Mostrando postagens com marcador slack. Mostrar todas as postagens
Mostrando postagens com marcador slack. Mostrar todas as postagens

quarta-feira, 3 de abril de 2013

QuTiP 2.2.0 packages (Slackware64-current 20130331) with patch

mcsolve_f90 not work (qutip 2.2.0 20130301), dont have blas library dependencies in the qutraj_run.so library
***** the patch ***** 
--- ./qutip/fortran/setup.py.orig       2013-03-01 04:49:31.000000000 -0300
+++ ./qutip/fortran/setup.py    2013-04-03 22:29:32.114191894 -0300
@@ -42,7 +42,7 @@
     if not lapack_opt:
         blas_opt = get_info('blas', notfound_action=2)
     else:
-        blas_opt = lapack_opt
+        blas_opt = get_info('blas', notfound_action=1)

     # Remove libraries key from blas_opt
     if 'libraries' in blas_opt:  # key doesn't exist on OS X ...

***********************
d340344fa72098276ca41a76cdf377e9  Cython-0.18-x86_64-1.txz
3b1bfbeeaf59f6683f38aa2fe3d2a67a  QuTiP-2.2.0-x86_64-1.txz
2481a78855f9d267c7f27c2cf2f93fb1  apiextractor-0.10.10-x86_64-2.txz
32145606c53e7e3a037f379547acdfce  blas-20110419-x86_64-3.txz
1fdd7b25d04996e2e240a90f4fae92c2  generatorrunner-0.6.16-x86_64-2.txz
201fde17c76e7dd88341e748a5bab7db  ipython-0.13.1-x86_64-1.txz
12c53c6efcfe7e2a91dbe724b33ea537  lapack-3.4.2-x86_64-2.txz
c9b79e07630931f52f1612daa3ab572b  matplotlib-1.2.1-x86_64-1.txz
b0f7603ea8925262649ee5b31008241b  nose-1.2.1-x86_64-1.txz
9fb835e2d268333efe00595c53ac6da0  numpy-1.7.0-x86_64-1.txz
03ce18e89658c8abf9c534b40a57505f  pysetuptools-0.6c11-x86_64-2.txz
64b584595a2c1d0da368c75396158e6f  pyside-qt4.8+1.1.2-x86_64-1.txz
b4e97d03d781b95d87a4ed21c1819bbe  python-dateutil-1.5-x86_64-2.txz
a8af1cec2c85ef9c9850013379e6c6d0  pytz-2013b-x86_64-1.txz
2dd6dadd6484d2ff6bcd06841d070689  scipy-0.11.0-x86_64-1.txz
e04240f5eb4b83e0709c140aaf83b275  shiboken-1.1.2-x86_64-1.txz

