# Re: AW: [NMusers] IMP and parallelisation

```Hi Dirk,
```
What do you mean "does not solve the issue"? Were the results identical with different number of nodes or not?
```Thanks
Leonid```
```
On 9/20/2016 9:47 AM, Dirk Garmann wrote:
```
```Thank you Leonid,
We have tried RANMETHOD=P, which is an interesting possibility.
Unfortunately this does not solve the issue. We will further evaluate if the
information from all nodes is used for the population update.
Any further hints are highly welcome

Best
Dirk

-----Ursprüngliche Nachricht-----
Von: Leonid Gibiansky [mailto:lgibian...@quantpharm.com]
Gesendet: Montag, 19. September 2016 22:26
An: Dirk Garmann; nmusers@globomaxnm.com
Betreff: Re: [NMusers] IMP and parallelisation

It is a good idea to use RANMETHOD=P at estimation step; then the
results should be identical independently of the number of nodes and

Concerning specific behavior .. looks strange. I would try to start from
the initial values of the model with the lowest OF and see what happens.

Thanks
Leonid

On 9/19/2016 1:29 PM, Dirk Garmann wrote:
```
```Dear nmusers.

During a popPK analysis using the M3 method and IMP we observed an
unexpected behavior and would be interested if anyone else observed the
same and can provide guidance/explanations.

The IMP produces "strange" results in cases requiring a parallelization.

We observed  a general (and strong)  trend that with increasing number
of nodes the OBF increases (!) which in my opinion is unexpected  if the
number of samples in MC is sufficiently large.

The initial settings have been as follows:

Parse Type 1

\$EST METHOD=IMP INTERACTION LAPLACIAN EONLY=0 ISAMPLE=300 NITER=1000
CTYPE=3 NOABORT GRD=SN(1,2) NOTHETABOUNDTEST PRINT=1

\$EST METHOD=IMP INTERACTION NOABORT GRD=SN(1,2) EONLY=1 ISAMPLE=3000
NITER=30 PRINT=1

With 1 node the OBF decreased to ~- 1400

Using 16 nodes the OBF stabilized at ~ 1000

In both cases the OBF does not fluctuate much after 100 interations
(monitoring of EM step) and seems to be stable (no clear hint for a
local minima).

Interestingly the estimated residual error is higher using 1 node. With
16 nodes the variability seems to be shifted to the ETAS.

This behavior might be a concern for a covariate analysis using IMP

Our first assumption was that we need to increase iSAMPLE in the EM
step, since a different seed might be used for each node. However even
increasing ISAMPLE to 3000 in the first step did not change the results
much.

My guess is that it points in the direction of how population values are
updated, but I am not an expert in the implementation of IMP in NONMEM

We would be highly interested in any guidance and explanation.

Dirk

Freundliche Grüße / Best regards,

Dirk Garmann

Bayer Pharma Aktiengesellschaft

BPH-DD-CS-CP-QP, Quantitative Pharmacology

Building 0431, 322

51368 Leverkusen, Germany

Tel:        +49 202 365577

Fax:

Mobile: +49 175 3109407

E-mail:   _dirk.garmann@bayer.com_

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Vorstand: Dieter Weinand, Vorsitzender | Christoph Bertram

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Sitz der Gesellschaft: Berlin | Amtsgericht Charlottenburg, HRB 283 B

```
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```