Tony Hey from the Science and Technology Facilities Council presented this talk at The Digital Future conference in Berlin. “Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud computing technologies.”
This week the White House Office of Science and Technology Policy released the Strategic Plan for the NSCI Initiative. “The NSCI strives to establish and support a collaborative ecosystem in strategic computing that will support scientific discovery and economic drivers for the 21st century, and that will not naturally evolve from current commercial activity,” writes Altaf Carim, William Polk, and Erin Szulman from the OSTP in a blog post.
In this video from PASC16, Andrew Lumsdaine from Indiana University gives his perspectives on the conference. “The PASC16 Conference, co-sponsored by the Association for Computing Machinery (ACM) and the Swiss National Supercomputing Centre (CSCS), brings together research across the areas of computational science, high-performance computing, and various domain sciences.”
Today SIGHPC announced the first-ever recipients of the ACM SIGHPC/Intel Computational and Data Science Fellowship. The fellowship is funded by Intel and was announced at the high performance computing community’s SC conference in November of last year. Established to increase the diversity of students pursuing graduate degrees in data science and computational science, the fellowship […]
“We present a procedure of implementing the intermediate profiling for openQCD code that will enable the global reduction of the cost of profiling and optimizing this code commonly used in the lattice QCD community. Our approach is based on well-known SimGrid simulator, which allows for fast and accurate performance predictions of the codes on HPC architectures. Additionally, accurate estimations of the program behavior on some future machines, not yet accessible to us, are anticipated.”
The University of Cincinnati is seeking a Computer & Information Analyst in our Job of the Week.
In this video from ISC 2016, Dave Sundstrom from Hewlett Packard Enterprise describes the newly enhanced HPE Software Stack for High Performance Computing. “The HPE Core HPC Software Stack is a complete software set for the creation, optimization, and running of HPC applications. It includes development tools, runtime libraries, a workload scheduler, and cluster management, integrated and validated by Hewlett Packard Enterprise into a single software set. Core HPC Stack uses the included HPC Cluster Setup Tool to simplify and speed the installation of an HPC cluster built with HPE servers.”
“It’s great to have these incredible servers and incredible processors, but if you don’t have the people to run them – if you don’t have the people that are passionate about supercomputing, we would never get there from here.”Behind all of this magnificent technology are the fantastic faculty, researchers, interns, our corporate partners that are part of this, the National Science Foundation, there are people behind all of the success of the TACC. I think that’s the point we can never forget.”
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.