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State of SYCL – ECP BOF Showcases Progress and Performance
Enabling interoperability across U.S. exascale supercomputers is one of the chief goals for the U.S. Exascale Computing Project (ECP), which has broadly overseen development of the early software …
Roadmap for Building a US National AI Research Resource Released
Last week the National AI Research Resource (NAIRR) Task Force released its final report and roadmap for building a national AI infrastructure to include computational resources, high-quality dat …
Fujitsu Study Says Quantum Decryption Threat Still Distant
January 23, 2023
Global computer and chip manufacturer Fujitsu today reported that a new study performed on its 39-qubit quantum simulator suggests it will remain difficult for Read more…
NSTC Issues New Report on US Quantum Sciences Funding and Priorities
January 12, 2023
The National Science and Technology Council (NSTC) this week released its third annual report on spending and activities supporting the National Quantum Initiat Read more…
Arm Aims to Be at the Center of Increasingly Diverse Datacenter
November 5, 2022
Arm's been riding high on the mobile market for decades now, but has struggled to make its mark on servers. But the company hopes to reverse that with some new initiatives that Arm executives addressed at the recent Arm DevSummit held last week. The top initiative revolves around providing programming and design tools so its chip... Read more…
CXL Brings Datacenter-sized Computing with 3.0 Standard, Thinks Ahead to 4.0
August 2, 2022
A new version of a standard backed by major cloud providers and chip companies could change the way some of the world's largest datacenters and fastest supercomputers are built. The CXL Consortium on Tuesday announced a new specification called CXL 3.0 – also known as Compute Express Link 3.0... Read more…
UCIe Consortium Incorporates, Nvidia and Alibaba Round Out Board
August 2, 2022
The Universal Chiplet Interconnect Express (UCIe) consortium is moving ahead with its effort to standardize a universal interconnect at the package level. The c Read more…
OpenCAPI to Be Folded into CXL
August 1, 2022
As the need for speed drives computational workloads, more standards organizations are coalescing around a standard called Compute Express Link – also known a Read more…
The Race to Ensure Post Quantum Data Security
July 21, 2022
Fault-tolerant quantum computers won’t exist for years – a decade is the most common estimate. When they do arrive, thanks to Shor’s now-famous algorithm, they will be able to crack the most widely-used encryption methods, which are based on factoring. Earlier this month, the National Institute of Standards and Technology (NIST) settled on four... Read more…
Reflecting on the 25th Anniversary of ASCI Red and Continuing Themes for Our Heterogenous Future
April 26, 2022
In the third of a series of guest posts on heterogeneous computing, James Reinders shares experiences surrounding the creation of ASCI Red and ties that system' Read more…
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Powering Up Automotive Simulation: Why Migrating to the Cloud is a Game Changer
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.
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How to Save 80% with TotalCAE Managed On-prem Clusters and Cloud
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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