7.74225, Residual: 0.0175415
iter = 143, Function value: -7.74421, Residual: 0.0204096
iter = 144, Function value: -7.74642, Residual: 0.0325727
iter = 145, Function value: -7.74861, Residual: 0.0168286
iter = 146, Function value: -7.74991, Residual: 0.0166282
iter = 147, Function value: -7.75202, Residual: 0.0187187
iter = 148, Function value: -7.75333, Residual: 0.043491
iter = 149, Function value: -7.75579, Residual: 0.0163257
iter = 150, Function value: -7.75685, Residual: 0.013081
iter = 151, Function value: -7.75808, Residual: 0.0146514
iter = 152, Function value: -7.75972, Residual: 0.0272383
iter = 153, Function value: -7.76143, Residual: 0.0146716
iter = 154, Function value: -7.76245, Residual: 0.012202
iter = 155, Function value: -7.76402, Residual: 0.0165763
iter = 156, Function value: -7.76485, Residual: 0.0264216
iter = 157, Function value: -7.76596, Residual: 0.0141675
iter = 158, Function value: -7.76711, Residual: 0.0118525
iter = 159, Function value: -7.76795, Residual: 0.0132682
iter = 160, Function value: -7.76906, Residual: 0.0307988
iter = 161, Function value: -7.77046, Residual: 0.0136723
iter = 162, Function value: -7.77101, Residual: 0.0105228
iter = 163, Function value: -7.77204, Residual: 0.0114438
iter = 164, Function value: -7.77257, Residual: 0.0246093
iter = 165, Function value: -7.77345, Residual: 0.0125104
iter = 166, Function value: -7.77417, Residual: 0.0102181
iter = 167, Function value: -7.77474, Residual: 0.0109432
iter = 168, Function value: -7.77532, Residual: 0.0199891
iter = 169, Function value: -7.77611, Residual: 0.0101163
iter = 170, Function value: -7.77655, Residual: 0.0105442
iter = 171, Function value: -7.77743, Residual: 0.0116992
iter = 172, Function value: -7.77779, Residual: 0.0293656
iter = 173, Function value: -7.77878, Residual: 0.0112078
iter = 174, Function value: -7.77919, Residual: 0.00808034
iter = 175, Function value: -7.77957, Residual: 0.00902743
iter = 176, Function value: -7.78023, Residual: 0.0122209
iter = 177, Function value: -7.78068, Residual: 0.0174619
iter = 178, Function value: -7.78127, Residual: 0.00842028
iter = 179, Function value: -7.78167, Residual: 0.00912139
iter = 180, Function value: -7.78215, Residual: 0.0100989
iter = 181, Function value: -7.78253, Residual: 0.0198295
iter = 182, Function value: -7.78312, Residual: 0.00877936
iter = 183, Function value: -7.78342, Residual: 0.00805744
iter = 184, Function value: -7.78381, Residual: 0.00884593
iter = 185, Function value: -7.78426, Residual: 0.0172994
iter = 186, Function value: -7.78476, Residual: 0.00859095
iter = 187, Function value: -7.78503, Residual: 0.00730765
iter = 188, Function value: -7.78543, Residual: 0.00819728
iter = 189, Function value: -7.7857, Residual: 0.0163602
iter = 190, Function value: -7.78611, Residual: 0.00840991
iter = 191, Function value: -7.78641, Residual: 0.00748283
iter = 192, Function value: -7.7867, Residual: 0.00842632
iter = 193, Function value: -7.78717, Residual: 0.0143767
iter = 194, Function value: -7.78759, Residual: 0.00972408
iter = 195, Function value: -7.78783, Residual: 0.00682761
iter = 196, Function value: -7.78815, Residual: 0.00741374
iter = 197, Function value: -7.7884, Residual: 0.00941704
iter = 198, Function value: -7.78875, Residual: 0.00770444
iter = 199, Function value: -7.78917, Residual: 0.0085247
iter = 200, Function value: -7.78949, Residual: 0.0119359
iter = 201, Function value: -7.7898, Residual: 0.00737882
iter = 202, Function value: -7.79006, Residual: 0.00692514
iter = 203, Function value: -7.79031, Residual: 0.00784755
iter = 204, Function value: -7.79062, Residual: 0.0133266
iter = 205, Function value: -7.79097, Residual: 0.00759952
iter = 206, Function value: -7.7912, Residual: 0.00672236
iter = 207, Function value: -7.79144, Residual: 0.00698928
iter = 208, Function value: -7.79165, Residual: 0.0137271
iter = 209, Function value: -7.79195, Residual: 0.00704828
iter = 210, Function value: -7.79218, Residual: 0.00665079
iter = 211, Function value: -7.79241, Residual: 0.00752568
iter = 212, Function value: -7.79273, Residual: 0.0135994
iter = 213, Function value: -7.79307, Residual: 0.00727429
iter = 214, Function value: -7.79324, Residual: 0.00610776
iter = 215, Function value: -7.79355, Residual: 0.00704834
iter = 216, Function value: -7.79371, Residual: 0.0129413
iter = 217, Function value: -7.79398, Residual: 0.00699312
iter = 218, Function value: -7.79422, Residual: 0.00615925
iter = 219, Function value: -7.79442, Residual: 0.00670629
iter = 220, Function value: -7.79466, Residual: 0.0129022
iter = 221, Function value: -7.79497, Residual: 0.00653843
iter = 222, Function value: -7.79512, Residual: 0.00595371
iter = 223, Function value: -7.79543, Residual: 0.00677522
iter = 224, Function value: -7.79557, Residual: 0.0157612
iter = 225, Function value: -7.79589, Residual: 0.00704513
iter = 226, Function value: -7.79609, Residual: 0.00558273
iter = 227, Function value: -7.79628, Residual: 0.00635017
iter = 228, Function value: -7.79656, Residual: 0.0100848
iter = 229, Function value: -7.79683, Residual: 0.00716537
iter = 230, Function value: -7.79701, Residual: 0.00566117
iter = 231, Function value: -7.79735, Residual: 0.00681235
iter = 232, Function value: -7.79758, Residual: 0.0114013
iter = 233, Function value: -7.79786, Residual: 0.00690156
iter = 234, Function value: -7.79816, Residual: 0.00560736
iter = 235, Function value: -7.79836, Residual: 0.00676259
iter = 236, Function value: -7.79867, Residual: 0.00733547
iter = 237, Function value: -7.79891, Residual: 0.00691113
iter = 238, Function value: -7.79917, Residual: 0.00634128
iter = 239, Function value: -7.79947, Residual: 0.00755322
iter = 240, Function value: -7.79971, Residual: 0.00881029
iter = 241, Function value: -7.79994, Residual: 0.00591148
iter = 242, Function value: -7.80019, Residual: 0.00531922
iter = 243, Function value: -7.80038, Residual: 0.00782399
iter = 244, Function value: -7.80062, Residual: 0.00560367
iter = 245, Function value: -7.80087, Residual: 0.00646445
iter = 246, Function value: -7.8011, Residual: 0.00669491
iter = 247, Function value: -7.8013, Residual: 0.00542145
iter = 248, Function value: -7.80156, Residual: 0.00646472
iter = 249, Function value: -7.80176, Residual: 0.00831322
iter = 250, Function value: -7.80195, Residual: 0.00593352
iter = 251, Function value: -7.80221, Residual: 0.00510617
iter = 252, Function value: -7.80237, Residual: 0.00726524
iter = 253, Function value: -7.80255, Residual: 0.00561318
iter = 254, Function value: -7.80277, Residual: 0.00598889
iter = 255, Function value: -7.80293, Residual: 0.00663841
iter = 256, Function value: -7.80309, Residual: 0.00557977
iter = 257, Function value: -7.80339, Residual: 0.00662109
iter = 258, Function value: -7.80351, Residual: 0.0103698
iter = 259, Function value: -7.80369, Residual: 0.00547099
iter = 260, Function value: -7.80385, Residual: 0.00412179
iter = 261, Function value: -7.80398, Residual: 0.00469965
iter = 262, Function value: -7.80418, Residual: 0.00800895
iter = 263, Function value: -7.80436, Residual: 0.00682549
iter = 264, Function value: -7.8045, Residual: 0.00448242
iter = 265, Function value: -7.8047, Residual: 0.00449525
iter = 266, Function value: -7.80483, Residual: 0.00623266
iter = 267, Function value: -7.80499, Residual: 0.00432503
iter = 268, Function value: -7.80516, Residual: 0.00443986
iter = 269, Function value: -7.80527, Residual: 0.00654341
iter = 270, Function value: -7.8054, Residual: 0.00403316
iter = 271, Function value: -7.80557, Residual: 0.0042036
iter = 272, Function value: -7.8057, Residual: 0.00486939
iter = 273, Function value: -7.80582, Residual: 0.00914879
iter = 274, Function value: -7.80599, Residual: 0.00371046
iter = 275, Function value: -7.80606, Residual: 0.00343322
iter = 276, Function value: -7.80617, Residual: 0.00408214
iter = 277, Function value: -7.80626, Residual: 0.00960308
iter = 278, Function value: -7.80641, Residual: 0.00403028
iter = 279, Function value: -7.8065, Residual: 0.00326197
iter = 280, Function value: -7.80658, Residual: 0.00364705
iter = 281, Function value: -7.8067, Residual: 0.00772842
iter = 282, Function value: -7.80684, Residual: 0.00352321
iter = 283, Function value: -7.80691, Residual: 0.00312168
iter = 284, Function value: -7.80703, Residual: 0.00413708
iter = 285, Function value: -7.8071, Residual: 0.00844741
iter = 286, Function value: -7.80722, Residual: 0.00387447
iter = 287, Function value: -7.80731, Residual: 0.00321656
iter = 288, Function value: -7.80739, Residual: 0.0035753
iter = 289, Function value: -7.8075, Residual: 0.00709712
iter = 290, Function value: -7.80762, Residual: 0.0038076
iter = 291, Function value: -7.80768, Residual: 0.00276752
iter = 292, Function value: -7.80778, Residual: 0.00341213
iter = 293, Function value: -7.80784, Residual: 0.00630934
iter = 294, Function value: -7.80793, Residual: 0.00369558
iter = 295, Function value: -7.80804, Residual: 0.00316572
iter = 296, Function value: -7.80811, Residual: 0.00341499
iter = 297, Function value: -7.80817, Residual: 0.00733733
iter = 298, Function value: -7.80828, Residual: 0.00290179
iter = 299, Function value: -7.80832, Residual: 0.002659
iter = 300, Function value: -7.80841, Residual: 0.00325143
iter = 301, Function value: -7.80846, Residual: 0.00851133
iter = 302, Function value: -7.80856, Residual: 0.00338366
iter = 303, Function value: -7.80862, Residual: 0.00242721
iter = 304, Function value: -7.80866, Residual: 0.00265834
iter = 305, Function value: -7.80874, Residual: 0.00468424
iter = 306, Function value: -7.80881, Residual: 0.00278952
iter = 307, Function value: -7.80886, Residual: 0.0024051
iter = 308, Function value: -7.80896, Residual: 0.00336636
iter = 309, Function value: -7.809, Residual: 0.00684994
iter = 310, Function value: -7.80907, Residual: 0.00284831
iter = 311, Function value: -7.80912, Residual: 0.0021039
iter = 312, Function value: -7.80915, Residual: 0.0024576
iter = 313, Function value: -7.80923, Residual: 0.00351195
iter = 314, Function value: -7.80925, Residual: 0.00653157
iter = 315, Function value: -7.80933, Residual: 0.00221326
iter = 316, Function value: -7.80935, Residual: 0.00193244
iter = 317, Function value: -7.8094, Residual: 0.00236798
iter = 318, Function value: -7.80944, Residual: 0.00544877
iter = 319, Function value: -7.80949, Residual: 0.00242602
iter = 320, Function value: -7.80952, Residual: 0.00187275
iter = 321, Function value: -7.80955, Residual: 0.00208739
iter = 322, Function value: -7.80959, Residual: 0.00369487
iter = 323, Function value: -7.80963, Residual: 0.00210818
iter = 324, Function value: -7.80965, Residual: 0.00171339
iter = 325, Function value: -7.8097, Residual: 0.00224769
iter = 326, Function value: -7.80972, Residual: 0.00409731
iter = 327, Function value: -7.80975, Residual: 0.00205957
iter = 328, Function value: -7.80978, Residual: 0.00166663
iter = 329, Function value: -7.8098, Residual: 0.00177559
iter = 330, Function value: -7.80984, Residual: 0.00401932
iter = 331, Function value: -7.80987, Residual: 0.0019732
iter = 332, Function value: -7.80988, Residual: 0.00146904
iter = 333, Function value: -7.80991, Residual: 0.00156285
iter = 334, Function value: -7.80993, Residual: 0.00366378
iter = 335, Function value: -7.80996, Residual: 0.00182864
iter = 336, Function value: -7.80998, Residual: 0.00143332
