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Job of the Week: Computer Systems Researcher at Baidu USA

Baidu USA in Silicon Valley is seeking a Computer Systems Researcher in our Job of the Week. “Baidu’s research mission is to develop hard AI technologies that will reach and impact hundreds of millions of users. Our Silicon Valley Artificial Intelligence Lab (SVAIL) develops state-of-the-art AI technologies that require bleeding-edge systems to improve accuracy, scale, and performance. Our systems researchers address the many and new systems challenges that arise as we extend the AI state-of-the-art.”

Video: Deep Learning for Science

Prabhat from NERSC and Michael F. Wehner from LBNL gave this talk at the Intel HPC Developer Conference in Denver. “Deep Learning has revolutionized the fields of computer vision, speech recognition and control systems. Can Deep Learning (DL) work for scientific problems? This talk will explore a variety of Lawrence Berkeley National Laboratory’s applications that are currently benefiting from DL.”

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.”

Intel tackles FPGA HPC Memory Bottleneck

Intel recently announced the availability of the Intel Stratix 10 MX FPGA, the industry’s first field programmable gate array (FPGA) with integrated HBM2. By integrating the FPGA and the HBM2, Intel Stratix 10 MX FPGAs offer up to 10 times the memory bandwidth when compared when compared to standard DDR 2400 DIMM.

Dr. Pradeep Dubey on AI & The Virtuous Cycle of Compute

“Deep Learning was recently scaled to obtain 15PF performance on the Cori supercomputer at NERSC. Cori Phase II features over 9600 KNL processors. It can significantly impact how we do computing and what computing can do for us. In this podcast, I will discuss some of the application-level opportunities and system-level challenges that lie at the heart of this intersection of traditional high performance computing with emerging data-intensive computing.”

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.”

Harp-DAAL: A Next Generation Platform for High Performance Machine Learning on HPC-Cloud

Judy Qiu from Indiana University gave this Invited Talk at SC17. “Our research has concentrated on runtime and data management to support HPC-ABDS. This is illustrated by our open source software Harp, a plug-in for native Apache Hadoop, which has a convenient science interface, high performance communication, and can invoke Intel’s Data Analytics Acceleration Library (DAAL). We are building a scalable parallel Machine Learning library that includes routines in Apache Mahout, MLlib, and others built in an NSF funded collaboration.”

Video: Deep Learning at 15 Petaflops

Narayanan Sundaram gave this talk at the Intel HPC Developer Conference. “We present the first 15-PetaFLOP Deep Learning system for solving supervised and semi-supervised scientific pattern classification problems, optimized for Intel Xeon Phi. We use a hybrid of synchronous and asynchronous training to scale to ~9600 nodes of Cori on CNN and autoencoder networks.”

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.”

Use Intel® Inspector to Diagnose Hidden Memory and Threading Errors in Parallel Code

Intel Inspector is an integrated debugger that can easily diagnose latent and intermittent errors and guide users to locate the root cause. It does this by instrumenting the binaries, including dynamically generated or linked libraries, even when the source code is not available. This includes C, C++, and legacy Fortran codes.