It was with one goal – accelerating Python execution performance – that lead to the creation of Intel Distribution for Python, a set of tools designed to provide Python application performance right out of the box, usually with no code changes required. This sponsored post from Intel highlights how Intel SDK can enhance Python development and execution, as Python continues to grow in popularity.
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Accelerated Python for Data Science
The Intel Distribution for Python takes advantage of the Intel® Advanced Vector Extensions (Intel® AVX) and multiple cores in the latest Intel architectures. By utilizing the highly optimized Intel MKL BLAS and LAPACK routines, key functions run up to 200 times faster on servers and 10 times faster on desktop systems. This means that existing Python applications will perform significantly better merely by switching to the Intel distribution.
Intel Performance Libraries Accelerate Python Performance for HPC and Data Science
Python is now the most popular programming language, according to IEEE Spectrum’s fifth annual interactive ranking of programming languages, ahead of C++ and C. Recent Intel Distributions for Python show that real HPC performance can be achieved with compilers and library packages optimized for the latest Intel architectures. Moreover, the library packages targeted for big data analysis and numerical computation included in this distribution now support scaling for multi-core and many-core processors as well as distributed cluster and cloud infrastructures.
Machine Learning with Python: Distributed Training and Data Resources on Blue Waters
Aaron Saxton from NCSA gave this talk at the Blue Waters Symposium. “Blue Waters currently supports TensorFlow 1.3, PyTorch 0.3.0 and we hope to support CNTK and Horovod in the near future. This tutorial will go over the minimum ingredients needed to do distributed training on Blue Waters with these packages. What’s more, we also maintain an ImageNet data set to help researchers get started training CNN models. I will review the process by which a user can get access to this data set.”
Python Can Do It
“Python remains a single threaded environment with the global interpreter lock as the main bottleneck. Threads must wait for other threads to complete before starting to do their assigned work. The result of this model is that production code is produced that is too slow to be useful for large simulations.”
Performance Insights Using the Intel Advisor Python API
Tuning a complex application for today’s heterogeneous platforms requires an understanding of the application itself as well as familiarity with tools that are available for assisting with analyzing where in the code itself to look for bottlenecks. The process for optimizing the performance of an application, in general, requires the following steps that are most likely applicable for a wide range of applications.
Video: Scientific Visualization with Python
In this video, Dr. Marcelo Ponce from SciNet presents: Scientific Visualization with Python. “SciNet is Canada’s largest supercomputer centre, providing Canadian researchers with computational resources and expertise necessary to perform their research on scales not previously possible in Canada. We help power work from the biomedical sciences and aerospace engineering to astrophysics and climate science.”
Intel Releases Optimized Python for HPC
“By implementing popular Python packages such as NumPy, SciPy, scikit-learn, to call the Intel Math Kernel Library (Intel MKL) and the Intel Data Analytics Acceleration Library (Intel DAAL), Python applications are automatically optimized to take advantage of the latest architectures. These libraries have also been optimized for multithreading through calls to the Intel Threading Building Blocks (Intel TBB) library. This means that existing Python applications will perform significantly better merely by switching to the Intel distribution.”
IA Optimized Python Rocks in Production
“Intel recently announced the first product release of its High Performance Python distribution powered by Anaconda. The product provides a prebuilt easy-to-install Intel Architecture (IA) optimized Python for numerical and scientific computing, data analytics, HPC and more. It’s a free, drop in replacement for existing Python distributions that requires no changes to Python code. Yet benchmarks show big Intel Xeon processor performance improvements and even bigger Intel Xeon Phi processor performance improvements.”
Python and HPC
“In the HPC domain, Python can be used to develop a wide range of applications. While tight loops may still need to be coded in C or FORTRAN, Python can still be used. As more systems become available with coprocessors or accelerators, Python can be used to offload the main CPU and take advantage of the coprocessor. pyMIC is a Python Offload Module for the Intel Xeon Phi Coprocessor and is available at popular open source code repositories.”