iter = 337, Function value: -7.80999, Residual: 0.00153632
iter = 338, Function value: -7.81001, Residual: 0.00300152
iter = 339, Function value: -7.81003, Residual: 0.00158852
iter = 340, Function value: -7.81005, Residual: 0.00139549
iter = 341, Function value: -7.81007, Residual: 0.0015773
iter = 342, Function value: -7.81008, Residual: 0.00381815
iter = 343, Function value: -7.8101, Residual: 0.00154118
iter = 344, Function value: -7.81011, Residual: 0.00110395
iter = 345, Function value: -7.81012, Residual: 0.00129509
iter = 346, Function value: -7.81014, Residual: 0.00187921
iter = 347, Function value: -7.81016, Residual: 0.00235816
iter = 348, Function value: -7.81017, Residual: 0.00112299
iter = 349, Function value: -7.81018, Residual: 0.00122119
iter = 350, Function value: -7.81019, Residual: 0.00136949
iter = 351, Function value: -7.81021, Residual: 0.00242993
iter = 352, Function value: -7.81022, Residual: 0.00113653
iter = 353, Function value: -7.81023, Residual: 0.00119118
iter = 354, Function value: -7.81024, Residual: 0.00131839
iter = 355, Function value: -7.81025, Residual: 0.00344071
iter = 356, Function value: -7.81027, Residual: 0.00107127
iter = 357, Function value: -7.81027, Residual: 0.000881068
iter = 358, Function value: -7.81028, Residual: 0.00103876
iter = 359, Function value: -7.81029, Residual: 0.00168859
iter = 360, Function value: -7.8103, Residual: 0.00128558
iter = 361, Function value: -7.81031, Residual: 0.000936338
iter = 362, Function value: -7.81032, Residual: 0.00112771
iter = 363, Function value: -7.81033, Residual: 0.00198573
iter = 364, Function value: -7.81034, Residual: 0.00111927
iter = 365, Function value: -7.81035, Residual: 0.000968416
iter = 366, Function value: -7.81036, Residual: 0.00106383
iter = 367, Function value: -7.81037, Residual: 0.00255311
iter = 368, Function value: -7.81038, Residual: 0.00109532
iter = 369, Function value: -7.81038, Residual: 0.000799371
iter = 370, Function value: -7.81039, Residual: 0.000852602
iter = 371, Function value: -7.8104, Residual: 0.00198778
iter = 372, Function value: -7.81041, Residual: 0.00113394
iter = 373, Function value: -7.81042, Residual: 0.000915895
iter = 374, Function value: -7.81043, Residual: 0.000993134
iter = 375, Function value: -7.81043, Residual: 0.00201892
iter = 376, Function value: -7.81044, Residual: 0.000831109
iter = 377, Function value: -7.81045, Residual: 0.000884957
iter = 378, Function value: -7.81045, Residual: 0.00110069
iter = 379, Function value: -7.81046, Residual: 0.0024113
iter = 380, Function value: -7.81047, Residual: 0.000842735
iter = 381, Function value: -7.81048, Residual: 0.000673622
iter = 382, Function value: -7.81048, Residual: 0.0007883
iter = 383, Function value: -7.81049, Residual: 0.00169046
iter = 384, Function value: -7.81049, Residual: 0.000872412
iter = 385, Function value: -7.8105, Residual: 0.000738495
iter = 386, Function value: -7.81051, Residual: 0.00109419
iter = 387, Function value: -7.81051, Residual: 0.00127752
iter = 388, Function value: -7.81052, Residual: 0.000781728
iter = 389, Function value: -7.81052, Residual: 0.000802141
iter = 390, Function value: -7.81053, Residual: 0.000918364
iter = 391, Function value: -7.81054, Residual: 0.00194135
iter = 392, Function value: -7.81054, Residual: 0.000837812
iter = 393, Function value: -7.81055, Residual: 0.000640064
iter = 394, Function value: -7.81055, Residual: 0.000663732
iter = 395, Function value: -7.81056, Residual: 0.00144857
iter = 396, Function value: -7.81056, Residual: 0.000820515
iter = 397, Function value: -7.81057, Residual: 0.00068299
iter = 398, Function value: -7.81057, Residual: 0.000751018
iter = 399, Function value: -7.81057, Residual: 0.00162709
iter = 400, Function value: -7.81058, Residual: 0.000692016
iter = 401, Function value: -7.81058, Residual: 0.000676616
iter = 402, Function value: -7.81059, Residual: 0.000839196
iter = 403, Function value: -7.81059, Residual: 0.00192361
iter = 404, Function value: -7.8106, Residual: 0.000734566
iter = 405, Function value: -7.8106, Residual: 0.000552234
iter = 406, Function value: -7.8106, Residual: 0.000666441
iter = 407, Function value: -7.81061, Residual: 0.00110812
iter = 408, Function value: -7.81061, Residual: 0.000805687
iter = 409, Function value: -7.81062, Residual: 0.000563736
iter = 410, Function value: -7.81062, Residual: 0.000747683
iter = 411, Function value: -7.81062, Residual: 0.00109469
iter = 412, Function value: -7.81063, Residual: 0.000705128
iter = 413, Function value: -7.81063, Residual: 0.000617784
iter = 414, Function value: -7.81064, Residual: 0.00119265
iter = 415, Function value: -7.81064, Residual: 0.000626528
iter = 416, Function value: -7.81064, Residual: 0.000587671
iter = 417, Function value: -7.81065, Residual: 0.00068946
iter = 418, Function value: -7.81065, Residual: 0.00129254
iter = 419, Function value: -7.81065, Residual: 0.000927012
iter = 420, Function value: -7.81066, Residual: 0.000498195
iter = 421, Function value: -7.81066, Residual: 0.000540408
iter = 422, Function value: -7.81066, Residual: 0.000728311
iter = 423, Function value: -7.81067, Residual: 0.00109958
iter = 424, Function value: -7.81067, Residual: 0.000615443
iter = 425, Function value: -7.81067, Residual: 0.000579832
iter = 426, Function value: -7.81068, Residual: 0.000682068
iter = 427, Function value: -7.81068, Residual: 0.000860703
iter = 428, Function value: -7.81068, Residual: 0.000559616
iter = 429, Function value: -7.81069, Residual: 0.000618818
iter = 430, Function value: -7.81069, Residual: 0.000794079
iter = 431, Function value: -7.81069, Residual: 0.00073984
iter = 432, Function value: -7.8107, Residual: 0.000747683
iter = 433, Function value: -7.8107, Residual: 0.000616185
iter = 434, Function value: -7.8107, Residual: 0.000693478
iter = 435, Function value: -7.81071, Residual: 0.000640444
iter = 436, Function value: -7.81071, Residual: 0.000620902
iter = 437, Function value: -7.81072, Residual: 0.000854389
iter = 438, Function value: -7.81072, Residual: 0.00106588
iter = 439, Function value: -7.81072, Residual: 0.000556506
iter = 440, Function value: -7.81072, Residual: 0.000495603
iter = 441, Function value: -7.81073, Residual: 0.000674669
iter = 442, Function value: -7.81073, Residual: 0.00103042
iter = 443, Function value: -7.81073, Residual: 0.000655499
iter = 444, Function value: -7.81074, Residual: 0.000607126
iter = 445, Function value: -7.81074, Residual: 0.000705519
iter = 446, Function value: -7.81074, Residual: 0.00119595
iter = 447, Function value: -7.81075, Residual: 0.000646184
iter = 448, Function value: -7.81075, Residual: 0.000594044
iter = 449, Function value: -7.81075, Residual: 0.000641001
iter = 450, Function value: -7.81076, Residual: 0.00125829
iter = 451, Function value: -7.81076, Residual: 0.000555608
iter = 452, Function value: -7.81076, Residual: 0.000525893
iter = 453, Function value: -7.81077, Residual: 0.000642375
iter = 454, Function value: -7.81077, Residual: 0.00143457
iter = 455, Function value: -7.81077, Residual: 0.000603507
iter = 456, Function value: -7.81077, Residual: 0.0004898
iter = 457, Function value: -7.81078, Residual: 0.000552409
iter = 458, Function value: -7.81078, Residual: 0.00108441
iter = 459, Function value: -7.81078, Residual: 0.000449216
iter = 460, Function value: -7.81078, Residual: 0.000447034
iter = 461, Function value: -7.81078, Residual: 0.000582248
iter = 462, Function value: -7.81079, Residual: 0.00129805
iter = 463, Function value: -7.81079, Residual: 0.000628522
iter = 464, Function value: -7.81079, Residual: 0.000497836
iter = 465, Function value: -7.81079, Residual: 0.000525396
iter = 466, Function value: -7.8108, Residual: 0.00116966
iter = 467, Function value: -7.8108, Residual: 0.000449353
iter = 468, Function value: -7.8108, Residual: 0.000376025
iter = 469, Function value: -7.8108, Residual: 0.000468749
iter = 470, Function value: -7.8108, Residual: 0.00101476
iter = 471, Function value: -7.81081, Residual: 0.000525062
iter = 472, Function value: -7.81081, Residual: 0.000429858
iter = 473, Function value: -7.81081, Residual: 0.000451821
iter = 474, Function value: -7.81081, Residual: 0.00120145
iter = 475, Function value: -7.81081, Residual: 0.000391866
iter = 476, Function value: -7.81081, Residual: 0.000333985
iter = 477, Function value: -7.81082, Residual: 0.000428342
iter = 478, Function value: -7.81082, Residual: 0.000739816
iter = 479, Function value: -7.81082, Residual: 0.000514762
iter = 480, Function value: -7.81082, Residual: 0.000342221
iter = 481, Function value: -7.81082, Residual: 0.000402088
iter = 482, Function value: -7.81082, Residual: 0.000503487
iter = 483, Function value: -7.81083, Residual: 0.000375629
iter = 484, Function value: -7.81083, Residual: 0.000387146
iter = 485, Function value: -7.81083, Residual: 0.000767867
iter = 486, Function value: -7.81083, Residual: 0.000318778
iter = 487, Function value: -7.81083, Residual: 0.000275048
Tao Object: 16 MPI processes
type: blmvm
Gradient steps: 0
TaoLineSearch Object: 16 MPI processes
type: more-thuente
Active Set subset type: subvec
convergence tolerances: fatol=1e-08, frtol=1e-08
convergence tolerances: gatol=1e-08, steptol=0, gttol=0
Residual in Function/Gradient:=0.000275048
Objective value=-7.81083
total number of iterations=487, (max: 50000)
total number of function/gradient evaluations=488, (max: 4000)
Solution converged: estimated |f(x)-f(X*)|/|f(X*)| <= frtol
it: 1 2.409978e+00 487 6.354219e+09
==========================================
Time summary:
==========================================
Creating DMPlex: 1.1421
Distributing DMPlex: 0.738438
Refining DMPlex: 0.388502
Setting up problem: 0.545519
Overall analysis time: 2.81322
Overall FLOPS/s: 5.22992e+09
************************************************************************************************************************
*** WIDEN YOUR WINDOW TO 120 CHARACTERS. Use 'enscript -r -fCourier9' to print this document ***
************************************************************************************************************************
---------------------------------------------- PETSc Performance Summary: ----------------------------------------------
./main_wolf on a arch-no-hdf5-opt named wf323.localdomain with 16 processors, by jychang48 Thu Jun 25 10:33:17 2015
Using Petsc Development GIT revision: unknown GIT Date: unknown
Max Max/Min Avg Total
Time (sec): 5.637e+00 1.00020 5.637e+00
Objects: 3.920e+02 1.11681 3.536e+02
Flops: 1.029e+09 1.12330 9.661e+08 1.546e+10
Flops/sec: 1.826e+08 1.12340 1.714e+08 2.742e+09
MPI Messages: 5.230e+03 1.42222 4.597e+03 7.354e+04
MPI Message Lengths: 8.956e+07 5.74006 4.876e+03 3.586e+08
MPI Reductions: 1.269e+04 1.00000
Flop counting convention: 1 flop = 1 real number operation of type (multiply/divide/add/subtract)
e.g., VecAXPY() for real vectors of length N --> 2N flops
and VecAXPY() for complex vectors of length N --> 8N flops
Summary of Stages: ----- Time ------ ----- Flops ----- --- Messages --- -- Message Lengths -- -- Reductions --
Avg %Total Avg %Total counts %Total Avg %Total counts %Total
0: Main Stage: 5.6369e+00 100.0% 1.5458e+10 100.0% 7.354e+04 100.0% 4.876e+03 100.0% 1.269e+04 100.0%
------------------------------------------------------------------------------------------------------------------------
See the 'Profiling' chapter of the users' manual for details on interpreting output.
