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Accelerate Big Data and HPC applications with FPGAs using JupyterHub

Today InAccel annnounced that it has integrated JupyterHub into the company’s adaptive acceleration platform for FPGAs. InAccel provides an FPGA resource manager that allows the instant deployment, scaling and virtualization of FPGAs making easier than ever the utilization of FPGA clusters for applications like machine learning, data processing, data analytics and many more HPC workloads.

Supercomputing and the Scientist: How HPC and Analytics are transforming experimental science

In this video from DataTech19, Debbie Bard from NERSC presents: Supercomputing and the scientist: How HPC and large-scale data analytics are transforming experimental science. “Debbie Bard leads the Data Science Engagement Group NERSC. NERSC is the mission supercomputing center for the USA Department of Energy, and supports over 7000 scientists and 700 projects with supercomputing needs.”

Wolfram Research Releases Mathematica Version 12 for Advanced Data Science

Today Wolfram Research released Version 12 of Mathematica for advanced data science and computational discovery. “After three decades of continuous R&D and the introduction of Mathematica Version 1.0, Wolfram Research has released its most powerful software offering with Version 12 of Wolfram Language, the symbolic backbone of Mathematica. The latest version includes over a thousand new functions and features for multiparadigm data science, automated machine learning, and blockchain manipulation for modern software development and technical computing.”

Intel High-Performance Python Extends to Machine Learning and Data Analytics

One of the big surprises of the past few years has been the spectacular rise in the use of Python* in high-performance computing applications. With the latest releases of Intel® Distribution for Python, included in Intel® Parallel Studio XE 2019, the numerical and scientific computing capabilities of high-performance Python now extends to machine learning and data analytics.

Rapids: Data Science on GPUs

Christoph Angerer from NVIDIA gave this talk at FOSDEM’19. “The next big step in data science will combine the ease of use of common Python APIs, but with the power and scalability of GPU compute. The RAPIDS project is the first step in giving data scientists the ability to use familiar APIs and abstractions while taking advantage of the same technology that enables dramatic increases in speed in deep learning. This session highlights the progress that has been made on RAPIDS, discusses how you can get up and running doing data science on the GPU, and provides some use cases involving graph analytics as motivation.”

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.

IBM’s Plan to bring Machine Learning Capabilities to Data Scientists Everywhere

Over at the IBM Blog, IBM Fellow Hillary Hunter writes that the company anticipates that the world’s volume of digital data will exceed 44 zettabytes, an astounding number. “IBM has worked to build the industry’s most complete data science platform. Integrated with NVIDIA GPUs and software designed specifically for AI and the most data-intensive workloads, IBM has infused AI into offerings that clients can access regardless of their deployment model. Today, we take the next step in that journey in announcing the next evolution of our collaboration with NVIDIA. We plan to leverage their new data science toolkit, RAPIDS, across our portfolio so that our clients can enhance the performance of machine learning and data analytics.”

Big 3 Cloud Providers join with NSF to Support Data Science

“NSF’s participation with major cloud providers is an innovative approach to combining resources to better support data science research,” said Jim Kurose, assistant director of NSF for Computer and Information Science and Engineering (CISE). “This type of collaboration enables fundamental research and spurs technology development and economic growth in areas of mutual interest to the participants, driving innovation for the long-term benefit of our nation.”

RCE-Podcast Looks at Project Jupyter for Interactive Data Science

In this RCE Podcast, Brock Palen and Jeff Squyres discuss Jupyter with Dr. Brian Granger from Cal Poly State University. “Jupyter is a non-profit, open-source project, born out of the IPython Project in 2014 as it evolved to support interactive data science and scientific computing across all programming languages.”

Video: Revolution in Computer and Data-enabled Science and Engineering

Ed Seidel from the University of Illinois gave this talk at the 2017 Argonne Training Program on Extreme-Scale Computing. The theme of his talk centers around the need for interdisciplinary research. “Interdisciplinary research (IDR) is a mode of research by teams or individuals that integrates information, data, techniques, tools, perspectives, concepts, and/or theories from two or more disciplines or bodies of specialized knowledge to advance fundamental understanding or to solve problems whose solutions are beyond the scope of a single discipline or area of research practice.”