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Job of the Week: Research Scientist at Intel

Intel is seeking a Research Scientist for their Extreme Scale Computing team in Oregon in our Job of the Week. “In this position, you will be responsible for conceiving, researching, and prototyping new parallel and distributed computing algorithms and frameworks, with special emphasis on machine learning and graph analytics, and you will develop relevant software and hardware technologies with the goal of converting these into products and fueling Intel’s growth. You will use your strong knowledge of scientific principles to identify and research promising technologies typically 3-5 years prior to product development. This requires a good understanding of state-of-the-art techniques in parallel computing, machine learning, distributed computing, optimization of algorithms, and bench-marking, along with a strong understanding of computer architecture.”

Video: Accelerated Any-Scale Ai Solutions from DDN

In this video from the DDN User Group at ISC 2018, James Coomer from DDN presents: A3I – Accelerated Any-Scale Solutions from DDN. “Engineered from the ground up for the AI-enabled data center, DDN’s A3I solutions are fully optimized to handle the spectrum of AI and DL activities concurrently: data ingest and preparation, training, validation, and inference. The DDN A3I platform is easy to deploy and manage, highly scalable in both performance and capacity, and represents a highly efficient and resilient solution for all of your current and future AI requirements.”

Podcast: From Here to Ai with Jack Wells from Oak Ridge

In this Conversations with Dez podcast, Dez Blanchfield sits down with Jack Wells from ORNL to talk about about his personal & professional life journey, his role at Oak Ridge National Lab, how Artificial Intelligence is being deployed & leveraged in HPC, and the role IBM’s POWER9 solution is playing in supporting Oak Ridge and its mission. Jack Wells is the Director of Science for the Oak Ridge Leadership Computing Facility.

SC18 Preview: Steve Furber on Brain-Inspired Massively-Parallel Computing

SC18 continues its series of Invited Talk previews with this quick look at “Brain-Inspired Massively-Parallel Computing” by Stephen Furber. “The SpiNNaker (Spiking Neural Network Architecture) platform is an example of a highly flexible digital neuromorphic platform, based upon a massively-parallel configuration of small processors with a bespoke interconnect fabric designed to support the very high connectivity of biological neural nets in real-time models. Although designed primarily to support brain science, it can also be used to explore more applications-oriented research.”

Intel to Host IXPUG eXtreme Performance User Group Sept. 25-28 in Oregon

Intel will host the Intel eXtreme Performance User Group (IXPUG) event Sept. 25-28 in Hillsboro, Oregon. “This IXPUG conference is focused on all aspects of employing and adopting many-core processing technologies and techniques for optimal application execution, including topics that cover system hardware beyond the processor (memory, interconnect, etc.), software tools, programming models, new workloads (visualization, data analytics, machine learning, etc.) and more. The conference will provide an interactive experience focused on key topics associated with high-performance computing, artificial intelligence, data analytics, cloud computing, and more.”

Lenovo Takes the TOP500 with Liquid Cooling at ISC 2018

In this video from ISC 2018, Rick Koopman from Lenovo describes the company’s latest HPC innovations including their new Neptune liquid cooling technology. Lenovo is the world’s largest TOP500 supercomputing provider measured by the number of systems ranked on the TOP500 list. 117 of the 500 most powerful supercomputers included in the TOP500 are Lenovo installations, meaning nearly one out of every four systems (23.4 percent) on the prestigious list is a Lenovo solution.

PASC18 Panel Discussion Big Data vs. Fast Computation

“The panelists will discuss the critical challenges facing key HPC application areas in the next 5-10 years, based on a mix of knowledge and speculation. They will explore whether we need to make radical changes to our practices, methods, tools, and techniques to be able to use modern resources and make faster and bigger progress on our scientific problems.”

There’s still Time to register for HPC User Forum in Detroit – Agenda targets AI, Autonomous Cars, & Sensor Networks

There’s still Time to register for HPC User Forum in Detroit. The meeting takes place September 4-6 in Dearborn, Michigan. Key topics include AI and other advanced analytics, automated driving systems a.k.a. self-driving vehicles, additive manufacturing and HPC in clouds (and outer space!). “If AI is in your crosshairs, you won’t want to miss the next HPC User Forum in Dearborn, Michigan. Aside from tackling leadership computing initiatives in the U.S. and around the world, the meeting will zero in on artificial intelligence use cases on prem and in the cloud, especially self-driving vehicle development and urban sensor networks.”

The Rigetti 128-qubit chip — What it means for quantum

Chad Rigetti writes that his company is building a 128-qubit quantum computer over the next 12 months. The company has already built the 128-qubit processing chip. If successful, it could be the world’s most powerful quantum computer and it could have the chance to outpace traditional supercomputers. “To drive practical use of quantum computing today, we must be able to scale and improve the performance of the chips and connect them to the electronics on which they run — which has proven to be one of the most challenging aspects of quantum computing.”

Dr. Eng Lim Goh presents: Prediction – Use Science or History?

Dr. Eng Lim Goh from HPE gave this keynote talk at PASC18. “Traditionally, scientific laws have been applied deductively – from predicting the performance of a pacemaker before implant, downforce of a Formula 1 car, pricing of derivatives in finance or the motion of planets for a trip to Mars. With Artificial Intelligence, we are starting to also use the data-intensive inductive approach, enabled by the re-emergence of Machine Learning which has been fueled by decades of accumulated data.”