Phase summary info:
Count: number of times phase was executed
Time and Flops: Max - maximum over all processors
Ratio - ratio of maximum to minimum over all processors
Mess: number of messages sent
Avg. len: average message length (bytes)
Reduct: number of global reductions
Global: entire computation
Stage: stages of a computation. Set stages with PetscLogStagePush() and PetscLogStagePop().
%T - percent time in this phase %F - percent flops in this phase
%M - percent messages in this phase %L - percent message lengths in this phase
%R - percent reductions in this phase
Total Mflop/s: 10e-6 * (sum of flops over all processors)/(max time over all processors)
------------------------------------------------------------------------------------------------------------------------
Event Count Time (sec) Flops --- Global --- --- Stage --- Total
Max Ratio Max Ratio Max Ratio Mess Avg len Reduct %T %F %M %L %R %T %F %M %L %R Mflop/s
------------------------------------------------------------------------------------------------------------------------
--- Event Stage 0: Main Stage
CreateMesh 489 1.0 2.7790e+00 1.0 2.81e+08 1.1 7.1e+04 3.7e+03 9.9e+02 49 27 96 74 8 49 27 96 74 8 1523
VecView 1 1.0 2.8899e-03 2.4 6.20e+04 2.6 8.6e+01 2.5e+04 0.0e+00 0 0 0 1 0 0 0 0 1 0 252
VecDot 10680 1.0 5.4635e-01 1.3 3.61e+08 1.1 0.0e+00 0.0e+00 1.1e+04 8 35 0 0 84 8 35 0 0 84 9915
VecNorm 488 1.0 3.0562e-02 1.8 1.65e+07 1.1 0.0e+00 0.0e+00 4.9e+02 0 2 0 0 4 0 2 0 0 4 8099
VecScale 1947 1.0 2.9742e-02 1.1 3.29e+07 1.1 0.0e+00 0.0e+00 0.0e+00 1 3 0 0 0 1 3 0 0 0 16602
VecCopy 6326 1.0 1.7738e-01 1.2 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 3 0 0 0 0 3 0 0 0 0 0
VecSet 11 1.0 1.6385e-02 1.3 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
VecAXPY 8245 1.0 2.7241e-01 1.2 2.87e+08 1.1 0.0e+00 0.0e+00 0.0e+00 4 28 0 0 0 4 28 0 0 0 15804
VecAYPX 972 1.0 3.0343e-02 1.5 1.64e+07 1.1 0.0e+00 0.0e+00 0.0e+00 0 2 0 0 0 0 2 0 0 0 8124
VecWAXPY 1 1.0 4.1008e-05 1.8 1.69e+04 1.1 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 6185
VecPointwiseMult 2917 1.0 7.4186e-02 1.1 4.93e+07 1.1 0.0e+00 0.0e+00 0.0e+00 1 5 0 0 0 1 5 0 0 0 9972
VecScatterBegin 489 1.0 2.2702e-02 1.4 0.00e+00 0.0 6.8e+04 2.5e+03 0.0e+00 0 0 93 48 0 0 0 93 48 0 0
VecScatterEnd 489 1.0 2.3321e-02 1.9 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
MatMult 489 1.0 4.4136e-01 1.1 2.32e+08 1.1 6.8e+04 2.5e+03 0.0e+00 8 23 93 48 0 8 23 93 48 0 7922
MatAssemblyBegin 2 1.0 2.9637e-0211.0 0.00e+00 0.0 2.1e+02 7.3e+04 4.0e+00 0 0 0 4 0 0 0 0 4 0 0
MatAssemblyEnd 2 1.0 1.2829e-02 1.7 0.00e+00 0.0 2.8e+02 6.3e+02 8.0e+00 0 0 0 0 0 0 0 0 0 0 0
MatZeroEntries 1 1.0 3.3212e-04 1.2 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
Mesh Partition 1 1.0 6.6892e-01 1.0 0.00e+00 0.0 1.6e+03 1.1e+04 4.0e+00 12 0 2 5 0 12 0 2 5 0 0
Mesh Migration 1 1.0 8.7067e-02 1.0 0.00e+00 0.0 3.0e+02 2.0e+05 2.0e+00 2 0 0 16 0 2 0 0 16 0 0
DMPlexInterp 3 1.0 9.7370e-014158.9 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 1 0 0 0 0 1 0 0 0 0 0
DMPlexDistribute 1 1.0 7.6774e-01 1.0 0.00e+00 0.0 1.9e+03 4.7e+04 6.0e+00 14 0 3 25 0 14 0 3 25 0 0
DMPlexDistCones 1 1.0 5.6297e-02 1.0 0.00e+00 0.0 1.3e+02 3.5e+05 0.0e+00 1 0 0 13 0 1 0 0 13 0 0
DMPlexDistLabels 1 1.0 2.6512e-0424.2 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
DMPlexDistField 2 1.0 2.6672e-02 1.1 0.00e+00 0.0 2.6e+02 7.8e+04 4.0e+00 0 0 0 6 0 0 0 0 6 0 0
DMPlexDistData 1 1.0 4.5035e-0132.4 0.00e+00 0.0 1.6e+03 7.0e+03 0.0e+00 7 0 2 3 0 7 0 2 3 0 0
DMPlexStratify 12 1.2 3.5558e-01 4.3 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 2 0 0 0 0 2 0 0 0 0 0
DMPlexPrealloc 1 1.0 1.9255e-01 1.0 0.00e+00 0.0 1.7e+03 5.2e+03 1.7e+01 3 0 2 2 0 3 0 2 2 0 0
DMPlexResidualFE 1 1.0 1.0459e-01 1.1 5.23e+06 1.1 0.0e+00 0.0e+00 0.0e+00 2 1 0 0 0 2 1 0 0 0 769
DMPlexJacobianFE 1 1.0 3.0138e-01 1.0 1.06e+07 1.1 2.1e+02 7.3e+04 2.0e+00 5 1 0 4 0 5 1 0 4 0 542
SFSetGraph 22 1.0 1.2767e-02 1.3 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
SFBcastBegin 35 1.0 4.6513e-01 7.6 0.00e+00 0.0 3.6e+03 3.0e+04 0.0e+00 8 0 5 30 0 8 0 5 30 0 0
SFBcastEnd 35 1.0 8.2554e-02 2.7 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 1 0 0 0 0 1 0 0 0 0 0
SFReduceBegin 6 1.0 1.3256e-02 9.8 0.00e+00 0.0 8.0e+02 1.9e+04 0.0e+00 0 0 1 4 0 0 0 1 4 0 0
SFReduceEnd 6 1.0 1.9113e-02 3.2 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
SFFetchOpBegin 1 1.0 7.4863e-0524.2 0.00e+00 0.0 7.0e+01 1.2e+04 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
SFFetchOpEnd 1 1.0 4.8614e-04 2.3 0.00e+00 0.0 7.0e+01 1.2e+04 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
SNESFunctionEval 1 1.0 1.1039e-01 1.1 5.23e+06 1.1 1.7e+02 2.5e+04 0.0e+00 2 1 0 1 0 2 1 0 1 0 729
SNESJacobianEval 1 1.0 3.0186e-01 1.0 1.06e+07 1.1 4.2e+02 4.4e+04 2.0e+00 5 1 1 5 0 5 1 1 5 0 541
TaoSolve 1 1.0 2.0006e+00 1.0 1.00e+09 1.1 6.8e+04 2.5e+03 1.3e+04 35 97 93 48 99 35 97 93 48 99 7529
TaoLineSearchApply 487 1.0 7.7062e-01 1.0 3.63e+08 1.1 6.8e+04 2.5e+03 3.9e+03 14 35 93 48 31 14 35 93 48 31 7083
------------------------------------------------------------------------------------------------------------------------
Memory usage is given in bytes:
Object Type Creations Destructions Memory Descendants' Mem.
Reports information only for process 0.
