Sign up for our newsletter and get the latest HPC news and analysis.
Send me information from insideHPC:


DDN Steps Up to HPC & AI Workloads at ISC 2018

In this video from ISC 2018, James Coomer from DDN describes the company’s latest high performance storage technologies for AI and HPC workloads. “Attendees at ISC 2018 learned how organizations around the world are leveraging DDN’s people, technology, performance and innovation to achieve their greatest visions and make revolutionary insights and discoveries! Designed, optimized and right-sized for Commercial HPC, Higher Education and Exascale Computing, our full range of  DDN products and solutions are changing the landscape of HPC and delivering the most value with the greatest operational efficiency.”

NVIDIA Offers Framework to Solve AI System Challenges

At the recent NVIDIA GPU Technology Conference (GTC) 2018, Jensen Huang, NVIDIA President and CEO, during his presentation focused on a new framework designed to contextualize the key challenges using AI systems and delivering deep learning-based solutions. A new white paper sponsored by NVIDIA outlines these requirements — coined PLASTER.

PLASTER: A Framework for Deep Learning Performance

Both hardware and software advances in deep learning (DL), a type of ML, appear to be catalysts for the early stages of a phenomenal AI growth trend. Download the new white paper from NVIDIA that addresses the challenges described in PLASTER, which is important in any  deep learning solution, and it is especially useful for developing and delivering the inference engines underpinning AI-based services. 

Deep Learning Open Source Framework Optimized on Apache Spark*

Intel recently released BigDL. It’s an open source, highly optimized, distributed, deep learning framework for Apache Spark*. It makes Hadoop/Spark into a unified platform for data storage, data processing and mining, feature engineering, traditional machine learning, and deep learning workloads, resulting in better economy of scale, higher resource utilization, ease of use/development, and better TCO.

NVIDIA Releases Code for Accelerated Machine Learning

Today NVIDIA made a number of announcements centered around Machine Learning software at the Computer Vision and Pattern Recognition Conference in Salt Lake City. “NVIDIA is kicking off the conference by demonstrating an early release of Apex, an open-source PyTorch extension that helps users maximize deep learning training performance on NVIDIA Volta GPUs. Inspired by state of the art mixed precision training in translational networks, sentiment analysis, and image classification, NVIDIA PyTorch developers have created tools bringing these methods to all levels of PyTorch users.”

Edge Computing Proves Critical for Drilling Rigs, Pipeline Integrity

This is the third entry in a five-part insideHPC series that takes an in-depth look at how machine learning, deep learning and AI are being used in the energy industry. Read on to learn how edge computing is playing a role in operating drilling rigs, ensuring pipeline integrity and more. 

Intel to Showcase AI and HPC Demos at ISC 2018

Today Intel released a sneak peek at their plans for ISC 2018 in Frankfurt. The company will showcase how it’s helping AI developers, data scientists and HPC programmers transform industries by tapping into HPC to power the AI solutions. “ISC brings together academic and commercial disciplines to share knowledge in the field of high performance computing. Intel’s presence at the event will include keynotes, sessions, and booth demos that will be focused on the future of HPC technology, including Artificial Intelligence (AI) and visualization.”

Energy Companies Embrace Deep Learning for Inspections, Exploration & More

This is the second entry in a five-part insideHPC series that takes an in-depth look at how machine learning, deep learning and AI are being used in the energy industry. Read on to learn how energy companies are embracing deep learning for inspections, exploration and more. 

Opportunities Abound: HPC and Machine Learning for Energy Exploration

The is the first entry in a five-part insideHPC series that takes an in-depth look at how machine learning, deep learning and AI are being used in the energy industry. Read on to find out how machine learning is driving energy exploration. “Any tool that reduces the time needed to understand where the deposits are located can save a company millions of dollars.”

Designing Scalable HPC, Deep Learning and Cloud Middleware for Exascale Systems

DK Panda from Ohio State University gave this talk at the Swiss HPC Conference. “This talk will focus on challenges in designing HPC, Deep Learning, and HPC Cloud middleware for Exascale systems with millions of processors and accelerators. For the HPC domain, we will discuss about the challenges in designing runtime environments for MPI+X (PGAS – OpenSHMEM/UPC/CAF/UPC++, OpenMP, and CUDA) programming models. For the Deep Learning domain, we will focus on popular Deep Learning frameworks (Caffe, CNTK, and TensorFlow) to extract performance and scalability with MVAPICH2-GDR MPI library.”