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Bank of Italy Converges HPC and Enterprise Office with New Cluster
October 10, 2016
The democratization of high performance computing (HPC) and the converged datacenter have been topics of late in the IT community. This is where HPC, high performance data analytics (big data/Hadoop workloads), and enterprise office applications all run on a common clustered compute architecture with a single file system and network. Read more…
Nielsen and Intel Migrate HPC Efficiency and Data Analytics to Big Data
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…
Hadoop and Spark Get RADICAL at SC15
November 13, 2015
The rapid maturation of the Apache Hadoop ecosystem has caught the eyes of HPC professionals who are eager to take advantage of emerging big data tools, such as Read more…
Stepping Up to the Life Science Storage System Challenge
October 5, 2015
Storage and data management have become the perhaps the most challenging computational bottlenecks in life sciences (LS) research. The volume and diversity of d Read more…
New Models for Research, Part III – Embracing the Big Data Stack
March 30, 2015
In the third of a four-part installation, Jay Etchings, director of operations for research computing, and senior HPC architect at Arizona State University, explores the brave new world of open big data alternatives to traditional bare metal HPC+MPI+InfiniBand for research computing. Read more…
The Best of HPC in 2014
January 27, 2015
As we turn the page to 2015, we’re taking a look back at the top stories from 2014 to reflect on just how far the fastest machines in the world (and the peopl Read more…
Tulane Accelerates Discovery with Hybrid Supercomputer
December 16, 2014
The rich culture and distinctive charm of the city of New Orleans served as the backdrop for this year's annual Supercomputing Conference (SC14). If you haven't Read more…
Cray Launches Hadoop into HPC Airspace
October 15, 2014
There has been little doubt that the convergence of traditional high performance computing with advanced analytics has been steadily underway, fed in part by a 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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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.
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