Tag: sc10 features
During this year’s SC event in New Orleans, we caught up with co-founder and CEO of Platform Computing, Songnian Zhou to take a big picture look at key movements in computing–and where grid and clouds fit within the “Renaissance” Zhou feels is taking place.
The latest Green500 list announced this week at SC10 is once again shining the spotlight on the energy efficiency of the world’s top supercomputers. But the path to more efficient high performance computing goes beyond this simple benchmark-based approach. Ralf Gruber and Vincent Keller, both from École Polytechnique Fédérale de Lausanne (EPFL), describe a holistic approach to more energy-efficient HPC operations in their book, HPC@GreenIT. HPCwire contributor Steve Conway interviewed the Swiss duo about their ideas, including a new benchmark.
The increased awareness in the HPC community of the need to maximize energy efficiency in compute-intensive environments has never been greater. With The Green500 results coming out this week, HPCwire’s Caroline Connor turned to Professor Wu Feng from Virginia Tech, the man largely credited with the movement towards environmentally-sustainable supercomputing.
Despite the still-modest showing of 10 Gigabit Ethernet (10GbE) technology in high performance computing deployments, vendors at SC10 were showcasing a wide array of performance-laden Ethernet products. IT Brand Pulse Labs analyst Tim Dales takes a look at the prospects for 10GbE in high performance computing, the migration pattern from GbE to 10GbE, and some application areas that seem especially suitable for the technology.
This week during the kickoff for SC10 we spent an hour with Bill Hilf, General Manager of Microsoft’s Technical Computing Group–a segment of the company that is devoted to HPC as well as parallel and cloud computing. We were able to cover everything from ease of use of HPC applications, GPU accessibility issues, job schedulers and their role in cloud for high-performance computing applications–and how Microsoft might finally be finding a way to grab the HPC market once again.
Interpreted programming languages usually don’t find too many friends in high performance computing. Yet Python, one of the most popular general-purpose interpreted languages, has garnered a small community of enthusiastic followers. True believers got the opportunity to hear about the language in the HPC realm in a tutorial session on Monday and a BoF session on Wednesday. Argonne National Lab’s William Scullin, who participated in both events, talked with HPCwire about the status of Python in this space and what developers might look forward to.
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.
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.
This week has brought news about GPU computing to the forefront of media attention with the announcement of the world’s most powerful supercomputer, which boasts 7,168 NVIDIA Tesla GPUs. It has also been a notable week for GPU computing in the cloud with Amazon’s unveiling of a new instance type–the Cluster GPU.
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.