Hi Lorenzo:
This is more of a question for the python community. However, a couple
things I have noticed. Pandas tends to be much slower than working in
numpy directly.
I never saw an improvement in timings when using Pool(). What I do is
utilize Process() and Queue() or JoinableQueue() from th
Dear Lorenzo,
On 03/03/2016 12:13 PM, Lorenzo Bottaccioli wrote:
Yes I have 8 cores! The I/O output files are different for each process.
I have to preform the gdal_calc on different maps each process. I just
wanted to lunch more than one gdal_calc.py script at time.
Yes, I assumed the files u
Dear Kor,
Yes I have 8 cores! The I/O output files are different for each process. I
have to preform the gdal_calc on different maps each process. I just wanted
to lunch more than one gdal_calc.py script at time.
Best
Lorenzo
2016-03-03 11:34 GMT+01:00 Kor de Jong :
> Dear Lorenzo,
>
> On 03/0
Dear Lorenzo,
On 03/03/2016 12:44 AM, Lorenzo Bottaccioli wrote:
If i run the code with out parallelization it takes around 650s to
complete the calculation. Each process of the for loop is executed in
~10s. If i run with parallelization it takes ~900s to complete the
procces and each process of
On my phone so can explain fully, but there are several blockers in GDAL
Library that prevent multi threading from being effective. Try using different
processes if it is completely required.
Blake Thompson
> On Mar 2, 2016, at 5:44 PM, Lorenzo Bottaccioli
> wrote:
>
> Hi,
> I'm trying to pa
Hi,
I'm trying to parallelize a code for raster calculation with Gdal_calc.py,
but i have relay bad results. I need to perform several raster operation
like FILE_out=FILA_a*k1+FILE_b*k2.
This is the code I'm usign:
import pandas as pdimport osimport timefrom multiprocessing import Pool
df = pd.r
