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UberCloud Voice May 2017

Dispelling the 7 Myths of Cloud Computing

Fort Knox 4Five years ago, when we started with our UberCloud Experiments, cloud computing for compute-intensive engineering and scientific applications was in its infancy, and we faced several severe roadblocks then. A cloud experiment took on average 3 months, and about 50% failed. But over the years we have learned how to reduce or even remove them. And from the last 50 cloud experiments we did, none of them failed anymore, and the average experiment time was 3 days.  And while the roadblocks were real five years ago, today, many of them have turned into myths, with the advent of new technologies, business models, and the growing acceptance of cloud computing. We are analyzing these 7 roadblocks-turned-into-myths herehttps://lnkd.in/gW8N2np.

Nvidia’s ‘GPU Cloud’ Adopts Containers for Artificial Intelligence Development

Nvidia deep learning smallNvidia’s entry into the cloud market is differentiated from public cloud leaders by its focus on delivering development tools for training artificial intelligence models and running AI workloads using application containers. Nvidia CEO Jensen Huang unveiled the “GPU-accelerated cloud platform optimized for deep learning” during the company’s annual technology conference on Wednesday (May 10). Its AI development stack runs on the company’s distribution of Docker containers and is touted as “purpose built” for developing deep learning models on GPUs. Among the goals is giving AI developers easier access to the growing suite of deep learning software available for AI applications. The on-ramp approach to GPU-based cloud computing addresses growing requirements to gather into a single stack the proliferation of deep learning frameworks, drivers, libraries, operating systems and processors used for AI development. Read HPCwire article HERE.

The Big Issues in Engineering Simulation-Democratization

Putting simulation into the hands of the non-expert is a subject that elicits much discussion within NAFEMS committees. NAFEMS aims to acts as an advocate for the deployment of simulation, however the concern is that if the capabilities are not controlled, errors and incorrect assumptions will lead to simulation being viewed with suspicion or to improper decisions. A crucial element is the relationship between the simulation expert and the non-expert, where the responsibility of the expert ends and the ability for the non-expert to be able to use simulation safely starts. This requires simulation experts to design smart simulation applications and is somewhat analogous to the traditional handbooks where experts would develop solutions in a parametric form and a working engineer didn’t have to know how those solutions were developed but would use a formula and perhaps some graphs in conjunction with the formula to come to some kind of a prediction. This article, written by Althea de Souza appeared first in NAFEMS April 2017 Benchmark Magazine. It is the first in a series of eight, looks at Democratization and is available HERE.

Unlimited Compute Capacity at Your Desktop

Wim ArticleAccording to Jamie Gooch, editorial director of Digital Engineering “we’re entering the sweet spot for the combination of high-performance computing and simulation.” The hardware is becoming ubiquitous thanks to the many forms it can take—powerful workstations, on-site clusters and cloud-based resources. The software is now built to take advantage of all that available processing power. The final hurdle to cross is for engineering teams to understand the possibilities and how to determine what implementation will speed their workflow. This Special Issue presents an article from Wim Slagter, Director of HPC and Cloud at ANSYS, about “Unlimited Compute Capacity at Your Desktop”, where he explains the increasing demand for compute capacity, HPC in the Cloud, pay-per-use licensing, and advantages of cloud computing. You can download the special issue HERE.

insideHPC Guide: HPC Moves to the Cloud – What You Need to Know

insideHPC HPC Cloud

This white paper, written by Michael Schulman and sponsored by RedHat, introduces into cloud computing for HPC. It covers major HPC application areas, the different cloud services and delivery models such as SaaS, PaaS, and IaaS, continues with public, private, and hybrid cloud, application migration to the cloud, looks at IaaS components, OpenStack fundamentals, and stresses the importance of operating systems (re memory latency, parallel execution, containers,and virtualization). The paper closes with a few customer examples and future directions. Download the white paper from insideHPC HERE.

UberCloud at ISC High Performance Conference in Frankfurt in June

ISCUberCloud will participate again this year at the 32. ISC High Performance Conference on June 18 – 22 in Frankfurt, Germany. Right before we will also attend HP-CAST 28, the Hewlett Packard Enterprise (HPE) High Performance Consortium for Advanced Scientific and Technical Computing users group for large-scale, scientific and technical computing. Burak Yenier and Wolfgang Gentzsch will present UberCloud’s recent developments of application software containers in collaboration with HPE, Intel, and Microsoft. Latest containers for engineering software from ANSYS, COMSOL, NUMECA, OpenFOAM, Siemens PLM, SIMULIA, and VSim, and for Life Science software like Gromacs, LAMMPS, NWChem, and QMCPack will be presented. Also, join us at the Microsoft Azure Booth in the exhibition where we will present live demos about CAE containers on the Azure Cloud. And, finally, we will talk about Porting and Scaling Engineering Applications in the Cloud, on Tuesday June 20, 3:45 PM, in the Large-Scale Engineering & Cloud Session.

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