--- Event Stage 0: Main Stage
Viewer 4 3 2264 0
Object 7 7 4032 0
Container 7 7 3976 0
Vector 49 49 35398640 0
Vector Scatter 1 1 1088 0
Matrix 4 4 3128844 0
Distributed Mesh 30 30 139040 0
GraphPartitioner 12 12 7248 0
Star Forest Bipartite Graph 78 78 63576 0
Discrete System 30 30 25440 0
Index Set 85 85 12912584 0
IS L to G Mapping 2 2 3821272 0
Section 70 70 46480 0
SNES 1 1 1332 0
SNESLineSearch 1 1 864 0
DMSNES 1 1 664 0
Krylov Solver 1 1 1216 0
Preconditioner 1 1 848 0
Linear Space 2 2 1280 0
Dual Space 2 2 1312 0
FE Space 2 2 1496 0
Tao 1 1 1752 0
TaoLineSearch 1 1 880 0
========================================================================================================================
Average time to get PetscTime(): 5.96046e-07
Average time for MPI_Barrier(): 5.00679e-06
Average time for zero size MPI_Send(): 1.68383e-06
#PETSc Option Table entries:
-al 1
-am 0
-at 0.001
-bcloc 0,1,0,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1,0,1,0,1,0,0,0,1,0,1,1,1,0,1,0.45,0.55,0.45,0.55,0.45,0.55
-bcnum 7
-bcval 0,0,0,0,0,0,1
-dim 3
-dm_refine 1
-dt 0.001
-edges 3,3
-floc 0.25,0.75,0.25,0.75,0.25,0.75
-fnum 0
-ftime 0,99
-fval 1
-ksp_max_it 50000
-ksp_rtol 1.0e-10
-ksp_type cg
-log_summary
-lower 0,0
-mat_petscspace_order 0
-mesh datafiles/cube_with_hole4_mesh.dat
-mu 1
-nonneg 1
-numsteps 0
-options_left 0
-pc_type jacobi
-petscpartitioner_type parmetis
-progress 0
-simplex 1
-solution_petscspace_order 1
-tao_fatol 1e-8
-tao_frtol 1e-8
-tao_max_it 50000
-tao_monitor
-tao_type blmvm
-tao_view
-trans datafiles/cube_with_hole4_trans.dat
-upper 1,1
-vtuname figures/cube_with_hole_4
-vtuprint 1
#End of PETSc Option Table entries
Compiled without FORTRAN kernels
Compiled with full precision matrices (default)
sizeof(short) 2 sizeof(int) 4 sizeof(long) 8 sizeof(void*) 8 sizeof(PetscScalar) 8 sizeof(PetscInt) 4
Configure options: --download-f2cblaslapack=/turquoise/users/jychang48/petsc-externalpackages/f2cblaslapack-3.4.2.q1.tar.gz --download-metis=/turquoise/users/jychang48/petsc-externalpackages/metis-5.1.0-p1.tar.gz --download-openmpi=/turquoise/users/jychang48/petsc-externalpackages/openmpi-1.8.5.tar.gz --download-parmetis=/turquoise/users/jychang48/petsc-externalpackages/parmetis-4.0.3-p1.tar.gz --download-sowing=/turquoise/users/jychang48/petsc-externalpackages/sowing-1.1.17-p1.tar.gz --with-cc=gcc --with-cxx=g++ --with-debugging=0 --with-fc=gfortran COPTFLAGS="-O3 -march=native -mtune=native" CXXOPTFLAGS="-O3 -march=native -mtune=native" PETSC_ARCH=arch-no-hdf5-opt --download-chaco=/turquoise/users/jychang48/petsc-externalpackages/Chaco-2.2-p2.tar.gz
-----------------------------------------
Libraries compiled on Tue Jun 23 12:16:19 2015 on wf-fe2.lanl.gov
Machine characteristics: Linux-2.6.32-431.29.2.2chaos.ch5.2.x86_64-x86_64-with-redhat-6.6-Santiago
Using PETSc directory: /turquoise/users/jychang48/petsc-master
Using PETSc arch: arch-no-hdf5-opt
-----------------------------------------
Using C compiler: /turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/bin/mpicc -fPIC -Wall -Wwrite-strings -Wno-strict-aliasing -Wno-unknown-pragmas -O3 -march=native -mtune=native ${COPTFLAGS} ${CFLAGS}
Using Fortran compiler: /turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/bin/mpif90 -fPIC -Wall -Wno-unused-variable -ffree-line-length-0 -Wno-unused-dummy-argument -O ${FOPTFLAGS} ${FFLAGS}
-----------------------------------------
Using include paths: -I/turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/include -I/turquoise/users/jychang48/petsc-master/include -I/turquoise/users/jychang48/petsc-master/include -I/turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/include
-----------------------------------------
Using C linker: /turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/bin/mpicc
Using Fortran linker: /turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/bin/mpif90
Using libraries: -Wl,-rpath,/turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/lib -L/turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/lib -lpetsc -Wl,-rpath,/turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/lib -L/turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/lib -lf2clapack -lf2cblas -lm -lparmetis -lmetis -lchaco -lX11 -lhwloc -lssl -lcrypto -lm -Wl,-rpath,/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib/gcc/x86_64-unknown-linux-gnu/4.8.2 -L/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib/gcc/x86_64-unknown-linux-gnu/4.8.2 -Wl,-rpath,/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib/gcc -L/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib/gcc -Wl,-rpath,/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib64 -L/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib64 -Wl,-rpath,/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib -L/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib -lmpi_usempi -lmpi_mpifh -lgfortran -lm -lgfortran -lm -lquadmath -lm -lmpi_cxx -lstdc++ -Wl,-rpath,/turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/lib -L/turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/lib -Wl,-rpath,/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib/gcc/x86_64-unknown-linux-gnu/4.8.2 -L/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib/gcc/x86_64-unknown-linux-gnu/4.8.2 -Wl,-rpath,/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib/gcc -L/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib/gcc -Wl,-rpath,/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib64 -L/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib64 -Wl,-rpath,/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib -L/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib -ldl -Wl,-rpath,/turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/lib -lmpi -lgcc_s -lpthread -ldl
-----------------------------------------
-------------- next part --------------
==================================================
==================================================
MESHID = 4
==================================================
==================================================
==========================================
16 processors:
==========================================
TSTEP ANALYSIS TIME ITER FLOPS/s
iter = 0, Function value: -1.57703e-07, Residual: 0.239098
iter = 1, Function value: -4.53878e-07, Residual: 0.223304
iter = 2, Function value: -7.89606e-06, Residual: 0.221146
iter = 3, Function value: -0.0808297, Residual: 0.225876
iter = 4, Function value: -1.67847, Residual: 0.351701
iter = 5, Function value: -3.2307, Residual: 0.18377
iter = 6, Function value: -3.80321, Residual: 0.168797
iter = 7, Function value: -4.25223, Residual: 0.208515
iter = 8, Function value: -4.54546, Residual: 0.211522
iter = 9, Function value: -4.76263, Residual: 0.121566
iter = 10, Function value: -4.93278, Residual: 0.11454
iter = 11, Function value: -5.1385, Residual: 0.149913
iter = 12, Function value: -5.3172, Residual: 0.188699
iter = 13, Function value: -5.35404, Residual: 0.414234
iter = 14, Function value: -5.46775, Residual: 0.166872
iter = 15, Function value: -5.5088, Residual: 0.109606
iter = 16, Function value: -5.55735, Residual: 0.137697
iter = 17, Function value: -5.61175, Residual: 0.20436
iter = 18, Function value: -5.67254, Residual: 0.145789
iter = 19, Function value: -5.7428, Residual: 0.131282
iter = 20, Function value: -5.78678, Residual: 0.168675
iter = 21, Function value: -5.83482, Residual: 0.107592
iter = 22, Function value: -5.88877, Residual: 0.120863
iter = 23, Function value: -5.94162, Residual: 0.170501
iter = 24, Function value: -5.99103, Residual: 0.137338
iter = 25, Function value: -6.03433, Residual: 0.11123
iter = 26, Function value: -6.08494, Residual: 0.123001
iter = 27, Function value: -6.12432, Residual: 0.158457
iter = 28, Function value: -6.16801, Residual: 0.100757
iter = 29, Function value: -6.21675, Residual: 0.100482
iter = 30, Function value: -6.25439, Residual: 0.150756
iter = 31, Function value: -6.29508, Residual: 0.10085
iter = 32, Function value: -6.33253, Residual: 0.0966374
iter = 33, Function value: -6.36431, Residual: 0.103262
iter = 34, Function value: -6.40181, Residual: 0.102187
iter = 35, Function value: -6.44427, Residual: 0.121893
iter = 36, Function value: -6.48004, Residual: 0.0983186
iter = 37, Function value: -6.51506, Residual: 0.092832
iter = 38, Function value: -6.5519, Residual: 0.116683
iter = 39, Function value: -6.58292, Residual: 0.116904
iter = 40, Function value: -6.60971, Residual: 0.0771122
iter = 41, Function value: -6.63585, Residual: 0.0757516
iter = 42, Function value: -6.6586, Residual: 0.10885
iter = 43, Function value: -6.68587, Residual: 0.0752019
iter = 44, Function value: -6.7155, Residual: 0.0769993
iter = 45, Function value: -6.7459, Residual: 0.1128
iter = 46, Function value: -6.77436, Residual: 0.0874185
iter = 47, Function value: -6.8015, Residual: 0.0772962
iter = 48, Function value: -6.82934, Residual: 0.0894704
iter = 49, Function value: -6.85383, Residual: 0.0930334
iter = 50, Function value: -6.87712, Residual: 0.0707267
iter = 51, Function value: -6.90227, Residual: 0.0704031
iter = 52, Function value: -6.92095, Residual: 0.11036
iter = 53, Function value: -6.94187, Residual: 0.0625794
iter = 54, Function value: -6.96158, Residual: 0.0585309
iter = 55, Function value: -6.98111, Residual: 0.07778
iter = 56, Function value: -7.00425, Residual: 0.0917501
iter = 57, Function value: -7.02495, Residual: 0.0680073
iter = 58, Function value: -7.04571, Residual: 0.0632846
iter = 59, Function value: -7.06812, Residual: 0.067345
iter = 60, Function value: -7.08734, Residual: 0.111259
iter = 61, Function value: -7.10785, Residual: 0.062663
iter = 62, Function value: -7.12178, Residual: 0.0546556
