“Available on GitHub as Open Source, the Batch Shipyard toolkit enables easy deployment of batch-style Dockerized workloads to Azure Batch compute pools. Azure Batch enables you to run parallel jobs in the cloud without having to manage the infrastructure. It’s ideal for parametric sweeps, Deep Learning training with NVIDIA GPUs, and simulations using MPI and InfiniBand.”
“Run your Windows and Linux HPC applications using high performance A8 and A9 compute instances on Azure, and take advantage of a backend network with MPI latency under 3 microseconds and non-blocking 32 Gbps throughput. This backend network includes remote direct memory access (RDMA) technology on Windows and Linux that enables parallel applications to scale to thousands of cores. Azure provides you with high memory and HPC-class CPUs to help you get results fast. Scale up and down based upon what you need and pay only for what you use to reduce costs.”
Today the PASC17 Conference announced that Matthias Troyer from Microsoft Research will give this year’s public lecture on the topic “Towards Quantum High Performance Computing.” The event will take place June 26-28 in Lugano, Switzerland.
Today Cray announced the results of a deep learning collaboration with Microsoft CSCS designed to expand the horizons of running deep learning algorithms at scale using the power of Cray supercomputers. “Cray’s proficiency in performance analysis and profiling, combined with the unique architecture of the XC systems, allowed us to bring deep learning problems to our Piz Daint system and scale them in a way that nobody else has,” said Prof. Dr. Thomas C. Schulthess, director of the Swiss National Supercomputing Centre (CSCS). “What is most exciting is that our researchers and scientists will now be able to use our existing Cray XC supercomputer to take on a new class of deep learning problems that were previously infeasible.”
Today Microsoft released an updated version of Microsoft Cognitive Toolkit, a system for deep learning that is used to speed advances in areas such as speech and image recognition and search relevance on CPUs and Nvidia GPUs. “We’ve taken it from a research tool to something that works in a production setting,” said Frank Seide, a principal researcher at Microsoft Artificial Intelligence and Research and a key architect of Microsoft Cognitive Toolkit.
In this video from the Microsoft Ignite Conference, Tejas Karmarkar describes how to run your HPC Simulations on Microsoft Azure – with UberCloud container technology. “High performance computing applications are some of the most challenging to run in the cloud due to requirements that can include fast processors, low-latency networking, parallel file systems, GPUs, and Linux. We show you how to run these engineering, research and scientific workloads in Microsoft Azure with performance equivalent to on-premises. We use customer case studies to illustrate the basic architecture and alternatives to help you get started with HPC in Azure.”
“We are still in the first minutes of the first day of the Intelligence revolution. In this keynote, Dr. Joseph Sirosh will present 5 solutions (and their implementations) that the intelligent cloud delivers. Sirosh shares five cloud AI patterns that his team and presented at the Summit. These five patterns are really about ways to bring data and learning together in cloud services, to infuse intelligence.”
“Aligning ourselves with an organization like Microsoft further supports our mission of enabling companies to manage their application workloads whether on-premise or in a hybrid or cloud context like Microsoft Azure,” said Gary Tyreman, CEO, Univa. “Univa is working hard to bridge the gap from traditional IT environments to the new hybrid world by helping enterprises reduce complexity and run workloads more efficiently in the cloud.”
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.”
Tejas Karmarkar from Microsoft presented this talk at SC15. “Azure provides on-demand compute resources that enable you to run large parallel and batch compute jobs in the cloud. Extend your on-premises HPC cluster to the cloud when you need more capacity, or run work entirely in Azure. Scale easily and take advantage of advanced networking features such as RDMA to run true HPC applications using MPI to get the results you want, when you need them.”