January 11, 2023
Intel is bringing subscription and rental services to semiconductors as it explores new business models, but it remains to be seen if buyers warm up to the idea of paying extra to unlock features on a chip. Intel is bringing an "on-demand" feature to its new Xeon CPUs codenamed Sapphire Rapids, which the company launched on Tuesday after long delays. The on-demand feature involves paying a fee to activate... Read more…
November 17, 2022
At the awards ceremony at SC22 in Dallas today, ACM awarded the 2022 ACM Gordon Bell Prize to a team of researchers who used four major supercomputers – inclu Read more…
April 20, 2021
HPC is all about scalability. The most powerful systems. The biggest data sets. The most cores, the most bytes, the most flops, the most bandwidth. HPC scales! Notwithstanding a few recurring arguments over the last twenty years about scaling up versus scaling out, the definition of scalability... Read more…
April 14, 2021
Deep learning (DL) applications have unique architectural characteristics and efficiency requirements. Hence, the choice of computing system has a profound impa Read more…
November 1, 2016
Graphcore emerged from stealth mode today with news of a $30 million Series A round to help finance ongoing development of its machine learning (ML) and deep le Read more…
January 19, 2016
2016 promises to be pivotal in the IBM/OpenPOWER effort to claim a non-trivial chunk of the Intel-dominated high-end server landscape. Big Blue’s stated goal Read more…
November 12, 2015
Intel x86 processors continue to dominate HPC servers while the number of cores per processor also keeps rising, perhaps no surprises there. Also somewhat antic Read more…
October 29, 2015
NVIDIA today announced availability of its newest PGI Accelerator Fortran, C and C++ compilers (version 15.10) now with support for OpenACC directives-based par Read more…
The increasing complexity of electric vehicles result in large and complex computational models for simulations that demand enormous compute resources. On-premises high-performance computing (HPC) clusters and computer-aided engineering (CAE) tools are commonly used but some limitations occur when the models are too big or when multiple iterations need to be done in a very short term, leading to a lack of available compute resources. In this hybrid approach, cloud computing offers a flexible and cost-effective alternative, allowing engineers to utilize the latest hardware and software on-demand. Ansys Gateway powered by AWS, a cloud-based simulation software platform, drives efficiencies in automotive engineering simulations. Complete Ansys simulation and CAE/CAD developments can be managed in the cloud with access to AWS’s latest hardware instances, providing significant runtime acceleration.
Two recent studies show how Ansys Gateway powered by AWS can balance run times and costs, making it a compelling solution for automotive development.
Five Recommendations to Optimize Data Pipelines
When building AI systems at scale, managing the flow of data can make or break a business. The various stages of the AI data pipeline pose unique challenges that can disrupt or misdirect the flow of data, ultimately impacting the effectiveness of AI storage and systems.
With so many applications and diverse requirements for data types, management systems, workloads, and compliance regulations, these challenges are only amplified. Without a clear, continuous flow of data throughout the AI data lifecycle, AI models can perform poorly or even dangerously.
To ensure your AI systems are optimized, follow these five essential steps to eliminate bottlenecks and maximize efficiency.
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