iter = 63, Function value: -7.13834, Residual: 0.0661359
iter = 64, Function value: -7.15288, Residual: 0.0977186
iter = 65, Function value: -7.16979, Residual: 0.0558807
iter = 66, Function value: -7.18517, Residual: 0.0514965
iter = 67, Function value: -7.19956, Residual: 0.0566699
iter = 68, Function value: -7.21503, Residual: 0.102862
iter = 69, Function value: -7.23295, Residual: 0.0520616
iter = 70, Function value: -7.24472, Residual: 0.0514462
iter = 71, Function value: -7.2607, Residual: 0.0603313
iter = 72, Function value: -7.27322, Residual: 0.114154
iter = 73, Function value: -7.29073, Residual: 0.0553255
iter = 74, Function value: -7.30301, Residual: 0.0468704
iter = 75, Function value: -7.31535, Residual: 0.0503862
iter = 76, Function value: -7.32483, Residual: 0.121667
iter = 77, Function value: -7.34239, Residual: 0.0473323
iter = 78, Function value: -7.35051, Residual: 0.0402786
iter = 79, Function value: -7.36252, Residual: 0.0513142
iter = 80, Function value: -7.37578, Residual: 0.0980691
iter = 81, Function value: -7.39103, Residual: 0.0561799
iter = 82, Function value: -7.40188, Residual: 0.0420525
iter = 83, Function value: -7.41352, Residual: 0.0506624
iter = 84, Function value: -7.42311, Residual: 0.0745317
iter = 85, Function value: -7.43349, Residual: 0.0418963
iter = 86, Function value: -7.44304, Residual: 0.0427164
iter = 87, Function value: -7.45309, Residual: 0.050941
iter = 88, Function value: -7.46557, Residual: 0.0855721
iter = 89, Function value: -7.47897, Residual: 0.0443531
iter = 90, Function value: -7.48731, Residual: 0.038289
iter = 91, Function value: -7.49667, Residual: 0.0433084
iter = 92, Function value: -7.50404, Residual: 0.0748006
iter = 93, Function value: -7.51295, Residual: 0.0379092
iter = 94, Function value: -7.52065, Residual: 0.0360034
iter = 95, Function value: -7.52815, Residual: 0.0407615
iter = 96, Function value: -7.53624, Residual: 0.0871539
iter = 97, Function value: -7.54733, Residual: 0.0370463
iter = 98, Function value: -7.55263, Residual: 0.0321752
iter = 99, Function value: -7.5611, Residual: 0.0380701
iter = 100, Function value: -7.56636, Residual: 0.0828749
iter = 101, Function value: -7.57523, Residual: 0.0364468
iter = 102, Function value: -7.58065, Residual: 0.0296879
iter = 103, Function value: -7.58617, Residual: 0.0331854
iter = 104, Function value: -7.59286, Residual: 0.0653273
iter = 105, Function value: -7.60073, Residual: 0.0311378
iter = 106, Function value: -7.605, Residual: 0.0271273
iter = 107, Function value: -7.61161, Residual: 0.0352389
iter = 108, Function value: -7.61636, Residual: 0.0633698
iter = 109, Function value: -7.62264, Residual: 0.0323803
iter = 110, Function value: -7.62822, Residual: 0.0286247
iter = 111, Function value: -7.63278, Residual: 0.0310643
iter = 112, Function value: -7.63766, Residual: 0.0695965
iter = 113, Function value: -7.64471, Residual: 0.0273006
iter = 114, Function value: -7.6477, Residual: 0.0241679
iter = 115, Function value: -7.65299, Residual: 0.0287846
iter = 116, Function value: -7.65608, Residual: 0.0686328
iter = 117, Function value: -7.66196, Residual: 0.029546
iter = 118, Function value: -7.66562, Residual: 0.022733
iter = 119, Function value: -7.6691, Residual: 0.0260237
iter = 120, Function value: -7.67366, Residual: 0.0518384
iter = 121, Function value: -7.67882, Residual: 0.0250519
iter = 122, Function value: -7.68159, Residual: 0.0208822
iter = 123, Function value: -7.68604, Residual: 0.0283777
iter = 124, Function value: -7.68918, Residual: 0.0502209
iter = 125, Function value: -7.69338, Residual: 0.026466
iter = 126, Function value: -7.69725, Residual: 0.0232737
iter = 127, Function value: -7.70029, Residual: 0.0246071
iter = 128, Function value: -7.70333, Residual: 0.0560219
iter = 129, Function value: -7.70795, Residual: 0.0219018
iter = 130, Function value: -7.70988, Residual: 0.0192943
iter = 131, Function value: -7.71333, Residual: 0.0223829
iter = 132, Function value: -7.71539, Residual: 0.0571394
iter = 133, Function value: -7.71939, Residual: 0.0241063
iter = 134, Function value: -7.72174, Residual: 0.0183287
iter = 135, Function value: -7.72395, Residual: 0.0206927
iter = 136, Function value: -7.7272, Residual: 0.0366094
iter = 137, Function value: -7.73034, Residual: 0.0229168
iter = 138, Function value: -7.73217, Residual: 0.0165316
iter = 139, Function value: -7.73523, Residual: 0.0207809
iter = 140, Function value: -7.7372, Residual: 0.0344397
iter = 141, Function value: -7.73964, Residual: 0.0197955
iter = 142, Function value: -7.74225, Residual: 0.0175415
iter = 143, Function value: -7.74421, Residual: 0.0204096
iter = 144, Function value: -7.74642, Residual: 0.0325727
iter = 145, Function value: -7.74861, Residual: 0.0168286
iter = 146, Function value: -7.74991, Residual: 0.0166282
iter = 147, Function value: -7.75202, Residual: 0.0187187
iter = 148, Function value: -7.75333, Residual: 0.043491
iter = 149, Function value: -7.75579, Residual: 0.0163257
iter = 150, Function value: -7.75685, Residual: 0.013081
iter = 151, Function value: -7.75808, Residual: 0.0146514
iter = 152, Function value: -7.75972, Residual: 0.0272383
iter = 153, Function value: -7.76143, Residual: 0.0146716
iter = 154, Function value: -7.76245, Residual: 0.012202
iter = 155, Function value: -7.76402, Residual: 0.0165763
iter = 156, Function value: -7.76485, Residual: 0.0264216
iter = 157, Function value: -7.76596, Residual: 0.0141675
iter = 158, Function value: -7.76711, Residual: 0.0118525
iter = 159, Function value: -7.76795, Residual: 0.0132682
iter = 160, Function value: -7.76906, Residual: 0.0307988
iter = 161, Function value: -7.77046, Residual: 0.0136723
iter = 162, Function value: -7.77101, Residual: 0.0105228
iter = 163, Function value: -7.77204, Residual: 0.0114438
iter = 164, Function value: -7.77257, Residual: 0.0246093
iter = 165, Function value: -7.77345, Residual: 0.0125104
iter = 166, Function value: -7.77417, Residual: 0.0102181
iter = 167, Function value: -7.77474, Residual: 0.0109432
iter = 168, Function value: -7.77532, Residual: 0.0199892
iter = 169, Function value: -7.77611, Residual: 0.0101163
iter = 170, Function value: -7.77655, Residual: 0.0105442
iter = 171, Function value: -7.77743, Residual: 0.0116992
iter = 172, Function value: -7.77779, Residual: 0.0293656
iter = 173, Function value: -7.77878, Residual: 0.0112078
iter = 174, Function value: -7.77919, Residual: 0.00808034
iter = 175, Function value: -7.77957, Residual: 0.00902743
iter = 176, Function value: -7.78023, Residual: 0.0122209
iter = 177, Function value: -7.78068, Residual: 0.0174618
iter = 178, Function value: -7.78127, Residual: 0.00842028
iter = 179, Function value: -7.78167, Residual: 0.0091214
iter = 180, Function value: -7.78215, Residual: 0.0100989
iter = 181, Function value: -7.78253, Residual: 0.0198294
iter = 182, Function value: -7.78312, Residual: 0.00877935
iter = 183, Function value: -7.78342, Residual: 0.00805744
iter = 184, Function value: -7.78381, Residual: 0.00884593
iter = 185, Function value: -7.78426, Residual: 0.0172995
iter = 186, Function value: -7.78476, Residual: 0.00859094
iter = 187, Function value: -7.78503, Residual: 0.00730765
iter = 188, Function value: -7.78543, Residual: 0.00819726
iter = 189, Function value: -7.7857, Residual: 0.0163602
iter = 190, Function value: -7.78611, Residual: 0.00840989
iter = 191, Function value: -7.78641, Residual: 0.00748282
iter = 192, Function value: -7.7867, Residual: 0.00842633
iter = 193, Function value: -7.78717, Residual: 0.0143768
iter = 194, Function value: -7.78759, Residual: 0.00972398
iter = 195, Function value: -7.78783, Residual: 0.0068276
iter = 196, Function value: -7.78815, Residual: 0.00741378
iter = 197, Function value: -7.7884, Residual: 0.00941715
iter = 198, Function value: -7.78875, Residual: 0.00770439
iter = 199, Function value: -7.78917, Residual: 0.00852455
iter = 200, Function value: -7.78949, Residual: 0.0119365
iter = 201, Function value: -7.7898, Residual: 0.00737878
iter = 202, Function value: -7.79006, Residual: 0.00692509
iter = 203, Function value: -7.79031, Residual: 0.0078474
iter = 204, Function value: -7.79062, Residual: 0.0133278
iter = 205, Function value: -7.79097, Residual: 0.00759941
iter = 206, Function value: -7.7912, Residual: 0.00672229
iter = 207, Function value: -7.79144, Residual: 0.00698935
iter = 208, Function value: -7.79165, Residual: 0.0137285
iter = 209, Function value: -7.79195, Residual: 0.00704832
iter = 210, Function value: -7.79218, Residual: 0.00665052
iter = 211, Function value: -7.79241, Residual: 0.00752555
iter = 212, Function value: -7.79273, Residual: 0.013596
iter = 213, Function value: -7.79307, Residual: 0.00727608
iter = 214, Function value: -7.79324, Residual: 0.0061077
iter = 215, Function value: -7.79355, Residual: 0.0070488
iter = 216, Function value: -7.79371, Residual: 0.0129342
iter = 217, Function value: -7.79398, Residual: 0.00699342
iter = 218, Function value: -7.79422, Residual: 0.00615994
iter = 219, Function value: -7.79442, Residual: 0.00670625
iter = 220, Function value: -7.79466, Residual: 0.0129079
iter = 221, Function value: -7.79497, Residual: 0.00653888
iter = 222, Function value: -7.79512, Residual: 0.00595468
iter = 223, Function value: -7.79543, Residual: 0.00677409
iter = 224, Function value: -7.79557, Residual: 0.0157728
iter = 225, Function value: -7.79589, Residual: 0.00704583
iter = 226, Function value: -7.79609, Residual: 0.00558073
iter = 227, Function value: -7.79628, Residual: 0.00634841
iter = 228, Function value: -7.79655, Residual: 0.0100776
iter = 229, Function value: -7.79683, Residual: 0.00715925
iter = 230, Function value: -7.79701, Residual: 0.00566091
