July 3, 2017
Looking at the Top500 and Green500 ranks, one clearly realizes that most HPC systems are heterogeneous architecture using COTS (Commercial Off-The-Shelf) hardware, combining traditional multi-core CPUs with massively parallel accelerators, such as GPUs and MICs. With processor frequencies now hitting a solid wall, the only truly open avenue for riding today the Moore’s law is increasing hardware parallelism in several different ways: more computing nodes, more processors in each node, more cores within each processor, and longer vector instructions in each core. Read more…
June 8, 2017
The ways that advanced computing performance depends on more – much more – than the processor take many forms. Regardless of Moore’s Law validity, it’s Read more…
January 23, 2017
The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn't made the task of parallel progr Read more…
July 21, 2016
A relatively new architecture explicitly designed for parallelism – Swarm – based on work at MIT has shown promise for substantially speeding up classes of Read more…
February 4, 2016
Is the parallel everything era here? What happens when you can assume parallel cores? In the second half of our in-depth interview, Intel's James Reinders discu Read more…
January 21, 2016
As Chief Evangelist of Intel Software Products, James Reinders spends most of his working hours thinking about and promoting parallel programming. He’s ess Read more…
September 2, 2015
Intel has reasserted its prominence on a subset of financial benchmarks designed to evaluate platforms for the pricing and market risk analytics. More powerful Read more…
August 24, 2015
One of the most popular sessions at the Intel Developer Forum last week in San Francisco, and certainly one of the most exciting from an HPC perspective, broug Read more…
Making the Most of Today’s Cloud-First Approach to Running HPC and AI Workloads With Penguin Scyld Cloud Central™
Bursting to cloud has long been used to complement on-premises HPC capacity to meet variable compute demands. But in today’s age of cloud, many workloads start on the cloud with little IT or corporate oversight. What is needed is a way to operationalize the use of these cloud resources so that users get the compute power they need when they need it, but with constraints that take costs and the efficient use of existing compute power into account. Download this special report to learn more about this topic.
Data center infrastructure running AI and HPC workloads requires powerful microprocessor chips and the use of CPUs, GPUs, and acceleration chips to carry out compute intensive tasks. AI and HPC processing generate excessive heat which results in higher data center power consumption and additional data center costs.
Data centers traditionally use air cooling solutions including heatsinks and fans that may not be able to reduce energy consumption while maintaining infrastructure performance for AI and HPC workloads. Liquid cooled systems will be increasingly replacing air cooled solutions for data centers running HPC and AI workloads to meet heat and performance needs.
QCT worked with Intel to develop the QCT QoolRack, a rack-level direct-to-chip cooling solution which meets data center needs with impressive cooling power savings per rack over air cooled solutions, and reduces data centers’ carbon footprint with QCT QoolRack smart management.
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