From: Santiago Torres <Santiago.Torres@arup.com>
Date: September 22, 2017 9:13:50 AM PDT
Dear Sarith, Greg,
Thank you for your replies. In this case I am running the annual calculation of an image, and each time-step (daytime) is taking 3-4 minutes to run, so the total calculation takes more than 9 days. And I would like to increase the resolution to reduce noise…
So I thought if I run 10 dctimestep processes in parallel, it should take less than a day. I hope this makes sense. I will try it out and let you know if I find problems.
From: Sarith Subramaniam [mailto:firstname.lastname@example.org]
Sent: 22 September 2017 16:53
I pretty much did the exact same thing that you mentioned to run multiple instances of dctimestep with Python. The syntax is something like:
import multiprocessing as mp
#Run dctimesteps, 16 at at time.
#runCommand is just a call to os.system
#commands is a list of dctimestep commands like ["dctimestep dc.mtx sky0001.smx > res0001.smx", "dctimestep dc.mtx sky0002.smx > res0002.smx" ...]
pools = mp.Pool(processes=16)
For the above, you'd have to do some additional scripting to run rmtxop and compile all the results into a single file. There is also a method that was discussed by Wangda Zuo and Andy Mcneil in this paper: http://www.ibpsa.org/proceedings/BS2011/P_1155.pdf
On 9/22/2017 9:55 AM, Santiago Torres wrote:
This might be a silly question, but I couldn't find any reference. Is there a way to run dctimestep in parallel?
Or, would it be possible to create a weather tape for each month, create a matrix from each, and run each month separately?