iter = 231, Function value: -7.79735, Residual: 0.00681582
iter = 232, Function value: -7.79758, Residual: 0.0114214
iter = 233, Function value: -7.79786, Residual: 0.00690348
iter = 234, Function value: -7.79816, Residual: 0.00560618
iter = 235, Function value: -7.79836, Residual: 0.00674194
iter = 236, Function value: -7.79867, Residual: 0.00742554
iter = 237, Function value: -7.79891, Residual: 0.00689359
iter = 238, Function value: -7.79916, Residual: 0.00634623
iter = 239, Function value: -7.79947, Residual: 0.00757451
iter = 240, Function value: -7.79971, Residual: 0.00895436
iter = 241, Function value: -7.79993, Residual: 0.00593646
iter = 242, Function value: -7.80019, Residual: 0.0052903
iter = 243, Function value: -7.80038, Residual: 0.00750854
iter = 244, Function value: -7.80063, Residual: 0.00558416
iter = 245, Function value: -7.80087, Residual: 0.00693769
iter = 246, Function value: -7.80111, Residual: 0.00608144
iter = 247, Function value: -7.80131, Residual: 0.00532918
iter = 248, Function value: -7.80156, Residual: 0.0068985
iter = 249, Function value: -7.80177, Residual: 0.00830931
iter = 250, Function value: -7.80197, Residual: 0.00580026
iter = 251, Function value: -7.80221, Residual: 0.00495945
iter = 252, Function value: -7.80236, Residual: 0.00744075
iter = 253, Function value: -7.80255, Residual: 0.00554201
iter = 254, Function value: -7.80278, Residual: 0.00566253
iter = 255, Function value: -7.80295, Residual: 0.00688607
iter = 256, Function value: -7.80311, Residual: 0.00544887
iter = 257, Function value: -7.8034, Residual: 0.00636679
iter = 258, Function value: -7.80355, Residual: 0.00897564
iter = 259, Function value: -7.80371, Residual: 0.00549888
iter = 260, Function value: -7.8039, Residual: 0.00415368
iter = 261, Function value: -7.80402, Residual: 0.00468554
iter = 262, Function value: -7.8042, Residual: 0.00944556
iter = 263, Function value: -7.8044, Residual: 0.00516081
iter = 264, Function value: -7.8045, Residual: 0.0043641
iter = 265, Function value: -7.8047, Residual: 0.00495905
iter = 266, Function value: -7.80479, Residual: 0.00991048
iter = 267, Function value: -7.80495, Residual: 0.00465967
iter = 268, Function value: -7.80507, Residual: 0.00374079
iter = 269, Function value: -7.80516, Residual: 0.00395313
iter = 270, Function value: -7.80527, Residual: 0.00847105
iter = 271, Function value: -7.80544, Residual: 0.00366557
iter = 272, Function value: -7.80554, Residual: 0.00421187
iter = 273, Function value: -7.80573, Residual: 0.00506542
iter = 274, Function value: -7.8058, Residual: 0.0113156
iter = 275, Function value: -7.80599, Residual: 0.00403993
iter = 276, Function value: -7.80607, Residual: 0.00309473
iter = 277, Function value: -7.80614, Residual: 0.00370412
iter = 278, Function value: -7.80629, Residual: 0.00589863
iter = 279, Function value: -7.80642, Residual: 0.00610977
iter = 280, Function value: -7.80654, Residual: 0.00338659
iter = 281, Function value: -7.80664, Residual: 0.00361862
iter = 282, Function value: -7.80672, Residual: 0.00436093
iter = 283, Function value: -7.80683, Residual: 0.00412742
iter = 284, Function value: -7.80694, Residual: 0.0034514
iter = 285, Function value: -7.80707, Residual: 0.00470219
iter = 286, Function value: -7.80717, Residual: 0.00523596
iter = 287, Function value: -7.80726, Residual: 0.00350967
iter = 288, Function value: -7.80737, Residual: 0.00311707
iter = 289, Function value: -7.80744, Residual: 0.0046191
iter = 290, Function value: -7.80754, Residual: 0.00378936
iter = 291, Function value: -7.80763, Residual: 0.00564189
iter = 292, Function value: -7.80771, Residual: 0.00311771
iter = 293, Function value: -7.80776, Residual: 0.00315966
iter = 294, Function value: -7.80786, Residual: 0.00369862
iter = 295, Function value: -7.80792, Residual: 0.00827063
iter = 296, Function value: -7.80803, Residual: 0.00357855
iter = 297, Function value: -7.80811, Residual: 0.00265554
iter = 298, Function value: -7.80816, Residual: 0.00290326
iter = 299, Function value: -7.80822, Residual: 0.00596541
iter = 300, Function value: -7.8083, Residual: 0.00280405
iter = 301, Function value: -7.80836, Residual: 0.00260171
iter = 302, Function value: -7.80844, Residual: 0.00323139
iter = 303, Function value: -7.80849, Residual: 0.00698054
iter = 304, Function value: -7.80858, Residual: 0.00268255
iter = 305, Function value: -7.80862, Residual: 0.00233456
iter = 306, Function value: -7.80867, Residual: 0.00274033
iter = 307, Function value: -7.80874, Residual: 0.00525292
iter = 308, Function value: -7.80882, Residual: 0.00315494
iter = 309, Function value: -7.80887, Residual: 0.00224967
iter = 310, Function value: -7.80893, Residual: 0.00288279
iter = 311, Function value: -7.80896, Residual: 0.0034635
iter = 312, Function value: -7.809, Residual: 0.00250577
iter = 313, Function value: -7.80908, Residual: 0.00252035
iter = 314, Function value: -7.80913, Residual: 0.00382787
iter = 315, Function value: -7.80918, Residual: 0.00268336
iter = 316, Function value: -7.80924, Residual: 0.00316437
iter = 317, Function value: -7.80927, Residual: 0.00287941
iter = 318, Function value: -7.8093, Residual: 0.00233516
iter = 319, Function value: -7.80936, Residual: 0.00313269
iter = 320, Function value: -7.8094, Residual: 0.00424648
iter = 321, Function value: -7.80943, Residual: 0.00254562
iter = 322, Function value: -7.80948, Residual: 0.00197848
iter = 323, Function value: -7.80951, Residual: 0.00219143
iter = 324, Function value: -7.80954, Residual: 0.00559215
iter = 325, Function value: -7.8096, Residual: 0.00207975
iter = 326, Function value: -7.80962, Residual: 0.00178679
iter = 327, Function value: -7.80967, Residual: 0.00219704
iter = 328, Function value: -7.80968, Residual: 0.00618964
iter = 329, Function value: -7.80974, Residual: 0.00217387
iter = 330, Function value: -7.80976, Residual: 0.00159697
iter = 331, Function value: -7.80978, Residual: 0.00181745
iter = 332, Function value: -7.80981, Residual: 0.00342114
iter = 333, Function value: -7.80984, Residual: 0.00156656
iter = 334, Function value: -7.80987, Residual: 0.0015906
iter = 335, Function value: -7.8099, Residual: 0.00220417
iter = 336, Function value: -7.80992, Residual: 0.00465009
iter = 337, Function value: -7.80995, Residual: 0.00187269
iter = 338, Function value: -7.80997, Residual: 0.00141207
iter = 339, Function value: -7.80999, Residual: 0.00165522
iter = 340, Function value: -7.81002, Residual: 0.00275905
iter = 341, Function value: -7.81004, Residual: 0.00236444
iter = 342, Function value: -7.81005, Residual: 0.00126158
iter = 343, Function value: -7.81007, Residual: 0.0013511
iter = 344, Function value: -7.81008, Residual: 0.00159616
iter = 345, Function value: -7.8101, Residual: 0.00263468
iter = 346, Function value: -7.81012, Residual: 0.00130192
iter = 347, Function value: -7.81013, Residual: 0.00125099
iter = 348, Function value: -7.81014, Residual: 0.00134643
iter = 349, Function value: -7.81015, Residual: 0.00322428
iter = 350, Function value: -7.81017, Residual: 0.00112657
iter = 351, Function value: -7.81017, Residual: 0.00105449
iter = 352, Function value: -7.81019, Residual: 0.00124955
iter = 353, Function value: -7.8102, Residual: 0.00223142
iter = 354, Function value: -7.81021, Residual: 0.00113859
iter = 355, Function value: -7.81022, Residual: 0.00098257
iter = 356, Function value: -7.81023, Residual: 0.00131657
iter = 357, Function value: -7.81024, Residual: 0.00218694
iter = 358, Function value: -7.81025, Residual: 0.00104937
iter = 359, Function value: -7.81025, Residual: 0.000917616
iter = 360, Function value: -7.81026, Residual: 0.00108485
iter = 361, Function value: -7.81027, Residual: 0.00205437
iter = 362, Function value: -7.81028, Residual: 0.00123308
iter = 363, Function value: -7.81029, Residual: 0.000869878
iter = 364, Function value: -7.8103, Residual: 0.000962767
iter = 365, Function value: -7.8103, Residual: 0.00183068
iter = 366, Function value: -7.81031, Residual: 0.00111156
iter = 367, Function value: -7.81032, Residual: 0.000900357
iter = 368, Function value: -7.81033, Residual: 0.0010033
iter = 369, Function value: -7.81034, Residual: 0.00178238
iter = 370, Function value: -7.81034, Residual: 0.000996511
iter = 371, Function value: -7.81035, Residual: 0.000886717
iter = 372, Function value: -7.81036, Residual: 0.00106146
iter = 373, Function value: -7.81036, Residual: 0.00222379
iter = 374, Function value: -7.81037, Residual: 0.00099812
iter = 375, Function value: -7.81038, Residual: 0.000723174
iter = 376, Function value: -7.81038, Residual: 0.000738951
iter = 377, Function value: -7.81039, Residual: 0.00185185
iter = 378, Function value: -7.8104, Residual: 0.00075251
iter = 379, Function value: -7.8104, Residual: 0.000839343
iter = 380, Function value: -7.81041, Residual: 0.00103561
iter = 381, Function value: -7.81041, Residual: 0.0024586
iter = 382, Function value: -7.81042, Residual: 0.000744648
iter = 383, Function value: -7.81042, Residual: 0.000538622
iter = 384, Function value: -7.81043, Residual: 0.000718492
iter = 385, Function value: -7.81043, Residual: 0.00104014
iter = 386, Function value: -7.81044, Residual: 0.00136274
iter = 387, Function value: -7.81044, Residual: 0.00074841
iter = 388, Function value: -7.81045, Residual: 0.000785691
iter = 389, Function value: -7.81045, Residual: 0.000898177
iter = 390, Function value: -7.81046, Residual: 0.00146418
iter = 391, Function value: -7.81046, Residual: 0.000690708
iter = 392, Function value: -7.81047, Residual: 0.000647569
iter = 393, Function value: -7.81047, Residual: 0.000697967
iter = 394, Function value: -7.81048, Residual: 0.00131163
iter = 395, Function value: -7.81048, Residual: 0.000827399
iter = 396, Function value: -7.81049, Residual: 0.000845789
