With the release of Intel Parallel Studio XE 2017, the focus is on making applications perform better on Intel architecture-based clusters. Intel MPI Library 2017, a fully integrated component of Intel Parallel Studio XE 2017, implements the high-performance MPI-3.1 specification on multiple fabrics. It enables programmers to quickly deliver the best parallel performance, even if you change or upgrade to new interconnects, without requiring changes to the software or operating environment.
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.”
“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.”
In this Intel Chip Chat podcast, Dr. Julie Krugler Hollek, co-organizer of PyLadies San Francisco and Data Scientist at Twitter, joins Allyson Klein to discuss efforts to democratize participation in open source communities and the future of data science. “PyLadies helps people who identify as women become participants in open source Python projects like The SciPy Stack, a specification that provides access to machine learning and data visualization tools.”
“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.”
Today NERSC announced plans to host a new, data-centric event called Data Day. The main event will take place on August 22, followed by a half-day hackathon on August 23. The goal: to bring together researchers who use, or are interested in using, NERSC systems for data-intensive work.
“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.”
In this video from PYCON 2016 in Portland, Lorena Barba from George Washinton University presents: Beyond Learning to Program, Education, Open Source Culture, Structured Collaboration, and Language. “PyCon is the largest annual gathering for the community using and developing the open-source Python programming language.”
“In GPAW, the high level nature of Python allows developers to design the algorithms, while C can be implemented for numeric intensive portions of the application through the use of highly optimized math kernels. In this application, the Python portions of the code are serial, which makes offloading to the Intel Xeon Phi coprocessor not feasible. However, and interface has been developed, pyMIC, which allows the application to launch kernels and control data transfers to the coprocessor.”
OCF in the U.K. recently deployed a new Fujitsu HPC cluster at the University of East Anglia. As the University’s second new HPC system in 4-years, the cluster can be easily scaled and expanded in the coming months through a framework agreement to match rapidly increasing demand for compute power.