Coming in the second half of 2016: The HPE Apollo 6500 System provides the tools and the confidence to deliver high performance computing (HPC) innovation. The system consists of three key elements: The HPE ProLiant XL270 Gen9 Server tray, the HPE Apollo 6500 Chassis, and the HPE Apollo 6000 Power Shelf. Although final configurations and performance are not yet available, the system appears capable of delivering over 40 teraflop/s double precision, and significantly more in single or half precision modes.
SC16 has extended the application deadline for its Impact Showcase, a forum designed to show attendees why HPC Matters in the real world. Submissions are now due Sept. 15.
IDC has announced the featured speakers for the next international HPC User Forum. The event will take place Sept. 22 in Beijing, China.
In this video from the 2016 Intel Developer Forum, Diane Bryant describes the company’s efforts to advance Machine Learning and Artificial Intelligence. Along the way, she offers a sneak peak at the Knights Mill processor, the next generation of Intel Xeon Phi slated for release sometime in 2017. “Now you can scale your machine learning and deep learning applications quickly – and gain insights more efficiently – with your existing hardware infrastructure. Popular open frameworks newly optimized for Intel, together with our advanced math libraries, make Intel Architecture-based platforms a smart choice for these projects.”
In this podcast, the Radio Free HPC team looks HPE’s pending acquisition of SGI. “Will the acquisition be good for SGI and HP customers? Our RFHPC team is in unprecedented agreement that indeed it will. The key, however, to HPE’s success will be keeping the SGI people. Rich thinks this acquisition will potentially give HPE the engineering talent it needs to compete with Cray at the high end of the market.”
Today Intel announced plans to acquire startup Nervana Systems as part of an effort to bolster the company’s artificial intelligence capabilities. “Nervana has a fully-optimized software and hardware stack for deep learning,” said Intel’s Diane Bryant in a blog post. “Their IP and expertise in accelerating deep learning algorithms will expand Intel’s capabilities in the field of AI. We will apply Nervana’s software expertise to further optimize the Intel Math Kernel Library and its integration into industry standard frameworks
“The ExaFlash Platform is an historic achievement that will reshape the storage and data center industries,” said Thomas Isakovich, CEO and Founder of Nimbus Data. “It offers unprecedented scale (from terabytes to exabytes), record-smashing efficiency (95% lower power and 50x greater density than existing all-flash arrays), and a breakthrough price point (a fraction of the cost of existing all-flash arrays). ExaFlash brings the all-flash data center dream to reality and will help empower humankind’s innovation for decades to come.”
Today Bright Computing announced that the Electronics Research Institute (ERI) and Brightskies Technologies have chosen the full suite of Bright technology to manage its HPC, big data, and cloud infrastructure. “Using Bright Computing’s technologies, we were able to showcase how to provision a virtual HPC cluster or big data cluster over cloud as extensions to the existing cluster or on demand as per users’ requests,” said Dr. Khaled Elamrawi, President of Brightskies Technologies. “This was very powerful and clearly addressed the challenges that ERI were facing.”
Olaf Weber from SGI presented this talk at LUG 2016. “In collaboration with Intel, SGI set about creating support for multiple network connections to the Lustre filesystem, with multi-rail support. With Intel Omni-Path and EDR Infiniband driving to 200Gb/s or 25GB/s per connection, this capability will make it possible to start moving data between a single SGI UV node and the Lustre file system at over 100GB/s.”
Deep learning is a method of creating artificial intelligence systems that combine computer-based multi-layer neural networks with intensive training techniques and large data sets to enable analysis and predictive decision making. A fundamental aspect of deep learning environments is that they transcend finite programmable constraints to the realm of extensible and trainable systems. Recent developments in technology and algorithms have enabled deep learning systems to not only equal but to exceed human capabilities in the pace of processing vast amounts of information.