high-performance computing


It is not just hardware in the spotlight,


however, as the company also highlighted some of the latest research that is making use of these technologies, such as the Human Brain Project. Created in 2013 by the European Commission, the project’s aims include gathering, organising and disseminating data describing the brain and its diseases, and simulating the brain itself. Scientists at the Jülich Research Centre


(Forschungszentrum Jülich), in Germany, are developing a 3D multi-modal model of the human brain. Tey do this by analysing thousands of ultrathin histological brain slices using microscopes and advanced image analysis methods – and then reconstructing these slices into a 3D computer model. Analysing and registering high-resolution 2D image data into a 3D reconstruction is both


data and compute-intensive. To process this data as fast as possible, the Jülich researchers are using Jülich’s Juron supercomputer – one of two pilot systems delivered by IBM and Nvidia to the Jülich Research Centre. Te Juron cluster is composed of 18 IBM Minsky servers, each with four Tesla P100 GPU accelerators with Nvidia NVLink interconnect technology.


Deep learning drives product innovation Hewlett Packard Enterprise was also keen to get in on the AI action, as the company launched the HPE Apollo 10 Series. HPE Apollo 10 Series is a new platform,


optimised for entry-level Deep Learning and AI applications. Te HPE Apollo sx40 System is a 1U dual socket Intel Xeon Gen10 server with support for up to four Nvidia Tesla SXM2 GPUs with NVLink. Te HPE Apollo pc40 System is a 1U dual socket Intel Xeon Gen10 server with support for up to four PCIe GPU cards. ‘Today, customer’s HPC requirements go


AS THE DATA


VOLUMES IN HPC GROW, THE INDUSTRY IS RESPONDING BY MOVING AWAY FROM THE PREVIOUS FLOPS- CENTRIC MODEL


www.scientific-computing.com l @scwmagazine


beyond superior performance and efficiency,’ said Bill Mannel, vice president and general manager, HPC and AI solutions, Hewlett Packard Enterprise. ‘Tey are also increasingly considering security, agility and cost control. With today’s announcements, we are addressing these considerations and delivering optimised systems, infrastructure management, and services capabilities that provide a new compute experience.’


Collaboration to drive AI performance Mellanox announced that it is optimising its existing technology to help accelerate deep learning performance. Te company announced that deep learning frameworks such


as TensorFlow, Caffe2, Microsoſt Cognitive Toolkit, and Baidu PaddlePaddle can now leverage Mellanox’ smart offloading capabilities to increase performance and, the company claims, provide near-linear scaling across multiple AI servers. Te Mellanox announcement highlights the


work of the company to ensure its products can meet the requirements of users running deep learning workloads, but it also demonstrates Mellanox’s willingness to work with partners, such as Nvidia, to further increase performance and integration of their technologies. ‘Advanced deep neural networks depend


upon the capabilities of smart interconnect to scale to multiple nodes, and move data as fast as possible, which speeds up algorithms and reduces training time,’ said Gilad Shainer, vice president of marketing at Mellanox Technologies. ‘By leveraging Mellanox technology and solutions, clusters of machines are now able to learn at a speed, accuracy, and scale, that push the boundaries of the most demanding cognitive computing applications.’ One of the key points of this announcement


is that Mellanox is working with partners to ensure that deep learning frameworks and hardware (such as Nvidia GPUs) are compatible with Mellanox interconnect fabric to help promote the use of Mellanox networking solutions to AI/deep learning users. More information was provided by Duncan


Poole, director of platform alliances at Nvidia, who concluded: ‘Developers of deep learning applications can take advantage of optimised frameworks and Nvidia’s upcoming NCCL 2.0 library, which implements native support for InfiniBand verbs and automatically selects GPUDirect RDMA for multi-node or Nvidia NVLink when available for intra-node communications.’ l


AUGUST/SEPTEMBER 2017 15


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32