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Converging HPC, Big Data, and AI at the Tokyo Institute of Technology

Satoshi Matsuoka from the Tokyo Institute of Technology gave this talk at the NVIDIA booth at SC17. “TSUBAME3 embodies various BYTES-oriented features to allow for HPC to BD/AI convergence at scale, including significant scalable horizontal bandwidth as well as support for deep memory hierarchy and capacity, along with high flops in low precision arithmetic for deep learning.”

ASC18 Student Cluster Competition to include 230 Teams

The 2018 ASC Student Supercomputer Challenge (ASC18) will begin on January 16, 2018. First launched in 2012, the competition will feature a whopping 230 student teams looking to build the fastest possible cluster with a power cap of 3 Kilowatts. “The USA Student Cluster Competition is like a marathon, testing participants’ hard work and perseverance,” said OrionX partner Dan Olds, who has covered all three major supercomputing challenges. “Germany’s ISC is a sprint, testing innovation and adaptability. China’s ASC is a combination of both.”

Video: Dell EMC AI Vision & Strategy

Jay Boisseau from Dell EMC gave this talk at SC17 in Denver. “Across every industry, organizations are moving aggressively to adopt AI | ML | DL tools and frameworks to help them become more effective in leveraging data and analytics to power their key business and operational use cases. To help our clients exploit the business and operational benefits of AI | ML | DL, Dell EMC has created “Ready Bundles” that are designed to simplify the configuration, deployment and management of AI | ML | DL solutions.”

Job of the Week: Senior Memory Systems Architect at NVIDIA

NVIDIA in Silicon Valley is seeking a Senior Memory Systems Architect in our Job of the Week. “NVIDIA is building the world’s fastest highly-parallel processing systems, period. Our high-bandwidth multi-client memory subsystems are blazing new territory with every generation. As we increase levels of parallelism, bandwidth and capacity, we are presented with design challenges exacerbated by clients with varying but simultaneous needs such as real-time, low latency, and high-bandwidth. In addition, we are adding improved virtualization and programming model capabilities.”

Jack Dongarra Presents: Overview of HPC and Energy Savings on NVIDIA’s V100

Jack Dongarra from the University of Tennessee gave this talk at SC17. “In this talk we will look at the current state of high performance computing and look to the future toward exascale. In addition, we will examine some issues that can help in reducing the power consumption for linear algebra computations.”

Michael Wolfe Presents: Why Iteration Space Tiling?

In this Invited Talk from SC17, Michael Wolfe from NVIDIA presents: Why Iteration Space Tiling? The talk is based on his noted paper, which won the SC17 Test of Time Award. “Tiling is well-known and has been included in many compilers and code transformation systems. The talk will explore the basic contribution of the SC1989 paper to the current state of iteration space tiling.”

Announcing a Series of Worldwide GPU Hackathons in 2018

ORNL is hosting a series of GPU Hackathons in 2018. The first event will take place March 5-9 at TU Dresden in Germany. “The goal of each Hackathon is for current or prospective user groups of large hybrid CPU-GPU systems to send teams of at least 3 developers along with either (1) a (potentially) scalable application that could benefit from GPU accelerators, or (2) an application running on accelerators that need optimization. There will be intensive mentoring during this 5-day hands-on workshop, with the goal that the teams leave with applications running on GPUs, or at least with a clear roadmap of how to get there. Our mentors come from national laboratories, universities, and vendors, and besides having extensive experience in programming GPUs, many of them develop the GPU-capable compilers and help define standards such as OpenACC and OpenMP.”

Advanced Protein Prediction Using Deep Learning on Blue Waters Supercomputer

Researchers at NCSA used the Blue Waters Supercomputer and Deep Learning to achieve a breakthrough in protein structure predictions. As published in the Cell Systems journal, the research was conducted by Jian Peng, NCSA Faculty Fellow and Assistant Professor in the Department of Computer Science at Illinois and Yang Liu, a graduate student in the Department of Electrical and Computer Engineering. “Peng’s research proposes to largely explore a more accurate function for evaluating predicted protein structures through his development of the deep learning tool, DeepContact. DeepContact automatically leverages local information and multiple features to discover patterns in contact map space and embeds this knowledge within the neural network. Furthermore, in subsequent prediction of new proteins, DeepContact uses what it has learned about structure and contact map space to impute missing contacts and remove spurious predictions, leading to significantly more accurate inference of residue-residue contacts.”

Swiss HPC Conference Returns to Lugano in April with Winter HPCXXL User Group

Today the HPC Advisory Council announced that registration is now open for the Swiss HPC Conference. The event takes place April 9-12 in Lugano, Switzerland. For the first time, the conference will be held in concert with the Winter HPCXXL User Group meeting. “We are very excited to organize a joint conference here in Lugano, bringing together the communities of HPCAC and HPCXXL,” said Hussein Harake, HPC system manager, CSCS. “We believe that such a collaboration will offer a unique opportunity for HPC professionals to discuss and share their knowledge and experiences.”

Video: Deep Learning for the Enterprise with POWER9

Sumit Gupta from IBM gave this talk at H2O World. “From chat bots, to recommendation engines, to Google Voice and Apple Siri, AI has begun to permeate our lives. We will demystify what AI is, present the difference between machine learning and deep learning, why the huge interest now, show some fun use cases and demos, and then discuss use cases of how deep learning based AI methods can be used to garner insights from data for enterprises. We will also talk about what IBM is doing to make deep learning and machine learning more accessible and useful to a broader set of data scientists, and how to build out the right hardware infrastructure.”