The Jülich Supercomputing Centre has installed a couple of HPC systems to support neuroscience applications as part of special EU-funded procurement for the European Human Brain Project (HBP). The two machines, JURON, from IBM, and JULIA, from Cray, are pilot systems that will be used evaluate technologies and architecture for a much larger HBP supercomputer down the line.
At NVIDIA’s first European GPU Technology Conference (GTC Europe) taking place in Amsterdam this week, CEO Jen-Hsun Huang announced a number of new users of the DGX-1 GPU-powered “supercomputer in a box.” Huang also teased attendees with an early look at one of their next-generation Volta GPUs designed to power self-driving cars.
Quantum computing pioneer D-Wave Systems has announced some details of a new 2000-qubit system it has developed. The processor that drives the system will contain twice the number of qubits that powers the current D-Wave 2X system, which was announced last year. The new hardware also includes additional control features that enables users to tune the quantum computations to arrive at a faster result and to explore the solution space more fully.
Microsoft has revealed that Altera FPGAs have been installed across every Azure cloud server, creating what the company is calling “the world’s first AI supercomputer.” The deployment spans 15 countries and represents an aggregate performance of more than one exa-op. The announcement was made by Microsoft CEO Satya Nadella and engineer Doug Burger during the opening keynote at the Ignite Conference in Atlanta.
TrueNorth, IBM’s brain-like microprocessor, has been found to be exceptionally proficient at inference work for deep neural networks. In particular, the chip has demonstrated it’s especially good at image recognition, being able accurately classify such data much more efficiently, from an energy perspective, than traditional processor architectures, suggesting new applications in mobile computing, IoT, robotics, autonomous cars, and HPC.
BenevolentAI, a London-based artificial intelligence company specializing in health and bioscience applications, has acquired NVIDIA’s DGX-1, a deep learning system accelerated by eight Tesla P100 GPUs. The company plans to use the $129,000 machine to advance its work in drug discovery and related biomedical research.
Researchers at the University of Basel in Switzerland have used machine learning to predict the thermodynamic characteristics of 90 new mineral compounds with potential commercial use. The machine learning models were able to predict the chemical stability of all possible iterations of a particular type of class of crystals several orders of magnitude faster than if the researchers had relied on quantum mechanical calculations.
The path to exascale computing hasn’t been an easy one. It has had to face a daunting set of challenges in energy efficiency, application parallelism, and system reliability, just to name a few. The difficulties in bringing the hardware and software up to this level is considerable, but there is a more fundamental challenge at the heart of exascale: doing the necessary work of building an ecosystem that will last for a decade or more, not just for a handful stunt machines.