Nvidia is seeking a GPU Performance Analysis Architect in our Job of the Week. “The NVIDIA GPU Compute Architecture group is seeking world-class architects to analyze processor and system architecture performance of full applications in machine learning, automotive, and high-performance computing. This position offers the opportunity to have a real impact on the hardware and software that underlies the most exciting trends in modern computing.”
The Penn State Cyber-Laboratory for Astronomy, Materials, and Physics (CyberLAMP) is acquiring a high-performance computer cluster that will facilitate interdisciplinary research and training in cyberscience and is funded by a grant from the National Science Foundation. The hybrid computer cluster will combine general purpose central processing unit (CPU) cores with specialized hardware accelerators, including the latest generation of NVIDIA graphics processing units (GPUs) and Intel Xeon Phi processors.
In this week’s Sponsored Post, Katie Garrison, of One Stop Systems explains how GPUs and Flash solutions are used in radar simulation and anti-submarine warfare applications. “High-performance compute and flash solutions are not just used in the lab anymore. Government agencies, particularly the military, are using GPUs and flash for complex applications such as radar simulation, anti-submarine warfare and other areas of defense that require intensive parallel processing and large amounts of data recording.”
Missouri-based Advanced Clustering Technologies is helping customers solve challenges by integrating NVIDIA Tesla P100 accelerators into its line of high performance computing clusters. Advanced Clustering Technologies builds custom, turn-key HPC clusters that are used for a wide range of workloads including analytics, deep learning, life sciences, engineering simulation and modeling, climate and weather study, energy exploration, and improving manufacturing processes. “NVIDIA-enabled GPU clusters are proving very effective for our customers in academia, research and industry,” said Jim Paugh, Director of Sales at Advanced Clustering. “The Tesla P100 is a giant step forward in accelerating scientific research, which leads to breakthroughs in a wide variety of disciplines.”
In this video from KAUST, Steve Scott from at Cray explains where supercomputing is going and why there is a never-ending demand for faster and faster computers. Responsible for guiding Cray’s long term product roadmap in high-performance computing, storage and data analytics, Mr. Scott is chief architect of several generations of systems and interconnects at Cray.
In this podcast, the Radio Free HPC team hosts Dan’s daughter Elizabeth. How did Dan get this way? We’re on a mission to find out even as Elizabeth complains of the early onset of Curmudgeon’s Syndrome. After that, we take a look at the Tsubame3.0 supercomputer coming to Tokyo Tech.
“TSUBAME3.0 is expected to deliver more than two times the performance of its predecessor, TSUBAME2.5,” writes Marc Hamilton from Nvidia. “It will use Pascal-based Tesla P100 GPUs, which are nearly three times as efficient as their predecessors, to reach an expected 12.2 petaflops of double precision performance. That would rank it among the world’s 10 fastest systems according to the latest TOP500 list, released in November. TSUBAME3.0 will excel in AI computation, expected to deliver more than 47 PFLOPS of AI horsepower. When operated concurrently with TSUBAME2.5, it is expected to deliver 64.3 PFLOPS, making it Japan’s highest performing AI supercomputer.”
“In recent years, major breakthroughs were achieved in different fields using deep learning. From image segmentation, speech recognition or self-driving cars, deep learning is everywhere. Performance of image classification, segmentation, localization have reached levels not seen before thanks to GPUs and large scale GPU-based deployments, leading deep learning to be a first class HPC workload.”
“In this guide, we take a high-level view of AI and deep learning in terms of how it’s being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We also present the results of a recent insideBIGDATA survey to see how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.”
DK Panda from Ohio State University presented this deck at the 2017 HPC Advisory Council Stanford Conference. “This talk will focus on challenges in designing runtime environments for exascale systems with millions of processors and accelerators to support various programming models. We will focus on MPI, PGAS (OpenSHMEM, CAF, UPC and UPC++) and Hybrid MPI+PGAS programming models by taking into account support for multi-core, high-performance networks, accelerators (GPGPUs and Intel MIC), virtualization technologies (KVM, Docker, and Singularity), and energy-awareness. Features and sample performance numbers from the MVAPICH2 libraries will be presented.”