Jack Dongarra: A Not So Simple Matter of Software

For a few moments, the atmosphere was more Rock Concert than Supercomputing Conference with many members of a packed audience standing, cheering, and waving signs as Jack Dongarra took the stage

By John Russell

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

By John Russell

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

Building a world-class research community in Lithuania

Vilnius University introduces a new era of scientific investigations in Lithuania with a ten-fold boost in processing power from new high performance computing cluster from Dell Technologies.

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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…

Team from Zhejiang University

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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Today, manufacturers of all sizes face many challenges. Not only do they need to deliver complex products quickly, they must do so with limited resources while continuously innovating and improving product quality. With the use of computer-aided engineering (CAE), engineers can design and test ideas for new products without having to physically build many expensive prototypes. This helps lower costs, enhance productivity, improve quality, and reduce time to market.

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Whitepaper

Porting CUDA Applications to Run on AMD GPUs

A workload-driven system capable of running HPC/AI workloads is more important than ever. Organizations face many challenges when building a system capable of running HPC and AI workloads. There are also many complexities in system design and integration. Building a workload driven solution requires expertise and domain knowledge that organizational staff may not possess.

This paper describes how Quanta Cloud Technology (QCT), a long-time Intel® partner, developed the Taiwania 2 and Taiwania 3 supercomputers to meet the research needs of the Taiwan’s academic, industrial, and enterprise users. The Taiwan National Center for High-Performance Computing (NCHC) selected QCT for their expertise in building HPC/AI supercomputers and providing worldwide end-to-end support for solutions from system design, through integration, benchmarking and installation for end users and system integrators to ensure customer success.

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