code base changed

milestone_2
Oli 3 years ago
parent 753a7d38cf
commit 30c4715f14

4
.gitignore vendored

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petsc/
petsc4py/
openmpi-2.1.1/
openmpi-2.1.1.tar.bz2

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#!/usr/bin/env bash
#SBATCH -J test_fftma
#SBATCH -N 4
#SBATCH --ntasks-per-node 32
#SBATCH --output %j_output.out
#SBATCH -p free-rider
#SBATCH -t 2-23:00:00
#source activate_env.sh
ulimit -v 115343360
k=0
for i in {1..4} # num de jobs totales divido num de cores (redondear para arriba)
do
for j in {1..1} #Numero de "carpetas" en donde resuelve simultanemente
do
mpirun -np 4 python ./tools/solver/Ndar.py $k 1 & # job, refinamiento
k=$(($k+1))
done
wait
done
wait

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#!/usr/bin/env bash
for i in {0..49}
do
mpirun python ./tools/solver/Ndar.py $i 2
done

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#!/usr/bin/env bash
#SBATCH -J test_fftma
#SBATCH -N 1
#SBATCH --ntasks-per-node 32
#SBATCH --output %j_output.out
#SBATCH -p free-rider
#SBATCH -t 2-23:00:00
source activate_env.sh
ulimit -v 115343360
for i in {1..4}
do
mpirun -np 32 python ./tools/solver/Ndar.py ./paper2_SinRef/$i/ 1 # job, refinamiento
k=$(($k+1))
done
for i in {1..4}
do
mpirun -np 32 python ./tools/solver/Ndar.py ./paper2_ref4/$i/ 4 # job, refinamiento
k=$(($k+1))
done

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#!/usr/bin/env bash
#SBATCH -J test_fftma
#SBATCH -N 4
#SBATCH --ntasks-per-node 32
#SBATCH --output %j_output.out
#SBATCH -p free-rider
#SBATCH -t 2-23:00:00
source activate_env.sh
ulimit -v 115343360
mpirun python -np 32 mpirunner.py

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export PATH=$HOME/python/Python-2.7.13/:$PATH
export PYTHONPATH=$HOME/python/Python-2.7.13
export PATH=$HOME/.local/bin:$PATH
export PETSC_DIR=$HOME/soft/petsc

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#!/usr/bin/env bash
#SBATCH -J connec3d_parte2
#SBATCH -N 2
#SBATCH --ntasks-per-node 32
#SBATCH --output %j_output.out
#SBATCH -p free-rider
#SBATCH -t 2-23:00:00
source activate_env.sh
ulimit -v 115343360
mpirun python -np 64 mpirunner.py

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#!/usr/bin/env bash
#SBATCH -J connec3d_parte2
#SBATCH -N 2
#SBATCH --ntasks-per-node 32
#SBATCH --output %j_output.out
#SBATCH -p free-rider
#SBATCH -t 2-23:00:00
for i in {0..4}
do
mpirun -np 32 python ./tools/solver/Ndar.py ./data/$i/ 1 # job, refinamiento
done

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[General]
simDir=output
startJob=0
[Iterables]
p=[10,39,15]
seeds=[5462,2]
lc=[4]
connectivity=[1,2,3]
variances=[1]
[Generation]
Nx = 16
Ny = 16
Nz = 16
variogram_type=1
binary = yes
kh = 100
kl = 0.01
compute_lc = yes
lcBin=yes
genera=yes
[Connectivity]
keep_aspect= yes
block_size = 4
indicators_MinBlockSize =4
Max_sample_size = 12
compGconec= 1
conec=yes
[Solver]
num_of_cores = 1
ref = 2
solve = yes
rtol = 1e-4
[K-Postprocess]
MinBlockSize =1
Max_sample_size = 4
kperm=yes
postprocess=yes
SaveVfield=yes

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import numpy as np
from mpi4py import MPI
#from tools.realization import realization
from tools.generation.config import DotheLoop, get_config
import os
import sys
from tools.Prealization import realization
def main():
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
pn = comm.Get_size()
if pn==1:
sequential()
return
if rank==0:
manager()
else:
worker()
return
def sequential():
comm = MPI.COMM_WORLD
conffile = './config.ini'
parser,iterables = get_config(conffile)
njobs = DotheLoop(-1,parser,iterables)
start_job=0
for job in range(start_job,njobs):
realization(job)
return
def manager():
comm = MPI.COMM_WORLD
conffile = './config.ini'
parser,iterables = get_config(conffile)
njobs = DotheLoop(-1,parser,iterables)
start_job=0
for job in range(start_job,njobs):
dest=comm.recv(source=MPI.ANY_SOURCE)
comm.send(job,dest=dest)
for i in range(comm.Get_size()-1):
dest=comm.recv(source=MPI.ANY_SOURCE)
comm.send(-1,dest=dest)
return
def worker():
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
job=1
while job!=-1:
comm.send(rank,dest=0)
job = comm.recv(source=0)
realization(job)
return
main()

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#!/bin/bash
source ~/miniconda3/etc/profile.d/conda.sh
conda activate py2_7
python --version
mpirun python mpirunner.py

