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.
Penguin Computing in Portland is seeking a Python Software Engineer in our Job of the Week.
Today the good folks at FlyElephant announced support for R, Python, and public API for the participants of its beta testing program.
“CDSW’s organizers are professional programmers and data scientists and several of us have experience teaching data science in more traditional university and corporate settings. Our talk will describe how “democratized” data science is similar to — and sometimes extremely different from — these more traditional approaches. We will talk about some of the challenges we have faced and highlight some of our most inspirational successes.”
“Despite the growing abundance of powerful tools, building and deploying machine-learning frameworks into production continues to be major challenge, in both science and industry. I’ll present some particular pain points and cautions for practitioners as well as recent work addressing some of the nagging issues. I advocate for a systems view, which, when expanded beyond the algorithms and codes to the organizational ecosystem, places some interesting constraints on the teams tasked with development and stewardship of ML products.”
“Software and computers are everywhere, revolutionizing every field around us. But the majority of schools don’t teach computer science. Code.org believes every student should have the opportunity to shape the 21st-century and wants to turn this problem around. This is just the beginning of a bold vision to bring this foundational field to every K-12 public school by 2020.”