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November 19, 2010
If there was a dominating theme at the Supercomputing Conference this year, it had to be GPU computing. Read more…
November 16, 2010
Although the parallel programming landscape is relatively young, it's already easy to get lost in. Beside legacy frameworks like MPI and OpenMP, we now have NVIDIA's CUDA, OpenCL, Cilk, Intel Threading Building Blocks, Microsoft's parallel programming extensions for .NET, and a whole gamut of PGAS languages. And according to Intel's Tim Mattson, that's not necessarily a good thing. Read more…
November 16, 2010
NVIDIA's CUDA is easily the most popular programming language for general-purpose GPU computing. But one of the more interesting developments in the CUDA-verse doesn't really involve GPUs at all. In September, HPC compiler vendor PGI (The Portland Group Inc.) announced its intent to build a CUDA compiler for x86 platforms. The technology will be demonstrated for the first time in public at SC10 this week in New Orleans. Read more…
November 15, 2010
Data-intensive applications are quickly emerging as a significant new class of HPC workloads. For this class of applications, a new kind of supercomputer, and a different way to assess them, will be required. That is the impetus behind the Graph 500, a set of benchmarks that aim to measure the suitability of systems for data-intensive analytics applications. Read more…
November 15, 2010
SGI has made good on its promise to create a petaflop-in-a-cabinet supercomputer that can scale up to tens and even hundreds of cabinets. Developed under the code name "Project Mojo," the company has dubbed the new product Prism XL. SGI will be showcasing the system this week in their exhibit booth at the Supercomputing Conference in New Orleans. Read more…
November 14, 2010
Like every technology-based sector, high performance computing takes its biggest leaps by the force of disruptive innovation, a term coined by the man who will keynote this year's Supercomputing Conference (SC10) in New Orleans. Clayton M. Christensen doesn't know a whole lot about supercomputing, but he knows a great deal about the forces that drive it. 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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