@ -26,11 +26,13 @@ source ~/.$1rc
cd .. cd ..
rm openmpi-2.1.1.tar.bz2
# install miniconda # install miniconda
curl https://repo.anaconda.com/miniconda/Miniconda3-py39_4.10.3-Linux-x86_64.sh --output Miniconda3-py39_4.10.3-Linux-x86_64.sh curl https://repo.anaconda.com/miniconda/Miniconda3-py39_4.10.3-Linux-x86_64.sh --output Miniconda3-py39_4.10.3-Linux-x86_64.sh
bash Miniconda3-py39_4.10.3-Linux-x86_64.sh -b $1 Miniconda3-py39_4.10.3-Linux-x86_64.sh -b
source ~/.$1rc source ~/.$1rc
@ -40,9 +42,16 @@ conda init $1
source ~/.$1rc source ~/.$1rc
rm Miniconda3-py39_4.10.3-Linux-x86_64.sh
conda create -n py2_7 python=2.7 conda create -n py2_7 python=2.7
source ~/miniconda3/etc/profile.d/conda.sh
conda activate py2_7 conda activate py2_7
python --version
pip install --user numpy scipy matplotlib ipython jupyter pandas sympy nose pip install --user numpy scipy matplotlib ipython jupyter pandas sympy nose
# setup petsc # setup petsc
@ -69,3 +78,4 @@ export PETSC_DIR=$WORKSPACE/petsc
python setup.py build python setup.py build
python setup.py install --user python setup.py install --user
python setup.py test python setup.py test
cd ..

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# nbl,ttotal,tcmaps/ttotal,PostConTime/ttotal
4.000000000000000000e+00
4.437680244445800781e-01
8.036789380314167675e-01
4.117019413334135902e-02

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# Runtime,Nx,Ny,Nz,con,lc,p,kh,kl,seed,variance,vario
4.421949386596679688e-03
1.600000000000000000e+01
1.600000000000000000e+01
1.600000000000000000e+01
1.000000000000000000e+00
4.000000000000000000e+00
1.000000000000000056e-01
1.000000000000000000e+02
1.000000000000000021e-02
5.462000000000000000e+03
0.000000000000000000e+00
1.000000000000000000e+00

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# K_diss, K_average,ttotal,tDiss/ttotal
2.044456438450047153e-02
2.093990598307741979e-02
1.699991226196289062e-01
8.312436538256562021e-03

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ref: 2
Matrix creation: 0.322577953339
Solver: 0.312026023865
Keff: 0.02097657095221267
N_cores: 1

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# Keff, ref, Runtime, N_cores
2.097657095221266976e-02
2.000000000000000000e+00
6.346039772033691406e-01
1.000000000000000000e+00

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6-CONNECTIVITY ANALYSIS
PROPORTION OF PHASE 1 IS: 0.00000000

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# lcG, lcNst, lcBin
4.617108983572631331e+00
4.617108983572631331e+00
5.221092552049793944e+00

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4.433069229125976562e-01
1.709461212158203125e-04
1.268386840820312500e-04

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# nbl,ttotal,tcmaps/ttotal,PostConTime/ttotal
4.000000000000000000e+00
4.130840301513671875e-01
8.103774673900496772e-01
3.892242871984301356e-02

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# Runtime,Nx,Ny,Nz,con,lc,p,kh,kl,seed,variance,vario
6.468057632446289062e-03
1.600000000000000000e+01
1.600000000000000000e+01
1.600000000000000000e+01
1.000000000000000000e+00
4.000000000000000000e+00
1.000000000000000056e-01
1.000000000000000000e+02
1.000000000000000021e-02
5.463000000000000000e+03
0.000000000000000000e+00
1.000000000000000000e+00

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# K_diss, K_average,ttotal,tDiss/ttotal
2.226209553813355765e-02
2.286500870826872062e-02
4.626035690307617188e-03
3.054682265629026228e-01

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ref: 2
Matrix creation: 0.324597120285
Solver: 0.276505947113
Keff: 0.022595141113124082
N_cores: 1

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# Keff, ref, Runtime, N_cores
2.259514111312408216e-02
2.000000000000000000e+00
6.011030673980712891e-01
1.000000000000000000e+00

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6-CONNECTIVITY ANALYSIS
PROPORTION OF PHASE 1 IS: 0.125000000
NCC : 1
NCC : 2
NCC : 3

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# lcG, lcNst, lcBin
8.214006169837110960e+00
8.214006169837110960e+00
2.860537317826781223e+00

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3.183081150054931641e-01
1.590251922607421875e-04

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# nbl,ttotal,tcmaps/ttotal,PostConTime/ttotal
4.000000000000000000e+00
5.578839778900146484e-01
8.596443918313970700e-01
2.408955804327898159e-02

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# Runtime,Nx,Ny,Nz,con,lc,p,kh,kl,seed,variance,vario
1.008915901184082031e-02
1.600000000000000000e+01
1.600000000000000000e+01
1.600000000000000000e+01
1.000000000000000000e+00
4.000000000000000000e+00
2.035714285714285698e-01
1.000000000000000000e+02
1.000000000000000021e-02
5.462000000000000000e+03
0.000000000000000000e+00
1.000000000000000000e+00

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# K_diss, K_average,ttotal,tDiss/ttotal
4.053728556426364898e-02
4.123944443032588614e-02
1.776230335235595703e-01
6.186535660834491246e-03

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ref: 2
Matrix creation: 0.356295824051
Solver: 0.41558098793
Keff: 0.041274959938125966
N_cores: 1

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# Keff, ref, Runtime, N_cores
4.127495993812596575e-02
2.000000000000000000e+00
7.718768119812011719e-01
1.000000000000000000e+00

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6-CONNECTIVITY ANALYSIS
PROPORTION OF PHASE 1 IS: 4.68750000E-02
NCC : 1

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