UberCloud is the online community and marketplace where engineers and scientists can discover, try and buy the computing power and expertise on demand they need for their computational and data-intensive tasks.

With the limits of their desktop workstations often unable to provide enough computing power and memory, simulations taking too long, and the number of jobs too small to get quality results, engineers and scientists are looking for additional computing power beyond their desktop workstations. The UberCloud Marketplace provides access to a wide variety of computing providers, software vendors, enabling tools, and independent experts to simplify and ease the search for the most suitable service providers and expertise, out of hundreds that joined UberCloud in the last 18 months.

The process is simple. End-users register at the UberCloud website and complete a form to “Request a Quote from Resource Providers”. They provide information about their application, software and licenses, network interconnect, main memory per node, number of parallel cores, total CPU usage, MIC/GPUs needed, storage, remote visualization, and instructions about timing, urgency, and location of resources. And they can ask any question via UberCloud’s LiveChat feature. Then, the UberCloud takes care of the reset: automatically searching for suitable resource providers; collecting up to three quotes and sending them to the end-user; then the end-user is free to contact any or all of them to discuss the details. That’s it.

UberCloud Marketplace Video

About the UberCloud

Successful companies use high performance computing to build better products, faster, cheaper. They have the options to use desktop workstation, HPC cluster, and cloud computing resources. For organizations looking for ways to speed up their product design and development cycles, or increase productivity of their engineers and researchers, the UberCloud helps to understand how they can access high performance computers at professional data centers.

The UberCloud started in July 2012 with the free voluntary HPC Experiment which today has over 1000 participating organizations and individuals, from 68 countries. We believe that on demand access to remote computing resources (like HPC Clouds) will become an indispensable part of the engineers and scientists R&D work in the near future, for organization in HPC, computational fluid dynamics, finite element material analysis, multi-physics, chemistry, life sciences, biology, big data, and others.

To explore the challenges of the end-to-end process for an end-user to access and use remote computing resources, we are building “Teams of Four”, i.e. industry end-user, software provider, resource provider, and HPC expert, to work together on the end-user’s application, defining the requirements, getting the licenses and implementing the software on the remote system, running and monitoring it, getting the results back to the end-user, and writing a short case study about their experience, lessons learned, and recommendations, for the benefit of our community. So far, we were able to build 125 international teams and published the UberCloud Compendium with the 25 best case studies about CAE in the Cloud, sponsored by Intel. We invite everybody to join the UberCloud HPC Experiment.

In addition, the UberCloud offers a services directory, case study discussion forums, technology and services webinars, a monthly newsletter, and other detailed information, to discover how to utilize HPC as a Service. And finally, for those who are ready to use HPC as a Service in production, the UberCloud now offers the public marketplace for engineers, scientists, and their service providers.

Why Do We Need an HPC Marketplace?

The benefits of using HPC within design and development processes can be huge; such as better quality products; high Return on Investment (ROI); reducing product failure early in design; and shorten time to market. Potentially, this leads to increased competitiveness and innovation. Why then are many engineers and scientists running simulations just on their workstations, although many are regularly dissatisfied with the performance? The main reason is that the other alternatives are still coming with a lot of challenges.

The first alternative of buying an HPC server comes with high Total Cost of Ownership (TCO) as has been demonstrated by IDC already in 2007: in addition to server cost, expenses for staffing, training, software, downtime, and maintenance easily sum up to the ten-fold of the server cost over three years. Also, there are often long and painful internal procurement and approval processes. And for many, the ROI is not clear, although it is expected to be huge according to a recent IDC study on ROI in HPC.

The second alternative is recently offered by cloud computing. HPC in the Cloud (or HPC as a Service) allows engineers and scientists to continue using their own desktop system for daily design and development, and to submit (burst) the larger, more complex, time-consuming jobs into the cloud. Benefits of HPC Cloud (in addition to HPC in general) are among others on-demand access to ‘infinite’ resources, pay per use, reduced capital expenditure (CAPEX), greater business agility, and dynamically scaling resources up and down as needed.

However, HPC as a Service (in the Cloud) comes with challenges too: it is a new business and working paradigm, for the manager as well as for the engineer; security, privacy, and trust in service providers is an issue; conservative software licensing is only slowly including the pay-per-use service model; Internet bandwidth is often not able to accommodate the heavy data transfer needs; unpredictable costs of cloud computing can be a major problem in securing a budget for a given project; and there is often a lack of easy, intuitive self-service access and use of cloud resources.

And here comes the UberCloud community and marketplace which provides a platform for engineers and researchers to discover, explore, and understand the end-to-end process of accessing and using HPC Cloud resources, and to identify and resolve the roadblocks as described above. After recognizing the strategic benefits and implications for their business, end-users then can buy HPC as a Service, on demand. The marketplace assures best matching of resources from the many participating providers with the end-user’s requirements, and then offering a selection of suitable resource providers to the end-user.

Fig. 1 – The image on the right shows the temperature field of the room, while the left image shows the velocity field at a certain time of the transient simulation.

UberCloud Case Study: Fluid Dynamics Simulation with Heat Transfer in the Cloud

In many engineering problems fluid dynamics is coupled with heat transfer and many other multiphysics scenarios. The simulation produces large numerical models to be solved, so that big computational power is required in order for simulation cycles to be affordable. For SME companies in particular it is hard to implement this kind of technology in-house, because of its high investment cost and the IT specialization needed.

Biscarri Consultoria in Spain decided to explore the capabilities of cloud computing for performing highly coupled computational mechanics simulations, as an alternative to the acquisition of new computing servers to increase the computing power available. UberCloud Team 30 consisted of members Lluís M. Biscarri and Pierre Lafortune from Biscarri Consultoria in Spain, Wibke Sudholt and Nicola Fantini from CloudBroker GmbH in Switzerland, Joël Cugnoni, researcher and developer of CAELinux, and Peter Råback from CSC in Finland. CloudBroker used Amazon’s IaaS cloud offerings EC2 for compute and S3 for storage resources for this experiment.

The validation case was a room with a cold air inlet on the roof, a warm section on the floor and an outlet on a lateral wall near the floor. The initial air temperature was 25ºC. The submission of jobs to be run at AWS was done through the web interface of the CloudBroker Platform. The team’s case study reports quite some challenges which had to be overcome before the jobs ran smoothly on AWS, details are described in the UberCloud Compendium. Simulation results are shown in Figures 1 and 2.

Fig. 2 – Streamline on the inlet section.

“The main lesson learned at Biscarri Consultoria arising from participation in the UberCloud Experiment is that collaborative work over the Internet, using on-line resources like cloud computing hardware, Open Source software such as Elmer and CAElinux, and middleware platforms like CloudBroker, is a very interesting alternative to in-house calculation servers,” said Lluís Biscarri, Director at Biscarri Consultoria SL. “A backbone network such as 10Gbit Ethernet connecting computational nodes of a cloud computing platform seems not to be suitable for computational mechanics calculations that need to be run on more than one large AWS Cluster Compute node in parallel. Infiniband is necessary when running in parallel on more than one AWS Cluster Compute instance with 16 cores, to reduce latency and increase bandwidth.”