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The Hyperion-insideHPC Interviews: Ryan Quick on the ‘Prometheus Fire’ where HPC, Hyperscale and AI Converge

Ryan Quick works at the crossroads of advanced technology innovation, where hyperscale, HPC and AI come together. A principal and co-founder of boutique consulting firm Providentia Worldwide, which implements systems solutions for its clientele, Quick says edge and IoT are two catalysts bringing about the day when HPC technologies will “really start to mix and match” – even within vendors’ own product lines.

Precision Medicine pushes demand for HPC at the Edge: AI on the Fly ® Delivers

In this special guest feature, Tim Miller from One Stop Systems writes that by bringing specialized, high performance computing capabilities to the edge through AI on the Fly, OSS is helping the industry deliver on the enormous potential of precision medicine. “The common elements of these solutions are high data rate acquisition, high speed low latency storage, and efficient high performance compute analytics. With OSS, all of these building block elements are connected seamlessly with memory mapped PCI Express interconnect configured and customized as appropriate, to meet the specific environmental requirements of ‘in the field’ installations.”

CUDA-Python and RAPIDS for blazing fast scientific computing

Abe Stern from NVIDIA gave this talk at the ECSS Symposium. “We will introduce Numba and RAPIDS for GPU programming in Python. Numba allows us to write just-in-time compiled CUDA code in Python, giving us easy access to the power of GPUs from a powerful high-level language. RAPIDS is a suite of tools with a Python interface for machine learning and dataframe operations. Together, Numba and RAPIDS represent a potent set of tools for rapid prototyping, development, and analysis for scientific computing. We will cover the basics of each library and go over simple examples to get users started.”

How NVIDIA Enables Scientific Research for HPC Developers

“Researchers, scientists, and developers are advancing science by accelerating their high performance computing applications on NVIDIA GPUs using specialized libraries, directives, and language-based programming models. From computational science to AI, CUDA-X HPC, OpenACC, and CUDA are GPU-accelerating applications to deliver groundbreaking scientific discoveries. And popular languages like C, C++, Fortran, and Python are being used to develop, optimize, and deploy these applications.”

NVIDIA and Arm look to accelerate HPC Worldwide

In this video, NVIDIA’s Duncan Poole and Arm’s David Lecomber explain how the two company’s accelerate the world’s fastest supercomputers. “At SC19, NVIDIA introduced a reference design platform that enables companies to quickly build GPU-accelerated Arm-based servers, driving a new era of high performance computing for a growing range of applications in science and industry. The reference design platform — consisting of hardware and software building blocks — responds to growing demand in the HPC community to harness a broader range of CPU architectures.”

Codeplay SYCL 1.2.1 Solution offers an Open Alternative to CUDA

Today Codeplay announced the world’s first fully-conformant SYCL 1.2.1 Solution. “As a non-proprietary alternative to the incumbent CUDA, SYCL is an open standard developed by the Khronos Group that enables developers to write code for heterogeneous systems using standard C++. Developers are looking at how they can accelerate their applications without having to write optimized processor specific code. SYCL is the industry standard for C++ acceleration, giving developers a platform to write high-performance code in standard C++, unlocking the performance of accelerators and specialized processors from companies such as AMD, Intel, Renesas and Arm.”

Visualizing and Simulating Atomic Structures with CUDA

In this video, John Stone from the University of Illinois, Urbana-Champaign discusses the role of CUDA and GPUs in processing large datasets to visualize and simulate high-resolution atomic structures. CUDA does this by allowing researchers to describe hundreds of thousands to millions of independent, data-parallel work units and write software that executes on those work units, all while achieving peak hardware performance.

The ABCI Supercomputer: World’s First Open AI Computing Infrastructure

Shinichiro Takizawa from AIST gave this talk at the MVAPICH User Group. “ABCI is the world’s first large-scale Open AI Computing Infrastructure, constructed and operated by AIST, Japan. It delivers 19.9 petaflops of HPL performance and world’ fastest training time of 1.17 minutes in ResNet-50 training on ImageNet datasets as of July 2019. In this talk, we focus on ABCI’s network architecture and communication libraries available on ABCI and shows their performance and recent research achievements.”

Exploring the Universe with the SKA Radio Telescope and CUDA

In this video, Wes Armour from the Oxford eResearch Centre discusses the role of GPUs in processing large amounts of astronomical data collected by the Square Kilometre Array and how CUDA is the best suited option for their signal processing software. “The massive computational power of modern day GPUs allows code to perform algorithms such as de-dispersion, single pulse searching and Fourier Domain Acceleration Searching in real-time on very large data-sets which are comparable to those which will be produced by next generation radio-telescopes such as the SKA.”

Video: Arm HPC Update from ISC 2019

In this video, Brent Gorda provides an update on the progress on Arm HPC from the ISC 2019 conference in Frankfurt. “From the perspective of Arm in HPC, it was an excellent event with several high-profile announcements that caught everyone’s attention. The Arm ecosystem was well represented with our partners visible on the show floor and around town.”