Today Penguin Computing announced the Relion 1904GT server, which packs four GPU accelerators in a 1U form factor. As the company’s densest 1U GPU server, Relion is an exceptional platform for running scientific and engineering applications that support GPU technology.
UCX is a collaboration between industry, laboratories, and academia to create an open-source production grade communication framework for HPC applications. “The path to Exascale, in addition to many other challenges, requires programming models where communications and computations unfold together, collaborating instead of competing for the underlying resources. In such an environment, providing holistic access to the hardware is a major component of any programming model or communication library. With UCX, we have the opportunity to provide not only a vehicle for production quality software, but also a low-level research infrastructure for more flexible and portable support for the Exascale-ready programming models.”
Today IBM along with Nvidia and two U.S. Department of Energy National Laboratories today announced a pair of Centers of Excellence for supercomputing – one at the Lawrence Livermore National Laboratory and the other at the Oak Ridge National Laboratory. The collaborations are in support of IBM’s supercomputing contract with the U.S. Department of Energy. They will enable advanced, large-scale scientific and engineering applications both for supporting DOE missions, and for the Summit and Sierra supercomputer systems to be delivered respectively to Oak Ridge and Lawrence Livermore in 2017 and to be operational in 2018.
Today IBM announced that the company is now offering Nvidia Tesla K80 GPU accelerators on bare metal cloud servers. With the new offering, IBM Cloud is bringing high-speed performance to the SoftLayer cloud infrastructure, enabling companies to build supercomputing clusters without having to expand their existing technology infrastructure.
Today Nvidia updated its GPU-accelerated deep learning software to accelerate deep learning training performance. With new releases of DIGITS and cuDNN, the new software provides significant performance enhancements to help data scientists create more accurate neural networks through faster model training and more sophisticated model design.
“In Deep Learning what we do is try to minimize the amount of hand engineering and get the neural nets to learn, more or less, everything. Instead of programing computers to do particular tasks, you program the computer to know how to learn. And then you can give it any old task, and the more data and the more computation you provide, the better it will get.”