Introduction
Intel® MPI is an optimized library for standard MPI applications, designed to deliver high-speed and scalable cluster messaging on Intel® based architecture. For Python users, the Intel® MPI runtime is included in Intel® Distribution for Python (IDP) and can be used in Python through mpi4py* - a Python binding package for MPI.
This article will discuss the key benefits of using Intel® MPI over standard MPI libraries, Intel® MPI’s simplicity in integrating with Python, and illustrate a Python usage example using Intel® MPI libraries.
Highlights of Intel® MPI
Optimized MPI application performance Application-specific tuning
- Automatic tuning
- Support for Intel® Xeon Phi™ Processor
- Support for Intel® Omni-Path Architecture Fabric
Lower latency and multi-vendor interoperability
- Industry leading latency
- Performance optimized support for the fabric capabilities through OpenFabrics*(OFI)
Faster MPI communication
- Optimized collectives
Sustainable scalability up to 340K cores
- Native InfiniBand* interface support allows for lower latencies, higher bandwidth, and reduced memory requirements
More robust MPI applications
- Seamless interoperability with Intel® Trace Analyzer and Collector
Intel® MPI with Python*(3.6)
In the Intel® Distribution for Python, mpi4py offers Python binding packages for MPI standards and wraps around Intel® MPI, thus enabling an optimized message passing interface and requiring no execution code changes in command line terminal.
Section below implements a simple master-slave python routine (mpi-sample.py) that sends partial arrays from all slave nodes to the master node
As mentioned, Intel® MPI is offered in Intel® Distribution for Python which should be installed prior to using Intel® MPI.
Intel® Distribution for Python is available for free for Windows*, Linux* and macOS*. The recommended install method is through Anaconda’s distribution cloud.
1. Install Intel® Distribution for Python with Anaconda
conda create -n IDP intel -c python=3.6
2. Activate Intel® Distribution for Python on Linux
source activate IDP
3. Activate Intel® Distribution for Python on Windows
activate IDP
4. Install Intel® MPI
conda install mpi4py –c intel # installs Python binding packages that wraps Intel® MPI
5. Create MPI program (Below is an example of a basic Master-Slave python routine – mpi-sample.py)
from mpi4py import MPI
import numpy as np
import sys
# MPI Initialization
size = MPI.COMM_WORLD.Get_size ()
rank = MPI.COMM_WORLD.Get_rank ()
name = MPI.Get_processor_name ()
numRows = int (sys.argv[1])
numCols = int (sys.argv[2])
seeded = np.random.RandomState (42)
partialArray = seeded.rand (numRows, numCols)
if rank == 0:
fullArray = [0] * size
print ("Master begins receiving partial arrays....")
if size > 1:
for i in range (1, size):
rank, size, name, fullArray[rank] = MPI.COMM_WORLD.recv (source=MPI.ANY_SOURCE, tag=1)
print ("Master completes receiving all partial arrays")
else:
MPI.COMM_WORLD.send ((rank, size, name, partialArray), dest=0, tag=1)
6. Run on terminal
Linux terminal
mpirun –n 4 python mpi-sample.py 100000 10
Windows terminal
mpiexec -n 4 python mpi-sample.py 100000 10