September 18, 2022
Albert Einstein famously described quantum mechanics as "spooky action at a distance" due to the non-intuitive nature of superposition and quantum entangled par Read more…
October 10, 2018
GPU leader Nvidia, generally associated with deep learning, autonomous vehicles and other higher-end enterprise and scientific workloads (and gaming, of course) Read more…
September 26, 2018
Already home to three Cray XC40 systems (the last one deployed in 2016), the Met Office, a leading weather center in the U.K., has now added Cray’s Urika-XC s Read more…
September 6, 2017
For about a year the Renaissance Computing Institute (RENCI) has been assembling best practices and open source components around data-driven scientific researc Read more…
August 11, 2016
Hewlett Packard Enterprise (HPE) announced today that it will acquire rival HPC server maker SGI for $7.75 per share, or about $275 million, inclusive of cash and debt. The deal ends the seven-year reprieve that kept the SGI banner flying after Rackable Systems purchased the bankrupt Silicon Graphics Inc. for $25 million in 2009 and assumed the SGI brand. Bringing SGI into its fold bolsters HPE's high-performance computing and data analytics capabilities and expands its position... Read more…
June 8, 2016
Who crunches more data faster, wins. It’s this drive that cuts through and clarifies the essence of the evolutionary spirit in the computer industry, the dual Read more…
May 16, 2016
Nielsen has collaborated with Intel to migrate important pieces of HPC technology into Nielsen’s big-data analytic workflows including MPI, mature numerical libraries from NAG (the Numerical Algorithms Group), as well as custom C++ analytic codes. This complementary hybrid approach integrates the benefits of Hadoop data management and workflow scheduling with an extensive pool of HPC tools and C/C++ capabilities for analytic applications. In particular, the use of MPI reduces latency, permits reuse of the Hadoop servers, and co-locates the MPI applications close to the data. Read more…
March 8, 2016
These are exciting times for HPC. High-performance computing and its cousin high-productivity computing are expanding such that the previous definitions of HPC 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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