iter = 397, Function value: -7.8105, Residual: 0.00105409
iter = 398, Function value: -7.8105, Residual: 0.00161545
iter = 399, Function value: -7.8105, Residual: 0.000687262
iter = 400, Function value: -7.81051, Residual: 0.00059559
iter = 401, Function value: -7.81051, Residual: 0.000752873
iter = 402, Function value: -7.81051, Residual: 0.00120154
iter = 403, Function value: -7.81052, Residual: 0.000798778
iter = 404, Function value: -7.81052, Residual: 0.000712079
iter = 405, Function value: -7.81053, Residual: 0.00110496
iter = 406, Function value: -7.81053, Residual: 0.00127187
iter = 407, Function value: -7.81054, Residual: 0.000842919
iter = 408, Function value: -7.81055, Residual: 0.00069032
iter = 409, Function value: -7.81055, Residual: 0.000841593
iter = 410, Function value: -7.81056, Residual: 0.0012262
iter = 411, Function value: -7.81056, Residual: 0.000918047
iter = 412, Function value: -7.81057, Residual: 0.0007945
iter = 413, Function value: -7.81057, Residual: 0.000866942
iter = 414, Function value: -7.81058, Residual: 0.00155323
iter = 415, Function value: -7.81058, Residual: 0.000891976
iter = 416, Function value: -7.81059, Residual: 0.000747428
iter = 417, Function value: -7.81059, Residual: 0.000821924
iter = 418, Function value: -7.8106, Residual: 0.00105041
iter = 419, Function value: -7.8106, Residual: 0.000994719
iter = 420, Function value: -7.81061, Residual: 0.00088687
iter = 421, Function value: -7.81061, Residual: 0.00103494
iter = 422, Function value: -7.81062, Residual: 0.00130036
iter = 423, Function value: -7.81063, Residual: 0.000908119
iter = 424, Function value: -7.81063, Residual: 0.000781051
iter = 425, Function value: -7.81064, Residual: 0.00113501
iter = 426, Function value: -7.81064, Residual: 0.000816488
iter = 427, Function value: -7.81065, Residual: 0.000743231
iter = 428, Function value: -7.81065, Residual: 0.00100657
iter = 429, Function value: -7.81065, Residual: 0.000910451
iter = 430, Function value: -7.81066, Residual: 0.00194077
iter = 431, Function value: -7.81067, Residual: 0.000792676
iter = 432, Function value: -7.81067, Residual: 0.000635068
iter = 433, Function value: -7.81068, Residual: 0.000844652
iter = 434, Function value: -7.81068, Residual: 0.00173968
iter = 435, Function value: -7.81068, Residual: 0.000718673
iter = 436, Function value: -7.81069, Residual: 0.000571681
iter = 437, Function value: -7.81069, Residual: 0.000659658
iter = 438, Function value: -7.81069, Residual: 0.0013505
iter = 439, Function value: -7.8107, Residual: 0.000568503
iter = 440, Function value: -7.8107, Residual: 0.000650747
iter = 441, Function value: -7.8107, Residual: 0.000749039
iter = 442, Function value: -7.81071, Residual: 0.00174564
iter = 443, Function value: -7.81071, Residual: 0.000662028
iter = 444, Function value: -7.81071, Residual: 0.000523397
iter = 445, Function value: -7.81072, Residual: 0.000656138
iter = 446, Function value: -7.81072, Residual: 0.00109849
iter = 447, Function value: -7.81073, Residual: 0.000874994
iter = 448, Function value: -7.81073, Residual: 0.000531464
iter = 449, Function value: -7.81073, Residual: 0.000593073
iter = 450, Function value: -7.81073, Residual: 0.000707091
iter = 451, Function value: -7.81074, Residual: 0.000810123
iter = 452, Function value: -7.81074, Residual: 0.000603729
iter = 453, Function value: -7.81074, Residual: 0.000561513
iter = 454, Function value: -7.81075, Residual: 0.000729322
iter = 455, Function value: -7.81075, Residual: 0.000673021
iter = 456, Function value: -7.81075, Residual: 0.000644186
iter = 457, Function value: -7.81076, Residual: 0.000523062
iter = 458, Function value: -7.81076, Residual: 0.000587717
iter = 459, Function value: -7.81076, Residual: 0.000496603
iter = 460, Function value: -7.81076, Residual: 0.000532727
iter = 461, Function value: -7.81077, Residual: 0.000998028
iter = 462, Function value: -7.81077, Residual: 0.000573228
iter = 463, Function value: -7.81077, Residual: 0.000469068
iter = 464, Function value: -7.81077, Residual: 0.000588658
iter = 465, Function value: -7.81077, Residual: 0.000786977
iter = 466, Function value: -7.81078, Residual: 0.000519669
iter = 467, Function value: -7.81078, Residual: 0.00048518
iter = 468, Function value: -7.81078, Residual: 0.000624198
iter = 469, Function value: -7.81078, Residual: 0.000502941
iter = 470, Function value: -7.81078, Residual: 0.000492811
iter = 471, Function value: -7.81079, Residual: 0.00055812
iter = 472, Function value: -7.81079, Residual: 0.000851356
iter = 473, Function value: -7.81079, Residual: 0.00054883
iter = 474, Function value: -7.81079, Residual: 0.000465062
iter = 475, Function value: -7.8108, Residual: 0.000552709
iter = 476, Function value: -7.8108, Residual: 0.000708076
iter = 477, Function value: -7.8108, Residual: 0.000473574
iter = 478, Function value: -7.8108, Residual: 0.000442428
iter = 479, Function value: -7.8108, Residual: 0.000635062
iter = 480, Function value: -7.81081, Residual: 0.000419477
iter = 481, Function value: -7.81081, Residual: 0.000422442
iter = 482, Function value: -7.81081, Residual: 0.000578847
iter = 483, Function value: -7.81081, Residual: 0.000499854
iter = 484, Function value: -7.81081, Residual: 0.000460825
iter = 485, Function value: -7.81081, Residual: 0.000545967
iter = 486, Function value: -7.81082, Residual: 0.00060014
iter = 487, Function value: -7.81082, Residual: 0.000418945
iter = 488, Function value: -7.81082, Residual: 0.000410794
iter = 489, Function value: -7.81082, Residual: 0.000750432
iter = 490, Function value: -7.81082, Residual: 0.000379884
iter = 491, Function value: -7.81082, Residual: 0.000331549
iter = 492, Function value: -7.81082, Residual: 0.000416861
iter = 493, Function value: -7.81083, Residual: 0.000579213
iter = 494, Function value: -7.81083, Residual: 0.000378749
iter = 495, Function value: -7.81083, Residual: 0.000391863
iter = 496, Function value: -7.81083, Residual: 0.000469321
iter = 497, Function value: -7.81083, Residual: 0.000849723
iter = 498, Function value: -7.81083, Residual: 0.000365311
iter = 499, Function value: -7.81083, Residual: 0.00030933
iter = 500, Function value: -7.81083, Residual: 0.000356744
iter = 501, Function value: -7.81084, Residual: 0.000652476
iter = 502, Function value: -7.81084, Residual: 0.000389956
iter = 503, Function value: -7.81084, Residual: 0.00030185
iter = 504, Function value: -7.81084, Residual: 0.000430598
iter = 505, Function value: -7.81084, Residual: 0.000506603
iter = 506, Function value: -7.81084, Residual: 0.000319141
iter = 507, Function value: -7.81084, Residual: 0.000290193
iter = 508, Function value: -7.81084, Residual: 0.000334589
iter = 509, Function value: -7.81084, Residual: 0.000689399
iter = 510, Function value: -7.81084, Residual: 0.000335727
iter = 511, Function value: -7.81084, Residual: 0.000261672
Tao Object: 16 MPI processes
type: blmvm
Gradient steps: 0
TaoLineSearch Object: 16 MPI processes
type: more-thuente
Active Set subset type: subvec
convergence tolerances: fatol=1e-08, frtol=1e-08
convergence tolerances: gatol=1e-08, steptol=0, gttol=0
Residual in Function/Gradient:=0.000261672
Objective value=-7.81084
total number of iterations=511, (max: 50000)
total number of function/gradient evaluations=512, (max: 4000)
Solution converged: estimated |f(x)-f(X*)|/|f(X*)| <= frtol
it: 1 2.475548e+00 511 6.486348e+09
==========================================
Time summary:
==========================================
Creating DMPlex: 1.16017
Distributing DMPlex: 0.726076
Refining DMPlex: 0.392075
Setting up problem: 0.55005
Overall analysis time: 2.88639
Overall FLOPS/s: 5.35456e+09
************************************************************************************************************************
*** WIDEN YOUR WINDOW TO 120 CHARACTERS. Use 'enscript -r -fCourier9' to print this document ***
************************************************************************************************************************
---------------------------------------------- PETSc Performance Summary: ----------------------------------------------
./main_wolf on a arch-no-hdf5-opt named wf148.localdomain with 16 processors, by jychang48 Thu Jun 25 10:36:25 2015
Using Petsc Development GIT revision: unknown GIT Date: unknown
Max Max/Min Avg Total
Time (sec): 5.725e+00 1.00016 5.724e+00
Objects: 3.920e+02 1.11681 3.536e+02
Flops: 1.079e+09 1.12340 1.013e+09 1.620e+10
Flops/sec: 1.885e+08 1.12351 1.769e+08 2.830e+09
MPI Messages: 5.470e+03 1.42250 4.807e+03 7.690e+04
MPI Message Lengths: 8.998e+07 5.60808 4.773e+03 3.671e+08
MPI Reductions: 1.332e+04 1.00000
Flop counting convention: 1 flop = 1 real number operation of type (multiply/divide/add/subtract)
e.g., VecAXPY() for real vectors of length N --> 2N flops
and VecAXPY() for complex vectors of length N --> 8N flops
Summary of Stages: ----- Time ------ ----- Flops ----- --- Messages --- -- Message Lengths -- -- Reductions --
Avg %Total Avg %Total counts %Total Avg %Total counts %Total
0: Main Stage: 5.7241e+00 100.0% 1.6201e+10 100.0% 7.690e+04 100.0% 4.773e+03 100.0% 1.332e+04 100.0%
------------------------------------------------------------------------------------------------------------------------
See the 'Profiling' chapter of the users' manual for details on interpreting output.
Phase summary info:
Count: number of times phase was executed
Time and Flops: Max - maximum over all processors
Ratio - ratio of maximum to minimum over all processors
Mess: number of messages sent
Avg. len: average message length (bytes)
Reduct: number of global reductions
Global: entire computation
Stage: stages of a computation. Set stages with PetscLogStagePush() and PetscLogStagePop().
