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ANL Special Colloquium on The Future of Computing
May 19, 2022
There are, of course, a myriad of ideas regarding computing’s future. At yesterday’s Argonne National Laboratory’s Director’s Special Colloquium, The Future of Computing, guest speaker Sadasivan Shankar, did his best to convince the audience that the high-energy cost of the current computing paradigm – not (just) economic cost; we’re talking entropy here – is fundamentally undermining computing’s progress such that... Read more…
Microsoft’s ‘Singularity’ to Enable Global Accelerator Network for AI Training
February 24, 2022
In science fiction and future studies, the word “singularity” is invoked in reference to a rapidly snowballing artificial intelligence that, repeatedly iterating on itself, eclipses all human knowledge and ability. It is this word that Microsoft—perhaps ambitiously—has invoked for its new AI project, a “globally distributed scheduling service for highly efficient and reliable execution of deep learning training and inference workloads.” Read more…
What’s New in HPC Research: Pollution, Dark Data, Human Brains & More
July 20, 2021
In this regular feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programming Read more…
Using XSEDE Allocation, Researchers Develop Neural Network to Predict DNA Methylation Sites
August 19, 2020
Through methylation, the behavior of DNA changes, but its overall structure remains the same. This process is central to many normal, essential processes, but e Read more…
Heterogeneous Computing Gets a Code Similarity Tool
July 31, 2020
A machine programming framework for heterogeneous computing championed by Intel Corp. and university partners is built around an automated engine that analyzes Read more…
Army Seeks AI Ground Truth
April 3, 2020
Deep neural networks are being mustered by U.S. military researchers to marshal new technology forces on the Internet of Battlefield Things. U.S. Army and industry researchers said this week they have developed a “confidence metric” for assessing the reliability of AI and machine learning algorithms used in deep neural networks. The metric seeks to boost... Read more…
Micron Accelerator Bumps Up Memory Bandwidth
February 26, 2020
Deep learning accelerators based on chip architectures coupled with high-bandwidth memory are emerging to enable near real-time processing of machine learning a Read more…
ML Experts Confront Reproducibility Claims
March 13, 2019
Machine learning researchers are pushing back on the recent assertion that the AI framework is a key contributor to a reproducibility crisis in scientific research. Rick Stevens, associate laboratory director for computing, environment and life sciences at Argonne National Laboratory... Read more…
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Whitepaper
A New Standard in CAE Solutions for Manufacturing
Today, manufacturers of all sizes face many challenges. Not only do they need to deliver complex products quickly, they must do so with limited resources while continuously innovating and improving product quality. With the use of computer-aided engineering (CAE), engineers can design and test ideas for new products without having to physically build many expensive prototypes. This helps lower costs, enhance productivity, improve quality, and reduce time to market.
As the scale and scope of CAE grows, manufacturers need reliable partners with deep HPC and manufacturing expertise. Together with AMD, HPE provides a comprehensive portfolio of high performance systems and software, high value services, and an outstanding ecosystem of performance optimized CAE applications to help manufacturing customers reduce costs and improve quality, productivity, and time to market.
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Sponsored by HPE
Whitepaper
Porting CUDA Applications to Run on AMD GPUs
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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