September 18, 2018
In this new bimonthly feature, HPCwire will highlight newly published research in the high-performance computing community and related domains. From exascale Read more…
April 29, 2014
There is little point to building expensive exaflop-class computing machines if applications are not available to exploit the tremendous scale and parallelism. Read more…
January 29, 2013
Noted HPC pioneers weigh in on the coming class of exascale systems. Read more…
June 6, 2012
As we move down the road toward exascale computing and engage in discussion of zettascale, one issue becomes increasingly obvious: we are leaving a large part of the HPC community behind. But it needn't be so. If we developed compact, power efficient petascale computers, not only could we help broaden the base of high-end users, but we could also provide a foundation for future bleeding-edge supercomputers. Read more…
March 31, 2011
Is the HPC community too focused on the 10-year milestone? Read more…
March 28, 2011
In Michael Wolfe's second column on programming for exascale systems, he underscores the importance of exposing parallelism at all levels of design, either explicitly in the program, or implicitly within the compiler. Wolfe calls on developers to express this parallelism, in a language and in the generated code, and to exploit the parallelism, efficiently and effectively, at runtime on the target machine. He reminds the community that the only reason to pursue parallelism is for higher performance. Read more…
March 8, 2011
There are at least two ways exascale computing can go, as exemplified by the top two systems on the latest TOP500 list: Tianhe-1A and Jaguar. The Chinese Tianhe-1A uses 14,000 Intel multicore processors with 7,000 NVIDIA Fermi GPUs as compute accelerators, whereas the American Jaguar Cray XT-5 uses 35,000 AMD 6-core processors. Read more…
February 2, 2011
With exascale predictions all the rage, here's a more sobering look at the next big thing in supercomputing. 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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