Getting ready for KNL? Take a Lesson from NERSC on Optimizing

February 10, 2016

NERSC is beginning to tell the world how to optimize applications to run on the new Intel Xeon Phi processors, code name Knights Landing (KNL), that will boot i Read more…

Contrary View: CPUs Sometimes Best for Big Data Visualization

December 1, 2015

Contrary to conventional thinking, GPUs are often not the best vehicles for big data visualization. In this commentary, I discuss several key technical reasons Read more…

Speaking Many Languages into the MIC

May 8, 2013

Traditional HPC languages, Fortran, C and C++, have little native control over hardware capabilities such as SIMD operations, multi-core availability and prefetch instructions. The burden of optimization is therefore... Read more…

Heterogeneous Computing in Firing Range

April 8, 2013

Despite developer hassle, this is a great problem from the perspective of companies who are finding ways to tailor clean layers around complex code for heterogeneous computing. Take, for example, Atlanta-based AccelerEyes, which is seeing booming business because of the demand for GPU acceleration and interest in kicking the Xeon Phi co-processor tires. Read more…

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Whitepaper

Penguin Computing Scyld Cloud Central™: A New Cloud-First Approach to HPC and AI Workloads

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.

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Whitepaper

QCT POD- An Adaptive Converged Platform for HPC and AI

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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