Programming/Python
Contents
Programming Details
Python is a widely used high-level programming language used for general-purpose programming.
An interpreted language, Python has a design philosophy which emphasizes code readability (notably using whitespace indentation to delimit code blocks rather than curly braces or keywords), and a syntax which allows programmers to express concepts in fewer lines of code than possible in languages such as C++ or JAVA
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When programming with Python in a HPC environment you will need to change the first line as shown below |
#!/usr/bin/python
To
#!/usr/bin/env python
Python example
#!/usr/bin/env python
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
if rank == 0:
data = {'key1' : [7, 2.72, 2+3j],
'key2' : ( 'abc', 'xyz')}
else:
data = None
data = comm.bcast(data, root=0)
if rank != 0:
print ("data is %s and %d" % (data,rank))
else:
print ("I am master\n")
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Due to the limited of the Python interpreter (GIL) there is no point to use threads for CPU intensive tasks in Python. You need either multiprocessing or use C extensions that release GIL during computations e.g., some of numpy functions, example. |
Modules Available
The following modules are available:
Python provided by the Python Software foundation
- module add python/2.7.11
- module add python/3.5.1
Anaconda python Anaconda is the open data science platform powered by Continuum
- module add python/anaconda/4.0/2.7
- module add python/anaconda/4.0/3.5
- module add python/anaconda/4.1.1/2.7
Compilation
Python is byte compiled at runtime by typing for example
[username@login01 ~]$ python myPython.py
Usage Examples
Batch example
#!/bin/bash #SBATCH -J compute-single-node #SBATCH -N 1 #SBATCH --ntasks-per-node 20 #SBATCH -o %N.%j.%a.out #SBATCH -e %N.%j.%a.err #SBATCH -p compute #SBATCH --exclusive echo $SLURM_JOB_NODELIST module purge module add anaconda/4.0 module add openmpi/gcc/1.10.2 export I_MPI_DEBUG=5 export I_MPI_FABRICS=shm:tmi export I_MPI_FALLBACK=no mpirun python broadcast.py
[username@login01 ~]$ sbatch python-demo.job Submitted batch job 289572
Python and OpenMP
The Python interpreter uses a GIL (Global Interpreter Lock) which makes multi-threading almost impossible. Although there are ways around this and one of the most common methods is to use Cython.
Cython supports native parallelism through the cython.parallel module. To use this kind of parallelism, the GIL must be released (see Releasing the GIL). It currently supports OpenMP
- Example with a reduction (on sum):
#!/usr/bin/env python
from cython.parallel import prange
cdef int i
cdef int n = 30
cdef int sum = 0
for i in prange(n, nogil=True):
sum += i
print(sum)
- Example with a typed memoryview (e.g. a NumPy array):
#!/usr/bin/env python
from cython.parallel import prange
def func(double[:] x, double alpha):
cdef Py_ssize_t i
for i in prange(x.shape[0]):
x[i] = alpha * x[i]
To actually use the OpenMP support, you need to tell the C or C++ compiler to enable OpenMP. For gcc this can be done as follows in a setup.py:
#!/usr/bin/env python
from distutils.core import setup
from distutils.extension import Extension
from Cython.Build import cythonize
ext_modules = [
Extension(
"hello",
["hello.pyx"],
extra_compile_args=['-fopenmp'],
extra_link_args=['-fopenmp'],
)
]
setup(
name='hello-parallel-world',
ext_modules=cythonize(ext_modules),
)
- See Cython Parallelism documentation for more details here.
Further Information
- https://en.wikipedia.org/wiki/Python_(programming_language)
- https://www.python.org/
- https://www.continuum.io/anaconda-overview
- OpenMPI
- OpenMP
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