September 2, 2010
Intel Corp has released Parallel Studio 2011, a set of four tools designed to mainstream software development on multicore x86 architectures. The update folds in a number of parallel programming technologies that the company has acquired or developed independently over the past few years, including the Cilk Arts and RapidMind technologies, and Intel's own Ct data parallel language framework. Read more…
February 11, 2010
Can a solution for HPC software live within MPI, OpenMP, CUDA, OpenCL, and/or Ct? Read more…
January 6, 2010
More videos from HPC's premier event of 2009. Read more…
December 7, 2009
While Intel prides itself on maintaining a breakneck speed for processor development, the company's Larrabee GPU effort just couldn't keep pace with graphics technology development at NVIDIA and AMD. Intel revealed late last Friday that the company would not be delivering a Larrabee-based discrete graphics product next year, and has instead decided to use the work as the basis for a software development platform. Read more…
August 20, 2009
Another one bites the dust. 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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