OpenMPI

mpi4py OpenMPI

This package provides Python bindings for the Message Passing Interface (MPI) standard. It is implemented on top of the MPI-1/2/3 specification and exposes an API which grounds on the standard MPI-2 C++ bindings.

relax manual on Multi processor usage

If you have OpenMPI and mpi4py installed, then you have access to Gary Thompson's multi-processor framework for MPI parallelisation.

Dependencies

1. Python 2.4 to 2.7 or 3.0 to 3.4, or a recent PyPy release.
2. A functional MPI 1.x/2.x/3.x implementation like MPICH or Open MPI built with shared/dynamic libraries.

Install

Linux and Mac

Remember to check, if there are newer versions of mpi4py.
The mpi4py library can be installed on all UNIX systems by typing:

# Change to bash, if in tcsh shell
bash

v=1.3.1

cd $HOME/Downloads
curl https://bitbucket.org/mpi4py/mpi4py/downloads/mpi4py-$v.tar.gz -o mpi4py-$v.tar.gz
tar -xzf mpi4py-$v.tar.gz
cd mpi4py-$v
# Then either
sudo pip install -e .
# Or
pip install .

cd ..

About

The code in relax must be written to support this. This is the case for the model-free analysis, in which case Gary has achieved near perfect scaling efficiency:

https://mail.gna.org/public/relax-devel/2007-05/msg00000.html

Update 2013/09/11

See Commit

Huge speed win for the relaxation dispersion analysis - optimisation now uses the multi-processor.

The relaxation dispersion optimisation has been parallelised at the level of the spin clustering.
It uses Gary Thompson's multi-processor framework. This allows the code to run on multi-core, multi -processor systems, clusters, grids, and anywhere the OpenMPI protocol is available.

Because the parallelisation is at the cluster level there are some situations, whereby instead of optimisation being faster when running on multiple slaves, the optimisation will be slower.
This is the case when all spins being studied is clustered into a small number of clusters. Example 100 spins into 1 cluster.
It is also likely to be slower for the minimise user function when no clustering is defined, due to the overhead costs of data transfer (but for the numeric models, in this case there will be a clear win).

The two situations where there will be a huge performance win' is the grid_search user function when no clustering is defined and the Monte Carlo simulations for error analysis.

Test of speed

Performed tests

A - Relax_disp systemtest

Relax_disp_systemtest

#!/bin/tcsh
set LOG=single.log ;
relax_single --time -s Relax_disp -t $LOG ;
set RUNTIME=`cat $LOG | awk '$1 ~ /^\./{print $0}' | awk '{ sum+=$2} END {print sum}'` ;
echo $RUNTIME >> $LOG ;
echo $RUNTIME ;

set LOG=multi.log ;
relax_multi --time -s Relax_disp -t $LOG ;
set RUNTIME=`cat $LOG | awk '$1 ~ /^\./{print $0}' | awk '{ sum+=$2} END {print sum}'` ;
echo $RUNTIME >> $LOG ;
echo $RUNTIME

B - Relax full analysis performed on dataset

First initialize data

set CPU1=tomat ;
set CPU2=haddock ;
set MODE1=single ;
set MODE2=multi ;
set DATA=$HOME/software/NMR-relax/relax_disp/test_suite/shared_data/dispersion/KTeilum_FMPoulsen_MAkke_2006/acbp_cpmg_disp_048MGuHCl_40C_041223/ ;
set TDATA=$HOME/relax_results
mkdir -p $TDATA/$CPU1 $TDATA/$CPU2 ;

cp -r $DATA $TDATA/$CPU1/$MODE1 ;
cp -r $DATA $TDATA/$CPU1/$MODE2 ;
cp -r $DATA $TDATA/$CPU2/$MODE1 ;
cp -r $DATA $TDATA/$CPU2/$MODE2 ;

relax_single $TDATA/$CPU1/$MODE1/relax_1_ini.py ;
relax_single $TDATA/$CPU1/$MODE2/relax_1_ini.py ;
relax_single $TDATA/$CPU2/$MODE1/relax_1_ini.py ;
relax_single $TDATA/$CPU2/$MODE2/relax_1_ini.py ;

Relax_full_analysis_performed_on_dataset

#!/bin/tcsh -e
set CPU=$HOST ;
set MODE1=single ;
set MODE2=multi ;
set TDATA=$HOME/relax_results

set LOG=timing.log ;
set TLOG=log.tmp ;
cd $TDATA

set MODE=$MODE1 ;
set RUNPROG="relax_${MODE} $TDATA/$CPU/$MODE/relax_4_model_sel.py -t ${CPU}_${MODE}.log" ;
echo "---\n$RUNPROG" >> $LOG ;
/usr/bin/time -o $TLOG $RUNPROG ;
cat $TLOG >> $LOG ; 
cat $LOG ;

set MODE=$MODE2 ;
set RUNPROG="relax_${MODE} $TDATA/$CPU/$MODE/relax_4_model_sel.py -t ${CPU}_${MODE}.log" ;
echo "---\n$RUNPROG" >> $LOG ;
/usr/bin/time -o $TLOG $RUNPROG ;
cat $TLOG >> $LOG ; 
cat $LOG ;

