From NWChem
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Posts 86
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| 9:00:10 PM PDT - Sat, Sep 30th 2017 |
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Hello Nwchem users
I have following input
title "cubane CCSD/cc-pVDZ hessian"
memory stack 500 mb heap 500 mb global 22000 mb
geometry
symmetry C2v
H 2.05734129 -1.32981120 0.02496974
C 1.09520984 -0.79607173 -0.03872782
H 2.05734129 1.32981120 0.02496974
C 1.09520984 0.79607173 -0.03872782
H 0.00000000 -1.59258557 -2.05035614
C 0.00000000 -1.20794408 -1.02589691
H 0.00000000 1.59258557 -2.05035614
C 0.00000000 1.20794408 -1.02589691
H 0.00000000 -1.45028854 1.97989592
C 0.00000000 -0.78106969 1.10677261
H 0.00000000 1.45028854 1.97989592
C 0.00000000 0.78106969 1.10677261
H -2.05734129 -1.32981120 0.02496974
C -1.09520984 -0.79607173 -0.03872782
H -2.05734129 1.32981120 0.02496974
C -1.09520984 0.79607173 -0.03872782
end
basis spherical
H library cc-pVDZ
C library cc-pVDZ
end
scf
direct
end
tce
mkccsd
2emet 1
freeze atomic
end
mrccdata
root 1
nref 2
22222222222222222222222222220
22222222222222222222222222202
end
task tce freq
I ran it on my personal 4-core computer and
get the following benchmarks
Symmetry of references
Ref. 1 sym:a
Ref. 2 sym:a
MR MkCCSD, version 1.0
Heff
=============================================
0 1 -664.87322151 0.09099694
0 2 0.09099694 -664.79730579
Eigenvalues (real and imaginary)
=============================================
-664.933860013217 0.00000000
-664.736667291187 0.00000000
Left eigenvectors
=============================================
1 -0.83216055 -0.55453478
2 0.55453478 -0.83216055
Right eigenvectors
=============================================
VR 1 -0.83216055 -0.55453478
VR 2 0.55453478 -0.83216055
Target root: 1
MkCC iter. # 1 -664.9338600132171 -307.3328942731508 -664.9338600132171
ddot R: 0.049763349351 1.692918082150
Iter cpu 236.1 315.6 1
I ran it on Kogence 128-core instance
https://kogence.com/app/jobs/files/list/-632%5ETransition_state_of_Cubane_and_azo-Cubane_t....
and have got
Symmetry of references
Ref. 1 sym:a
Ref. 2 sym:a
MR MkCCSD, version 1.0
Heff
=============================================
0 1 -664.87322151 0.09099694
0 2 0.09099694 -664.79730579
Eigenvalues (real and imaginary)
=============================================
-664.933860013248 0.00000000
-664.736667291216 0.00000000
Left eigenvectors
=============================================
1 -0.83216055 -0.55453478
2 0.55453478 -0.83216055
Right eigenvectors
=============================================
VR 1 -0.83216055 -0.55453478
VR 2 0.55453478 -0.83216055
Target root: 1
MkCC iter. # 1 -664.9338600132476 -307.3328942731813 -664.9338600132476
ydot R: 0.043047295224 1.692940929883
Iter cpu 3.7 64.3 1
128-core isn't faster 32 times, it faster only 5 times.
Why this happens?
Best Vladimir.
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Edited On 9:19:36 PM PDT - Sat, Sep 30th 2017 by Vladimir
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| 4:00:55 AM PDT - Thu, Oct 5th 2017 |
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| Nothing scales perfectly. Your calculation isn't big enough to expect reasonable scaling to 128 cores.
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Clicked A Few Times
Threads 1
Posts 5
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| 8:13:51 AM PDT - Mon, Oct 9th 2017 |
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Re: MK-CCSD on Kogence isn't much faster as expected
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Vladimir,
I suggest you try running same problem on 8 threads, 16 threads and 32 threads on Kogence and see how computational time is scaling. I am guessing may be able to see that performance improves linearly initially and sub-linearly later.
As Sean said, most computational algorithms do not scale linearly. Scaling is also problem dependent.
You also have to understand what is taking time. If you try to run a very small problem on a 128 core computer, you dont expect to see much benefit because overhead is more expense and your problem is small enough to not compensate for overhead.
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Gets Around
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| 8:20:54 AM PDT - Tue, Oct 10th 2017 |
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Quote:JenniferCarter Oct 9th 11:13 pmVladimir,
I suggest you try running same problem on 8 threads, 16 threads and 32 threads on Kogence and see how computational time is scaling. I am guessing may be able to see that performance improves linearly initially and sub-linearly later.
As Sean said, most computational algorithms do not scale linearly. Scaling is also problem dependent.
You also have to understand what is taking time. If you try to run a very small problem on a 128 core computer, you dont expect to see much benefit because overhead is more expense and your problem is small enough to not compensate for overhead.
I apologize for the fact that I did not quite accurately formulate my question.
I would like to draw attention to the fact that only Multireference CC iterations not scaling linearly.
I want to ask you what settings I should choose for the MRCC method to achieve the highest performance on 64-128 cores
http://www.nwchem-sw.org/index.php/Release66:TCE#State-Specific_Multireference_Coupled_Clu...
P.S. to Jennifer
NWCHEM has many methods of calculating and many parameters to adjust their performance. Possible default settings do not fit.
can You describe in more detail the topology of your computer and in particular the network through which the data is exchanged between processors and with which keys NWCHEM was compiled in accordance with this.
Best, Vladimir.
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Edited On 8:25:57 AM PDT - Tue, Oct 10th 2017 by Vladimir
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