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.”
Search Results for: python
BlueData Brings DevOps Agility to Data Science Operations with Spark, R, and Python
February 24, 2017 by
BlueData, provider of a leading Big-Data-as-a-Service (BDaaS) software platform, announced the new winter release for the BlueData EPIC software platform. This new release delivers several new enhancements for data science operations, bringing DevOps agility and collaboration to data science teams as well as support for new machine learning use cases.
Intel Releases Optimized Python for HPC
February 9, 2017 by
“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
January 13, 2017 by
“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
September 1, 2016 by
“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.”
Video: Speeding Up Code with the Intel Distribution for Python
June 11, 2016 by
David Bolton from Slashdot shows how ‘embarrassingly parallel’ code can be sped up over 2000x (not percent) by utilizing Intel tools including the Intel Python compiler and OpenMP. “The Intel Distribution for Python* 2017 Beta program is now available. The Beta product adds new Python packages like scikit-learn, mpi4py, numba, conda, tbb (Python interfaces to Intel Threading Building Blocks) and pyDAAL (Python interfaces to Intel Data Analytics Acceleration Library). “
Video: Computational Physics with Python
June 4, 2016 by
“Newton’s explanation of planetary orbits is one of the greatest achievements of science. We will follow Feynman’s approach to show how the motion of the planets around the sun can be calculated using computers and without using Newton’s advanced mathematics. This talk will convince you that doing physics with Python is way more fun than the way you did physics in high school or university.”
Intel and Continuum Analytics Work Together to Extend the Power of Python-based Analytics Across the Enterprise
June 1, 2016 by
Continuum Analytics, the creator and driving force behind Anaconda, a leading open data science platform powered by Python, welcomes Intel into the Anaconda ecosystem. Intel has adopted the Anaconda packaging and distribution and is working with Continuum to provide interoperability.
The Best Data Scientists Run R and Python
March 15, 2016 by
Ken Sanford, an Analytics Architect and Evangelist at H2O.ai shares his empirical evidence in support of the statement – The best data scientists use R and Python.
HPC Podcast Looks at Intel’s Pending Distribution of Python
February 18, 2016 by
In this HPC Podcast, Don Kinhorn and Chris Stevens from Puget Systems discuss the boom in FPGAs at SC15 as well as Intel’s announcement that the company is going to maintain a build of Python. “Python is a pretty important programming language. It has a large and growing number of useful libraries for mathematical/scientific computing and machine learning, NumPy, SciPy, pandas, Scikit-learn, PySpark, theano, and more.”
