January 5, 2022
Edge computing is an approach in which the data is processed and analyzed at the point of origin – the place where the data is generated. This is done to make data more accessible to end-point devices, or users, and to reduce the response time for data requests. HPC-class computing and networking technologies are critical to many edge use cases, and the intersection of HPC and ‘edge’ promises to be a hot topic in 2022. Read more…
July 31, 2020
The header image was captured over a 24-hour period—across all time zones—by a U.S. National Aeronautics and Space Administration (NASA) satellite. It illus Read more…
May 29, 2020
Developing and deploying applications across heterogeneous infrastructures like HPC or Cloud with diverse hardware is a complex problem. Enabling developers to Read more…
February 20, 2019
“Are we under attack?” asked Professor Elmarie Biermann of the Cyber Security Institute during the recent South African Centre for High Performance Computin Read more…
Giving developers the ability to write code once and use it on different platforms is important. Organizations are increasingly moving to open source and open standard solutions which can aid in code portability. AMD developed a porting solution that allows developers to port proprietary NVIDIA® CUDA® code to run on AMD graphic processing units (GPUs).
This paper describes the AMD ROCm™ open software platform which provides porting tools to convert NVIDIA CUDA code to AMD native open-source Heterogeneous Computing Interface for Portability (HIP) that can run on AMD Instinct™ accelerator hardware. The AMD solution addresses performance and portability needs of artificial intelligence (AI), machine learning (ML) and high performance computing (HPC) for application developers. Using the AMD ROCm platform, developers can port their GPU applications to run on AMD Instinct accelerators with very minimal changes to be able to run their code in both NVIDIA and AMD environments.
A workload-driven system capable of running HPC/AI workloads is more important than ever. Organizations face many challenges when building a system capable of running HPC and AI workloads. There are also many complexities in system design and integration. Building a workload driven solution requires expertise and domain knowledge that organizational staff may not possess.
This paper describes how Quanta Cloud Technology (QCT), a long-time Intel® partner, developed the Taiwania 2 and Taiwania 3 supercomputers to meet the research needs of the Taiwan’s academic, industrial, and enterprise users. The Taiwan National Center for High-Performance Computing (NCHC) selected QCT for their expertise in building HPC/AI supercomputers and providing worldwide end-to-end support for solutions from system design, through integration, benchmarking and installation for end users and system integrators to ensure customer success.
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