%T - percent time in this phase %F - percent flops in this phase
%M - percent messages in this phase %L - percent message lengths in this phase
%R - percent reductions in this phase
Total Mflop/s: 10e-6 * (sum of flops over all processors)/(max time over all processors)
------------------------------------------------------------------------------------------------------------------------
Event Count Time (sec) Flops --- Global --- --- Stage --- Total
Max Ratio Max Ratio Max Ratio Mess Avg len Reduct %T %F %M %L %R %T %F %M %L %R Mflop/s
------------------------------------------------------------------------------------------------------------------------
--- Event Stage 0: Main Stage
CreateMesh 513 1.0 2.8079e+00 1.0 2.95e+08 1.1 7.4e+04 3.7e+03 1.0e+03 49 27 96 74 8 49 27 96 74 8 1581
VecView 1 1.0 2.8341e-03 2.4 6.20e+04 2.6 8.6e+01 2.5e+04 0.0e+00 0 0 0 1 0 0 0 0 1 0 257
VecDot 11208 1.0 5.5847e-01 1.3 3.79e+08 1.1 0.0e+00 0.0e+00 1.1e+04 9 35 0 0 84 9 35 0 0 84 10179
VecNorm 512 1.0 2.0345e-02 1.5 1.73e+07 1.1 0.0e+00 0.0e+00 5.1e+02 0 2 0 0 4 0 2 0 0 4 12765
VecScale 2043 1.0 3.0195e-02 1.1 3.46e+07 1.1 0.0e+00 0.0e+00 0.0e+00 1 3 0 0 0 1 3 0 0 0 17159
VecCopy 6638 1.0 1.8342e-01 1.1 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 3 0 0 0 0 3 0 0 0 0 0
VecSet 11 1.0 1.5627e-02 1.2 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
VecAXPY 8653 1.0 2.8274e-01 1.2 3.01e+08 1.1 0.0e+00 0.0e+00 0.0e+00 4 28 0 0 0 4 28 0 0 0 15981
VecAYPX 1020 1.0 3.0797e-02 1.5 1.73e+07 1.1 0.0e+00 0.0e+00 0.0e+00 0 2 0 0 0 0 2 0 0 0 8400
VecWAXPY 1 1.0 4.1008e-05 1.9 1.69e+04 1.1 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 6185
VecPointwiseMult 3061 1.0 7.7274e-02 1.1 5.18e+07 1.1 0.0e+00 0.0e+00 0.0e+00 1 5 0 0 0 1 5 0 0 0 10046
VecScatterBegin 513 1.0 2.4148e-02 1.5 0.00e+00 0.0 7.2e+04 2.5e+03 0.0e+00 0 0 93 49 0 0 0 93 49 0 0
VecScatterEnd 513 1.0 2.4307e-02 1.9 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
MatMult 513 1.0 4.5839e-01 1.1 2.44e+08 1.1 7.2e+04 2.5e+03 0.0e+00 8 23 93 49 0 8 23 93 49 0 8002
MatAssemblyBegin 2 1.0 2.8213e-0212.5 0.00e+00 0.0 2.1e+02 7.3e+04 4.0e+00 0 0 0 4 0 0 0 0 4 0 0
MatAssemblyEnd 2 1.0 1.2559e-02 1.6 0.00e+00 0.0 2.8e+02 6.3e+02 8.0e+00 0 0 0 0 0 0 0 0 0 0 0
MatZeroEntries 1 1.0 3.2997e-04 1.2 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
Mesh Partition 1 1.0 6.5906e-01 1.1 0.00e+00 0.0 1.6e+03 1.1e+04 4.0e+00 11 0 2 5 0 11 0 2 5 0 0
Mesh Migration 1 1.0 8.5906e-02 1.0 0.00e+00 0.0 3.0e+02 2.0e+05 2.0e+00 1 0 0 16 0 1 0 0 16 0 0
DMPlexInterp 3 1.0 9.6569e-014074.8 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 1 0 0 0 0 1 0 0 0 0 0
DMPlexDistribute 1 1.0 7.5649e-01 1.0 0.00e+00 0.0 1.9e+03 4.7e+04 6.0e+00 13 0 3 25 0 13 0 3 25 0 0
DMPlexDistCones 1 1.0 5.5471e-02 1.0 0.00e+00 0.0 1.3e+02 3.5e+05 0.0e+00 1 0 0 12 0 1 0 0 12 0 0
DMPlexDistLabels 1 1.0 2.6703e-0429.5 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
DMPlexDistField 2 1.0 2.6206e-02 1.1 0.00e+00 0.0 2.6e+02 7.8e+04 4.0e+00 0 0 0 5 0 0 0 0 5 0 0
DMPlexDistData 1 1.0 4.4285e-0131.5 0.00e+00 0.0 1.6e+03 7.0e+03 0.0e+00 7 0 2 3 0 7 0 2 3 0 0
DMPlexStratify 12 1.2 3.5430e-01 4.2 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 2 0 0 0 0 2 0 0 0 0 0
DMPlexPrealloc 1 1.0 1.9255e-01 1.0 0.00e+00 0.0 1.7e+03 5.2e+03 1.7e+01 3 0 2 2 0 3 0 2 2 0 0
DMPlexResidualFE 1 1.0 1.0427e-01 1.1 5.23e+06 1.1 0.0e+00 0.0e+00 0.0e+00 2 0 0 0 0 2 0 0 0 0 771
DMPlexJacobianFE 1 1.0 2.9981e-01 1.0 1.06e+07 1.1 2.1e+02 7.3e+04 2.0e+00 5 1 0 4 0 5 1 0 4 0 545
SFSetGraph 22 1.0 1.2753e-02 1.3 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
SFBcastBegin 35 1.0 4.5945e-01 7.6 0.00e+00 0.0 3.6e+03 3.0e+04 0.0e+00 8 0 5 29 0 8 0 5 29 0 0
SFBcastEnd 35 1.0 8.2271e-02 2.7 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 1 0 0 0 0 1 0 0 0 0 0
SFReduceBegin 6 1.0 1.3219e-0210.0 0.00e+00 0.0 8.0e+02 1.9e+04 0.0e+00 0 0 1 4 0 0 0 1 4 0 0
SFReduceEnd 6 1.0 1.8614e-02 3.2 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
SFFetchOpBegin 1 1.0 7.2002e-0517.8 0.00e+00 0.0 7.0e+01 1.2e+04 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
SFFetchOpEnd 1 1.0 4.7493e-04 2.1 0.00e+00 0.0 7.0e+01 1.2e+04 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
SNESFunctionEval 1 1.0 1.0910e-01 1.1 5.23e+06 1.1 1.7e+02 2.5e+04 0.0e+00 2 0 0 1 0 2 0 0 1 0 737
SNESJacobianEval 1 1.0 3.0030e-01 1.0 1.06e+07 1.1 4.2e+02 4.4e+04 2.0e+00 5 1 1 5 0 5 1 1 5 0 544
TaoSolve 1 1.0 2.0681e+00 1.0 1.05e+09 1.1 7.2e+04 2.5e+03 1.3e+04 36 98 93 49100 36 98 93 49100 7643
TaoLineSearchApply 511 1.0 8.0252e-01 1.0 3.81e+08 1.1 7.2e+04 2.5e+03 4.1e+03 14 35 93 49 31 14 35 93 49 31 7136
------------------------------------------------------------------------------------------------------------------------
Memory usage is given in bytes:
Object Type Creations Destructions Memory Descendants' Mem.
Reports information only for process 0.
--- Event Stage 0: Main Stage
Viewer 4 3 2264 0
Object 7 7 4032 0
Container 7 7 3976 0
Vector 49 49 35398640 0
Vector Scatter 1 1 1088 0
Matrix 4 4 3128844 0
Distributed Mesh 30 30 139040 0
GraphPartitioner 12 12 7248 0
Star Forest Bipartite Graph 78 78 63576 0
Discrete System 30 30 25440 0
Index Set 85 85 12912584 0
IS L to G Mapping 2 2 3821272 0
Section 70 70 46480 0
SNES 1 1 1332 0
SNESLineSearch 1 1 864 0
DMSNES 1 1 664 0
Krylov Solver 1 1 1216 0
Preconditioner 1 1 848 0
Linear Space 2 2 1280 0
Dual Space 2 2 1312 0
FE Space 2 2 1496 0
Tao 1 1 1752 0
TaoLineSearch 1 1 880 0
========================================================================================================================
Average time to get PetscTime(): 5.96046e-07
Average time for MPI_Barrier(): 3.19481e-06
Average time for zero size MPI_Send(): 1.68383e-06
#PETSc Option Table entries:
-al 1
-am 0
-at 0.001
-bcloc 0,1,0,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1,0,1,0,1,0,0,0,1,0,1,1,1,0,1,0.45,0.55,0.45,0.55,0.45,0.55
-bcnum 7
-bcval 0,0,0,0,0,0,1
-dim 3
-dm_refine 1
-dt 0.001
-edges 3,3
-floc 0.25,0.75,0.25,0.75,0.25,0.75
-fnum 0
-ftime 0,99
-fval 1
-ksp_max_it 50000
-ksp_rtol 1.0e-10
-ksp_type cg
-log_summary
-lower 0,0
-mat_petscspace_order 0
-mesh datafiles/cube_with_hole4_mesh.dat
-mu 1
-nonneg 1
-numsteps 0
-options_left 0
-pc_type jacobi
-petscpartitioner_type parmetis
-progress 0
-simplex 1
-solution_petscspace_order 1
-tao_fatol 1e-8
-tao_frtol 1e-8
-tao_max_it 50000
-tao_monitor
-tao_type blmvm
-tao_view
-trans datafiles/cube_with_hole4_trans.dat
-upper 1,1
-vtuname figures/cube_with_hole_4
-vtuprint 1
#End of PETSc Option Table entries
Compiled without FORTRAN kernels
Compiled with full precision matrices (default)
sizeof(short) 2 sizeof(int) 4 sizeof(long) 8 sizeof(void*) 8 sizeof(PetscScalar) 8 sizeof(PetscInt) 4
Configure options: --download-f2cblaslapack=/turquoise/users/jychang48/petsc-externalpackages/f2cblaslapack-3.4.2.q1.tar.gz --download-metis=/turquoise/users/jychang48/petsc-externalpackages/metis-5.1.0-p1.tar.gz --download-openmpi=/turquoise/users/jychang48/petsc-externalpackages/openmpi-1.8.5.tar.gz --download-parmetis=/turquoise/users/jychang48/petsc-externalpackages/parmetis-4.0.3-p1.tar.gz --download-sowing=/turquoise/users/jychang48/petsc-externalpackages/sowing-1.1.17-p1.tar.gz --with-cc=gcc --with-cxx=g++ --with-debugging=0 --with-fc=gfortran COPTFLAGS="-O3 -march=native -mtune=native" CXXOPTFLAGS="-O3 -march=native -mtune=native" PETSC_ARCH=arch-no-hdf5-opt --download-chaco=/turquoise/users/jychang48/petsc-externalpackages/Chaco-2.2-p2.tar.gz
-----------------------------------------
Libraries compiled on Tue Jun 23 12:16:19 2015 on wf-fe2.lanl.gov
Machine characteristics: Linux-2.6.32-431.29.2.2chaos.ch5.2.x86_64-x86_64-with-redhat-6.6-Santiago
Using PETSc directory: /turquoise/users/jychang48/petsc-master
Using PETSc arch: arch-no-hdf5-opt
-----------------------------------------
Using C compiler: /turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/bin/mpicc -fPIC -Wall -Wwrite-strings -Wno-strict-aliasing -Wno-unknown-pragmas -O3 -march=native -mtune=native ${COPTFLAGS} ${CFLAGS}
Using Fortran compiler: /turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/bin/mpif90 -fPIC -Wall -Wno-unused-variable -ffree-line-length-0 -Wno-unused-dummy-argument -O ${FOPTFLAGS} ${FFLAGS}
-----------------------------------------
Using include paths: -I/turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/include -I/turquoise/users/jychang48/petsc-master/include -I/turquoise/users/jychang48/petsc-master/include -I/turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/include
-----------------------------------------
Using C linker: /turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/bin/mpicc
Using Fortran linker: /turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/bin/mpif90
Using libraries: -Wl,-rpath,/turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/lib -L/turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/lib -lpetsc -Wl,-rpath,/turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/lib -L/turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/lib -lf2clapack -lf2cblas -lm -lparmetis -lmetis -lchaco -lX11 -lhwloc -lssl -lcrypto -lm -Wl,-rpath,/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib/gcc/x86_64-unknown-linux-gnu/4.8.2 -L/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib/gcc/x86_64-unknown-linux-gnu/4.8.2 -Wl,-rpath,/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib/gcc -L/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib/gcc -Wl,-rpath,/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib64 -L/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib64 -Wl,-rpath,/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib -L/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib -lmpi_usempi -lmpi_mpifh -lgfortran -lm -lgfortran -lm -lquadmath -lm -lmpi_cxx -lstdc++ -Wl,-rpath,/turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/lib -L/turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/lib -Wl,-rpath,/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib/gcc/x86_64-unknown-linux-gnu/4.8.2 -L/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib/gcc/x86_64-unknown-linux-gnu/4.8.2 -Wl,-rpath,/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib/gcc -L/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib/gcc -Wl,-rpath,/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib64 -L/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib64 -Wl,-rpath,/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib -L/turquoise/usr/projects/hpcsoft/toss2/common/gcc/4.8.2/lib -ldl -Wl,-rpath,/turquoise/users/jychang48/petsc-master/arch-no-hdf5-opt/lib -lmpi -lgcc_s -lpthread -ldl
-----------------------------------------
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