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MLCommons Issues MLPerf HPC Training Results for Larger Systems
MLCommons last week issued its third annual set of MLPerf HPC (v2.0) results intended to showcase the performance of larger systems when training more rigorous scientific models. The large size o …
Increasing the pace of innovation with HPC clusters as a service
Deploying clusters as a service with Bright Cluster Manager on Dell EMC vSAN Ready Nodes enables organizations to spin up clusters on demand to increase access to HPC resources.
In recent year …
OCF Delivers Huawei-Made HPC ‘Rocket’ for UK University
July 5, 2018
Newcastle University's first institution-wide service for high-performance computing is up and running based on a new HPC cluster called Rocket, designed and co Read more…
Zhejiang University Sets New Student Cluster LINPACK Record: 12 Teraflops
April 20, 2016
At 11:30 am local time on Wednesday in Wuhan, China, Zhejiang University was declared the winner of the High Performance LINPACK (HPL) benchmark portion of the Read more…
Cluster Lifecycle Management: Capacity Planning and Reporting
May 8, 2015
In the previous Cluster Lifecycle Management column, I discussed the best practices for proper care and feeding of your cluster to keep it running smoothly on a Read more…
Care and Feeding of Your Cluster
February 23, 2015
In the previous Cluster Lifecycle Management column, I described the crucial steps that should be taken to deploy and validate your new cluster. In this column, Read more…
How to Deploy and Validate Your Cluster
January 13, 2015
In the previous Cluster Lifecycle Management column, I discussed best practices for choosing the right vendor to build the cluster that meets your needs. Once y Read more…
NICS Tackles Big Science with Beacon
June 16, 2014
With support from the National Science Foundation and the University of Tennessee, Knoxville, the National Institute for Computational Science (NICS) is expandi Read more…
Accelerate Hadoop MapReduce Performance using Dedicated OrangeFS Servers
September 9, 2013
Recent tests performed at Clemson University achieved a 25 percent improvement in Apache Hadoop Terasort run times by replacing Hadoop Distributed File System (HDFS) with an OrangeFS configuration using dedicated servers. Key components included extension of the MapReduce “FileSystem” class and a Java Native Interface (JNI) shim to the OrangeFS client. No modifications of Hadoop were required, and existing MapReduce jobs require no modification to utilize OrangeFS. Read more…
FhGFS Designed for Scalability, Flexibility in HPC Clusters
July 24, 2013
When researchers in Germany sat down nearly a decade ago to create a brand new parallel file system for HPC clusters, they had three goals: maximum scalability, maximum flexibility, and ease of use. What they came up with was the Fraunhofer Parallel File System (FhGFS), which is now in use on supercomputers. 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.
Two recent studies show how Ansys Gateway powered by AWS can balance run times and costs, making it a compelling solution for automotive development.
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How to Save 80% with TotalCAE Managed On-prem Clusters and Cloud
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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