Paul Messina from Argonne presented this talk at the HPC User Forum in Santa Fe. “The Exascale Computing Project (ECP) was established with the goals of maximizing the benefits of HPC for the United States and accelerating the development of a capable exascale computing ecosystem. The ECP is a collaborative effort of two U.S. Department of Energy organizations – the Office of Science (DOE-SC) and the National Nuclear Security Administration (NNSA).”
This Rock Stars of HPC series is about the men and women who are changing the way the HPC community develops, deploys, and operates the supercomputers and social and economic impact of their discoveries. “As the lead developer of the VMD molecular visualization and analysis tool, John Stone’s code is used by more than 100,000 researchers around the world. He’s also a CUDA Fellow, helping to bring HPC to the masses with accelerated computing. In this way and many others, John Stone is certainly one of the Rock Stars of HPC.”
“HPC software is becoming increasingly complex. The space of possible build configurations is combinatorial, and existing package management tools do not handle these complexities well. Because of this, most HPC software is built by hand. This talk introduces “Spack”, an open-source tool for scientific package management which helps developers and cluster administrators avoid having to waste countless hours porting and rebuilding software.” A tutorial video on using Spack is also included.
“The basic idea of deep learning is to automatically learn to represent data in multiple layers of increasing abstraction, thus helping to discover intricate structure in large datasets. NVIDIA has invested in SaturnV, a large GPU-accelerated cluster, (#28 on the November 2016 Top500 list) to support internal machine learning projects. After an introduction to deep learning on GPUs, we will address a selection of open questions programmers and users may face when using deep learning for their work on these clusters.”
“Artificial Intelligence will deliver the next wave of societal transformation on parallel with the industrial, technical and internet revolutions that preceded it. As our AI-fueled future evolves, we have a tremendous opportunity to address opportunities from scare resource utilization and scientific exploration to inclusion and human rights expansion. Intel Executive VP Diane Bryant will share Intel’s vision for unleashing AI as well as a perspective on how to accelerate the delivery of #AIforgood.”
On Thursday, the U.S.-China Economic & Security Review Commission (USCC) held a hearing on the current and potential future state of supercomputing innovation worldwide, with an emphasis on China’s position on the global stage relative to the USA. Addison Snell from Intersect360 Research provided this testimony in answer to USCC’s questions for the hearing.
“2017 will see the introduction of many technologies that will help shape the future of HPC systems. Production-scale ARM supercomputers, advancements in memory and storage technology such as DDN’s Infinite Memory Engine (IME), and much wider adoption of accelerator technologies and from Nvidia, Intel and FPGA manufacturers such as Xilinx and Altera, are all helping to define the supercomputers of tomorrow.”
In this video, Dr. Marcelo Ponce from SciNet presents: Scientific Visualization with Python. “SciNet is Canada’s largest supercomputer centre, providing Canadian researchers with computational resources and expertise necessary to perform their research on scales not previously possible in Canada. We help power work from the biomedical sciences and aerospace engineering to astrophysics and climate science.”
“A new data type called a “posit” is designed for direct drop-in replacement for IEEE Standard 754 floats. Unlike unum arithmetic, posits do not require interval-type mathematics or variable size operands, and they round if an answer is inexact, much the way floats do. However, they provide compelling advantages over floats, including simpler hardware implementation that scales from as few as two-bit operands to thousands of bits. For any bit width, they have a larger dynamic range, higher accuracy, better closure under arithmetic operations, and simpler exception-handling.”
In this video from SC16, Dr. Eng Lim Goh from HPE/SGI discusses new trends in HPC Energy Efficiency and Deep Learning. “SGI’s leadership in data analytics derives from deep expertise in High Performance Computing and over two decades delivering many of the world’s fastest supercomputers. Leveraging this experience and SGI’s innovative shared and distributed memory computing solutions for data analytics enables organizations to achieve greater insight, accelerate innovation, and gain competitive advantage.”