Google drve link
qutip-2.2.0-packages.tar

>>> import qutip.testing as qt
>>> qt.run()
Qobj data ... ok
Qobj type ... ok
Qobj Hermicity ... ok
Qobj shape ... ok
Qobj addition ... ok
Qobj subtraction ... ok
Qobj multiplication ... ok
Qobj division ... ok
Qobj power ... ok
Qobj negation ... ok
Qobj equals ... ok
Qobj getitem ... ok
Qobj multiplication type ... ok
Qobj conjugate ... ok
Qobj adjoint (dagger) ... ok
Qobj diagonals ... ok
Qobj eigenenergies ... ok
Qobj eigenstates ... ok
Qobj expm ... ok
Qobj full ... ok
Qobj ket type ... ok
Qobj bra type ... ok
Qobj operator type ... ok
Qobj superoperator type ... ok
Transform 2-level to eigenbasis and back ... ok
Transform 10-level real-values to eigenbasis and back ... ok
Transform 10-level to eigenbasis and back ... ok
Transform 10-level imag to eigenbasis and back ... ok
Consistency between transformations of kets and denstity matrices ... ok
correlation: comparing me and es for oscillator in coherent initial state ... ok
correlation: comparing me and es for oscillator in steady state ... ok
correlation: compare spectrum obtained for eseries and fft methods ... ok
Diagonalization of random two-level system ... ok
Diagonalization of composite systems ... ok
von-Neumann entropy ... ok
Linear entropy ... ok
Concurrence ... ok
Mutual information ... ok
Conditional entropy ... ok
Read and write complex valued decimal formatted data ... ok
Read and write complex valued default formatted data ... ok
Read and write complex valued exp formatted data ... ok
Read and write real valued decimal formatted data ... ok
Read and write real valued default formatted data ... ok
Read and write real valued exp formatted data ... ok
Read and write with automatic separator detection ... ok
Floquet: test unitary evolution of time-dependent two-level system ... ok
Monte-carlo: Constant H with no collapse ops (expect) ... ok
Monte-carlo: Constant H with no collapse ops (states) ... ok
Monte-carlo: Constant H (str format) with no collapse ops (expect) ... ok
Monte-carlo: Constant H (func format) with no collapse ops (expect) ... ok
Monte-carlo: Constant H (str format) with no collapse ops (states) ... ok
Monte-carlo: Constant H (func format) with no collapse ops (states) ... ok
Monte-carlo: Constant H with constant collapse ... ok
Monte-carlo: Constant H with single collapse operator ... ok
Monte-carlo: Constant H with single expect operator ... ok
Monte-carlo: Collapse terms constant (func format) ... ok
Monte-carlo: Collapse terms constant (str format) ... ok
Monte-carlo: Time-dependent H (func format) ... ok
Monte-carlo: Time-dependent H (str format) ... ok
Monte-carlo: check for correct dtypes (mc_avg=True) ... ok
Monte-carlo: check for correct dtypes (mc_avg=False) ... ok
mcsolve_f90: Constant H with no collapse ops (expect) ... ok
mcsolve_f90: Constant H with no collapse ops (states) ... ok
mcsolve_f90: Constant H with constant collapse ... ok
mcsolve_f90: Constant H with single collapse operator ... ok
mcsolve_f90: Constant H with single expect operator ... ok
mcsolve_f90: check for correct dtypes (mc_avg=True) ... ok
mcsolve_f90: check for correct dtypes (mc_avg=False) ... ok
mesolve: cavity-qubit interaction, no dissipation ... ok
mesolve: cavity-qubit without interaction, decay ... ok
mesolve: cavity-qubit with interaction, decay ... ok
mesolve: qubit without dissipation ... ok
mesolve: simple time-dependence as function list ... ok
mesolve: simple time-dependence as string list ... ok
mesolve: simple constant decay ... ok
mesolve: constant decay as function list ... ok
mesolve: constant decay as string list ... ok
mesolve: simple constant decay ... ok
mesolve: simple constant decay ... ok
odechecks: monte-carlo ... ok
Spin 1/2 operators ... ok
Spin 3/2 operators ... ok
Spin 2 operators ... ok
Spin 5/2 operators ... ok
Destruction operator ... ok
Creation operator ... ok
Identity operator ... ok
Number operator ... ok
Squeezing operator ... ok
Displacement operator ... ok
parfor ... ok
partial transpose of bipartite systems ... ok
partial transpose: comparing sparse and dense implementations ... ok
partial transpose: randomized tests on tripartite system ... ok
quantum process tomography for snot gate ... ok
quantum process tomography for cnot gate ... ok
Test mesolve qubit, with dissipation ... ok
Test mesolve qubit, no dissipation ... ok
Test essolve qubit, with dissipation ... ok
Test mcsolve qubit, with dissipation ... ok
Test mcsolve qubit, no dissipation ... ok
random Unitary ... ok
random density matrix ... ok
random hermitian ... ok
random ket ... ok
Sparse eigs Hermitian ... ok
Sparse eigs non-Hermitian ... ok
Sparse eigvals only Hermitian. ... ok
Dense eigs Hermitian. ... ok
Dense eigs non-Hermitian ... ok
Dense eigvals only Hermitian ... ok
states: coherent density matrix ... ok
states: Fock density matrix ... ok
states: thermal density matrix ... ok
Steady state: Thermal qubit ... ok
Steady state: Thermal harmonic oscillator ... ok
Superoperator: Conversion matrix to vector to matrix ... ok
Superoperator: Test compability between matrix/vector conversion and ... ok
Superoperator: Conversion between matrix and vector indices ... ok
Superoperator: Conversion vector to matrix to vector ... ok
wigner: test wigner function calculation for coherent states ... ok
wigner: test wigner function calculation for Fock states ... ok
wigner: compare wigner methods for random density matrices ... ok
wigner: compare wigner methods for random state vectors ... ok

----------------------------------------------------------------------
Ran 125 tests in 316.766s

OK

sexta-feira, 25 de janeiro de 2013

Benchmark on GPU HD7970 / LAMMPS

AMD FX-8150@4.086GHz / GPU XFX HD7970 GHz edition / MPICH 3.0.1 / Open64 4.5.2 (AMD) / APP SDK 2.8 / LAMMPS 20130121 (compiled with -O3)