C - Relax full analysis performed on dataset with clustering

Relax_full_analysis_performed_on_dataset_cluster

#!/bin/tcsh -e
set CPU=$HOST ;
set MODE1=single ;
set MODE2=multi ;
set TDATA=$HOME/relax_results

set LOG=timing.log ;
set TLOG=log.tmp ;
cd $TDATA

set MODE=$MODE1 ;
set RUNPROG="relax_${MODE} $TDATA/$CPU/$MODE/relax_5_cluster.py -t ${CPU}_${MODE}_cluster.log" ;
echo "---\n$RUNPROG" >> $LOG ;
/usr/bin/time -o $TLOG $RUNPROG ;
cat $TLOG >> $LOG ; 
cat $LOG ;

set MODE=$MODE2 ;
set RUNPROG="relax_${MODE} $TDATA/$CPU/$MODE/relax_5_cluster.py -t ${CPU}_${MODE}_cluster.log" ;
echo "---\n$RUNPROG" >> $LOG ;
/usr/bin/time -o $TLOG $RUNPROG ;
cat $TLOG >> $LOG ; 
cat $LOG ;

Setup of test

List of computers - the 'lscpu' command

CPU 1

Architecture:          x86_64
CPU op-mode(s):        32-bit, 64-bit
Byte Order:            Little Endian
CPU(s):                2
On-line CPU(s) list:   0,1
Thread(s) per core:    1
Core(s) per socket:    2
Socket(s):             1
NUMA node(s):          1
Vendor ID:             GenuineIntel
CPU family:            6
Model:                 23
Stepping:              6
CPU MHz:               2659.893
BogoMIPS:              5319.78
L1d cache:             32K
L1i cache:             32K
L2 cache:              3072K
NUMA node0 CPU(s):     0,1

CPU 2

Architecture:          x86_64
CPU op-mode(s):        32-bit, 64-bit
Byte Order:            Little Endian
CPU(s):                24
On-line CPU(s) list:   0-23
Thread(s) per core:    2
Core(s) per socket:    6
Socket(s):             2
NUMA node(s):          2
Vendor ID:             GenuineIntel
CPU family:            6
Model:                 44
Stepping:              2
CPU MHz:               2394.136
BogoMIPS:              4787.82
Virtualization:        VT-x
L1d cache:             32K
L1i cache:             32K
L2 cache:              256K
L3 cache:              12288K
NUMA node0 CPU(s):     0,2,4,6,8,10,12,14,16,18,20,22
NUMA node1 CPU(s):     1,3,5,7,9,11,13,15,17,19,21,23

Execution scripts

relax_single

#!/bin/tcsh -fe
# Set the relax version used for this script.
set RELAX=/sbinlab2/tlinnet/software/NMR-relax/relax_disp/relax
# Remove env set to wrong library files.
unsetenv LD_LIBRARY_PATH

# Run relax in multi processor mode.
$RELAX $argv

relax_multi

#!/bin/tcsh -fe
# Set the relax version used for this script.
set RELAX=/sbinlab2/tlinnet/software/NMR-relax/relax_disp/relax
# Remove env set to wrong library files.
unsetenv LD_LIBRARY_PATH

# Set number of available CPUs.
set NPROC=`nproc`
set NP=`echo $NPROC + 1 | bc `

# Run relax in multi processor mode.
/usr/lib64/openmpi/bin/mpirun -np $NP $RELAX --multi='mpi4py' $argv

Installation

Installation of openmpi and the mpi4py python package according to wiki notes.

Results

Computer	Nr of CPU's.	Test type	Nr of spins	Nr exp.	GRID_INC	MC_NUM	MODELS	Time (s)
CPU 1	1	A	-	-	-	-	-	95, 105
CPU 1	2	A	-	-	-	-	-	96, 120
CPU 2	1	A	-	-	-	-	-	85, 78
CPU 2	24	A	-	-	-	-	-	133, 143
CPU 1	1	B	82	16	11	50	MODEL_ALL, single res	9:16:33
CPU 1	2	B	82	16	11	50	MODEL_ALL, single res	8:06:44
CPU 2	1	B	82	16	11	50	MODEL_ALL, single res	8:18:21
CPU 2	24	B	82	16	11	50	MODEL_ALL, single res	2:17:02
CPU 1	1	C	78	16	11	50	'R2eff', 'No Rex', 'TSMFK01', clustering	71:32:18
CPU 1	2	C	78	16	11	50	'R2eff', 'No Rex', 'TSMFK01', clustering	82:27:13
CPU 2	1	C	78	16	11	50	'R2eff', 'No Rex', 'TSMFK01', clustering	58:45:47
CPU 2	24	C	78	16	11	50	'R2eff', 'No Rex', 'TSMFK01', clustering	145:01:33

Notes:

1. Nr exp. = Nr of experiments = Nr of CPMG frequencies subtracted repetitions and reference spectrums.
2. MODEL_ALL = ['R2eff', 'No Rex', 'TSMFK01', 'LM63', 'LM63 3-site', 'CR72', 'CR72 full', 'IT99', 'NS CPMG 2-site 3D', 'NS CPMG 2-site expanded', 'NS CPMG 2-site star']