Though AI has been widely demystified, its full materialization in real field implementation entails diversified challenges. The article by Dr Cédric Bourrasset (Atos) gives perspective on the hiccups and recommendations on the way to realize “Scientific Machine Learning and HPC-AI technology convergence”
Search Results for: machine learning
New Cornell Virtual HPC, Data Science, Machine Learning Workshops at XSEDE
March 1, 2021 by
ITHACA, NY – Cornell University announced today that four new Cornell Virtual Workshop training topics are available at the Extreme Science and Engineering Discovery Environment (XSEDE) User Portal: Getting Started on Frontera Introduction to Advanced Cluster Architectures Using the Jetstream APIs Python for Data Science: Part 2 – Data Modeling and Machine Learning Cornell Virtual Workshop topics are freely available at all times to the entire scientific community – […]
CrownBio and JSR Life Sciences Partner with Cambridge Quantum Computing to Leverage Quantum Machine Learning for Novel Cancer Treatment Biomarker Discovery
February 24, 2021 by
SUNNYVALE, CA and CAMBRIDGE, UK, February 24, 2021 – Crown Bioscience (CrownBio), JSR Life Sciences and Cambridge Quantum Computing (CQC) today announced a partnership agreement to explore the application of quantum technology to drive the identification of multi-gene biomarker discovery for oncology drug discovery. The partnership will combine CrownBio’s domain expertise and vast data sets generated […]
NAG Delivers Machine Learning Guidance via New Azure HPC + AI Collaboration Centre
February 23, 2021 by
23 February 2021 – Oxford, UK: The Numerical Algorithms Group (NAG) will provide machine learning guidance to Microsoft Azure users via the new Azure HPC & AI Collaboration Centre. In partnership with NVIDIA, this new centre will develop best practices for the deployment of scalable machine learning in the Cloud. “Microsoft and NVIDIA innovation around […]
GIGABYTE Joins MLCommons to Accelerate the Machine Learning Community
December 22, 2020 by
Taipei, Taiwan, December 22nd 2020 – GIGABYTE Technology, (TWSE: 2376), a maker of high-performance servers and workstations, today announced GIGABYTE as one of the founding members of MLCommons, an open engineering consortium with the goal of accelerating machine learning with benchmarking, large-scale open data sets, and best practices that are community-driven. In 2018, a group […]
Creators of MLPerf Launch MLCommons Consortium for Machine Learning Benchmarks, Metrics, Datasets, Models and Best Practices
December 3, 2020 by
SAN FRANCISCO – Dec. 3, 2020 – Today, open engineering consortium MLCommons launched an industry-academic partnership to accelerate machine learning innovation and broaden access to this critical technology for the public good. MLCommons will focus on: Benchmarks and Metrics – that deliver transparency and a level playing field for comparing ML systems, software, and solutions, e.g., MLPerf, […]
At Virtual SC20: An Update on the Fraunhofer Institute’s Carme, Where HPC Meets Interactive Machine Learning
November 16, 2020 by
At Virtual SC20, we spent time with Philipp Reusch, Scientific Assistant at Fraunhofer ITWM in Kaiserslautern, Germany. Reusch is closely involved in the development for the institute’s Carme (“kar-mee”), a framework to manage resources for multiple users running interactive AI jobs on a cluster of (GPU) compute nodes. Carme, by the way, is the name […]
Intel, NSF Name Winners of Wireless Machine Learning Research Funding
June 25, 2020 by
Intel and the National Science Foundation (NSF), joint funders of the Machine Learning for Wireless Networking Systems (MLWiNS) program, today announced recipients of awards for research projects into ultra-dense wireless systems that deliver the throughput, latency and reliability requirements of future applications – including distributed machine learning computations over wireless edge networks. Here are the […]
AMD Wins Slot in Latest NVIDIA A100 Machine Learning System
May 15, 2020 by
Today AMD demonstrated continued momentum in HPC with NVIDIA’s announcement that 2nd Generation AMD EPYC 7742 processors will power their new DGX A100 dedicated AI and Machine Learning system. AMD has an impressive set of HPC wins in the past year, and has been chosen by the DOE to power two pending exascale-class supercomputers, Frontier and El Capitan. “2nd Gen AMD EPYC processors are the first and only current x86-architecture server processor supporting PCIe 4.0, providing up to 128 lanes of I/O, per processor for high performance computing and connections to other devices like GPUs.”
Video: Machine Learning for Weather Forecasts
May 10, 2020 by
Peter Dueben from ECMWF gave this talk at the Stanford HPC Conference. “I will present recent studies that use deep learning to learn the equations of motion of the atmosphere, to emulate model components of weather forecast models and to enhance usability of weather forecasts. I will than talk about the main challenges for the application of deep learning in cutting-edge weather forecasts and suggest approaches to improve usability in the future.”