Results for Rhodopsin
On the FX-8150
Single core 45.9495 seconds
4 cores 11.94 seconds
8 cores 7.06722 seconds

On the GPU HD7970 GHz edition
1 proc 4.8 seconds
2 procs 3.67 seconds

Results with FirePro3D V8800 and Tesla C2050
https://sites.google.com/site/akohlmey/news-and-announcements/gpuacceleratedlammpsonamdgpus

Results for EAM

1 procs
mpirun -n 1 ./lmp_mpich  -sf gpu -c off -v g 1 -v x 80 -v y 80 -v z 80 -v t 100 < in.eam.gpu
--------------------------------------------------------------------------
GPU 0: Tahiti, 256 cores, 2.9 GB, 1.1 GHZ (Double Precision)
--------------------------------------------------------------------------

Initializing GPU and compiling on process 0...Done.
Initializing GPU 0 on core 0...Done.

Setting up run ...
Memory usage per processor = 426.905 Mbytes
Step Temp E_pair E_mol TotEng Press
       0         1600     -7249920            0   -6826360.6    18704.149
      50    780.81547   -7031660.9            0   -6824959.8    52291.364
     100    798.21786   -7036295.3            0   -6824987.4    51479.467
Loop time of 27.893 on 1 procs for 100 steps with 2048000 atoms


2 procs

mpirun -n 2 ./lmp_mpich  -sf gpu -c off -v g 1 -v x 80 -v y 80 -v z 80 -v t 100 < in.eam.gpu
--------------------------------------------------------------------------
GPU 0: Tahiti, 256 cores, 2.9 GB, 1.1 GHZ (Double Precision)
--------------------------------------------------------------------------

Initializing GPU and compiling on process 0...Done.
Initializing GPU 0 on core 0...Done.
Initializing GPU 0 on core 1...Done.

Setting up run ...
Memory usage per processor = 225.757 Mbytes
Step Temp E_pair E_mol TotEng Press
       0         1600     -7249920            0   -6826360.6    18704.149
      50    780.81547   -7031660.9            0   -6824959.8    52291.364
     100    798.21786   -7036295.3            0   -6824987.4    51479.467
Loop time of 22.4219 on 2 procs for 100 steps with 2048000 atoms



mpirun -n 1 ./lmp_cuda  -sf cuda -v g 2 -v x 80 -v y 80 -v z 80 -v t 100 < in.eam.cuda

LAMMPS (21 Jan 2013)
# Using LAMMPS_CUDA
USER-CUDA mode is enabled (lammps.cpp:393)
# CUDA: Activate GPU
# Using device 0: GeForce GTX 580
Lattice spacing in x,y,z = 3.615 3.615 3.615
Created orthogonal box = (0 0 0) to (289.2 289.2 289.2)
  1 by 1 by 1 MPI processor grid
Created 2048000 atoms
# CUDA: VerletCuda::setup: Allocate memory on device for maximum of 2050000 atoms...
# CUDA: Using precision: Global: 8 X: 8 V: 8 F: 8 PPPM: 8
Setting up run ...
# CUDA: VerletCuda::setup: Upload data...
# CUDA: Total Device Memory useage post setup: 1314.117188 MB
Memory usage per processor = 416.017 Mbytes
Step Temp E_pair E_mol TotEng Press
       0         1600     -7249920            0   -6826360.6    18704.149
      50    780.81547   -7031660.9            0   -6824959.8    52291.364
     100    798.21786   -7036295.3            0   -6824987.4    51479.467
Loop time of 20.0248 on 1 procs for 100 steps with 2048000 atoms
 




segunda-feira, 13 de agosto de 2012

open64 4.5.2 patch


Some patchs for errors compiling x86 Open64 4.5.2 on Slackware64
perl 5.16 obsolete getopts, ( _SC_CLK_TCK + sysconf) errors,  g++:error:unrecognized option '-CG:all_sched=0'
***********************************************
--- ./osprey/be/opt/opt_emit.cxx.orig   2012-08-08 08:14:06.000000000 -0300
+++ ./osprey/be/opt/opt_emit.cxx        2012-08-12 20:03:04.850966207 -0300
@@ -78,6 +78,7 @@

