A single issue has always defined the history of HPC systems: performance. While offloading and co-design may seem like new approaches to computing, they actually have been used, to a lesser degree, in the past as a way to enhance performance. Current co-design methods are now going deeper into cluster components than was previously possible. These new capabilities extend from the local cluster nodes into the “computing network.”
In this Intel Chip Chat Podcast, Nidhi Chappell, the Director of Machine Learning Strategy at Intel discusses the company’s planned acquisition of Nervana Systems to further drive Intel’s capabilities in the artificial intelligence (AI) field. “We will apply Nervana’s software expertise to further optimize the Intel Math Kernel Library and its integration into industry standard frameworks. Nervana’s Engine and silicon expertise will advance Intel’s AI portfolio and enhance the deep learning performance and TCO of our Intel Xeon and Intel Xeon Phi processors.”
Researchers at the University of Oxford have achieved a quantum logic gate with record-breaking 99.9% precision, reaching the benchmark required theoretically to build a quantum computer. “An analogy from conventional computing hardware would be that we have finally worked out how to build a transistor with good enough performance to make logic circuits, but the technology for wiring thousands of those transistors together to build an electronic computer is still in its infancy.”
“A quantum computer cannot just be created from just trapping ions, it is necessary to move the information (the ions) between different locations in a trap, for example between calculation and storage regions. Our group has developed a method which allows the means to confidently control the motion of individual ions and shuttle an ion to any position in a ion trap microchip. By developing traps that generate complex electrical fields, it is possible to push and pull the ions by varying the strength of these fields, making it possible to manipulate single ions around corners! Right now, we are in the process of developing full scale architectures that contain all the necessary features for a full scale quantum computer.”
“We have been working on developing a number of tools that enable users to quantify power and performance in both software and hardware, and then design a more efficient system. We can also utilize the tools to predict the performance of a piece of software on a system that may not be available or does not yet exist – the aim is to take the guesswork away from novel system design.”
IBM scientists have created randomly spiking neurons using phase-change materials to store and process data. This demonstration marks a significant step forward in the development of energy-efficient, ultra-dense integrated neuromorphic technologies for applications in cognitive computing.
Thomas Lippert presented this talk at The Digital Future conference. “The Human Brain Project brings together neuroscientists, physicians, computer scientists, physicists, mathematicians and computer specialists from internationally respected scientific institutions in 23 countries. Their goal is to simulate the complete human brain within the next ten years using a supercomputer of the future. The simulation will be accurate in every detail, and will take in aspects such as genetics, the molecular level and the interaction of whole cell clusters.”
Today SC16 announced that Katharine Frase has been selected as the SC16 Keynote Speaker. “We are thrilled to have such an experienced pioneer and leader address pressing issues across so many industry fronts,” says John West, SC16 General Chair from the Texas Advanced Computing Center. “Her discussion will be thought-provoking to everyone in the room – from industry veterans to those new to the field.”
The inaugural Misha Mahowald Prize for Neuromorphic Engineering has been awarded to the TrueNorth project, led by Dr. Dharmendra S. Modha at IBM Research. “The Misha Mahowald Prize recognizes outstanding achievement in the field of neuromorphic engineering. Neuromorphic engineering is defined as the construction of artificial computing systems which implement key computational principles found in natural nervous systems. Understanding how to build such systems may enable a new generation of intelligent devices, able to interact in real-time in uncertain real-world conditions under severe power constraints, as biological brains do.”
Do you have new technology that could disrupt HPC in the near future? There’s still time to get free exhibit space at SC16 in November. “At the SC16 Emerging Technologies Showcase, we invite submissions from industry, academia, and government researchers.