MLCommons today released the latest MLPerf Inferencing (v3.0) results for the datacenter and edge. While Nvidia continues to dominate the results – topping all performance categories – other …
Hedging is, of course, a ubiquitous practice in FS and there are well-developed classical computational approaches for implementing this risk mitigation strategy. The challenge has been the compu …
March 28, 2023
In what’s become a regular GPU Technology Conference feature, Bill Dally, Nvidia chief scientist and SVP of research, provides a glimpse into how Nvidia organ Read more…
March 21, 2023
Nvidia today announced general availability for its BlueField-3 data processing unit (DPU) along with impressive early deployments including Oracle Cloud Infras Read more…
March 6, 2023
While quantum computing makes its way haphazardly towards practical realty, quantum key distribution (QKD) is moving quickly towards greater commercial use. Tod Read more…
February 21, 2023
In an interesting twist on quantum-inspired work making its way into traditional HPC – and in this case a step further into cloud-based HPC – AWS today intr Read more…
February 15, 2023
Bringing early visions of quantum technology into practical commercial reality will require many participants. How important will the big consulting firms be? A Read more…
February 9, 2023
QC Ware is one of the growing number of quantum algorithm/software specialists seeking to create tools that leverage quantum computing’s power and also hide t Read more…
January 26, 2023
Europe-based quantum computing pioneers Multiverse Computing and Pasqal, and global bank Crédit Agricole CIB today announced successful conclusion of a 1.5-yea Read more…
January 9, 2023
Making sense of today’s quantum computing landscape is challenging. IBM’s research chief Dario Gil says quantum computing writ large is now undeniably an industry. Jim Clarke, Intel’s quantum hardware chief, says hold your horses – we’re still 10-15 years from a fault-tolerant machine. Markus Pflitsch, founder and CEO of Swiss start-up Terra Quantum/QMware, says why wait, his company is delivering quantum... 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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