 #include
 #include
+#include
 #include "defs.h"
 #include "tracing.h"
 #include "erglob.h"
--- ./osprey/ipa/local/Makefile.gbase.orig      2012-08-08 08:14:43.000000000 -0300
+++ ./osprey/ipa/local/Makefile.gbase   2012-08-12 20:30:36.601746311 -0300
@@ -338,4 +338,6 @@
 #----------------------------------------------------------------------
 #  Temporary workaround
 #----------------------------------------------------------------------
+ifeq ($(BUILD_COMPILER), OPEN64)
 ipl_summarize_util.o: OPTIMIZER += -CG:all_sched=0
+endif
\ No newline at end of file
--- ./osprey/common/util/gen_x_set.orig 2012-08-08 08:14:37.000000000 -0300
+++ ./osprey/common/util/gen_x_set      2012-08-09 17:17:02.046382148 -0300
@@ -111,7 +111,8 @@
 ### ====================================================================
 ### ====================================================================

-require "getopts.pl";
+#require "getopts.pl";
+use Getopt::Std;

 # Look for the templates in the same directory as this script resides.
 #
@@ -119,7 +120,8 @@
 $utildir =~ s=/[^/]*$==;
 $utildir = '.' if ( $utildir eq $0 );

-&Getopts("fs");
+#&Getopts("fs");
+getopts("fs");

 # Macro or functional interface?
 #

terça-feira, 14 de fevereiro de 2012

Driver fglrx no Slackware

O driver que a AMD fornece para o linux gera o pacote do slackware. É muito interessante criar o pacote do que instalar diretamente, pois assim podemos remover-los mais facilmente. Desde a versão posteriores a 11.8 (8.881), eu não estava conseguindo usar essas versões novas, dava erro e não sabia o que fazer para corrigir o erro. Depois de muita procura no google, finalmente ví num forum que precisa por na blacklist o drm, fora que a radeon também na blacklist.

Então só acrescentar no arquivo /etc/modprobe.d/blacklist.conf


blacklist radeon
blacklist drm


aticonfig --initial
por  as linhas no /etc/X11/xorg.conf
Section "DRI"
        Group 0
        Mode  0666
EndSection

quinta-feira, 27 de outubro de 2011

QuTiP: The Quantum Toolbox in Python - Slackware64 13.37 packages

QuTiP is an open source framework designed for solving the dynamics of open quantum systems. More informations in http://code.google.com/p/qutip/

My slackpackages are hosted on google docs server.
https://docs.google.com/open?id=0Bz0A3IDwzSRjZDA5OTM1YWMtM2FiMi00M2EyLWJhM2EtOWEzNzNiN2UyNWMz

The packages was builded for Slackware64 13.37 + KDE 4.7.2 (Alien Bob packages)
md5sum
0f9c1793ba0dd36c5439dc43f1e32340  QuTiP-1.1.2-x86_64-1.txz
826b4f2ea0ffc204780704ee56dbaf52  Cython-0.15.1-x86_64-1.txz
4729b571d0c0826913a82fafe4b48f71  apiextractor-0.10.8-x86_64-1.txz
84e89828ba6f23bf1d7b10dddb68e501  blas-20110419-x86_64-1.txz
d3c00d7ef2d667a483781ddeae71f20a  lapack-3.3.1-x86_64-1.txz
d5f5340590b847810f8b24b1d9de361a  matplotlib-1.1.0-x86_64-1.txz
aec216cb9767831b38f16dfe4d632a62  numpy-1.6.1-x86_64-1.txz
0f8e6fb975f32ef6cc0740ae9a040fd2  pysetuptools-0.6c11-x86_64-1.txz
841c9ad6a66c8ca8649fe91f9af46d3e  pyside-qt4.7+1.0.8-x86_64-1.txz
81373ae2259534e9ec7b92d1dd723a8c  python-dateutil-1.5-x86_64-1.txz
5e610c4bc3a9d1382ab0846d729197de  pytz-2011d-x86_64-1.txz
7f728e394e4cd75e8b6462aa01efb565  scipy-0.9.0-x86_64-1.txz
c3579afe818e083b00c14562c8ba18bf  shiboken-1.0.9-x86_64-1.txz




from qutip import *
from matplotlib import *
from numpy import *
from pylab import *
H = 2 * pi * sigmax()
psi0 = fock(2, 0)
tlist = arange(0.0, 4.0, 0.01)
expt_sz = odesolve(H, psi0, tlist,[], [sigmaz()])
plot(tlist, expt_sz[0])
show()