Helix Nebula: from Grid to Cloud and Lessons Learned So Far

By Tiffany Trader

July 17, 2012

European cloud computing is taking off as can be seen in the progress of Helix Nebula – the Science Cloud, a collaboration between select service providers and three of Europe’s most prominent research centers, CERN, the European Space Agency (ESA), and the European Molecular Biology Laboratory (EMBL). The Helix Nebula project announced last week that they were on the verge of moving from the initial proof of concept phase to the start of the two-year pilot phase, which involves expanded proofs of concepts and perhaps some additional demand side partners.

Helix NebulaThe three flagship applications (one from each research institution) have been deployed to cloud resources provided by Atos, CloudSigma and T-Systems.

Michael Higgins, Chief Enterprise Solutions Officer at CloudSigma, makes the point that CERN and the other research institutions are not really customers, not yet at least. Currently, they are all partners exploring the feasibility of migrating workloads from the grid to the cloud. Higgins further explains that Helix Nebula brings together two sets of consortia, the demand side and the supply side, which is comprised of both public and private cloud providers.

In the initial proof-of-concept phase, commercial terms are not imposed by the cloud providers on a pay-per-use basis, but instead involve agreed-upon bulk-payment monetary contribution from each of the demand side participants, based on each vendor’s ability to deliver the proofs of concept.

CERN and the Worldwide LHC Computing Grid

Project participant CERN generates a huge amount of data on its Large Hadron Collider. The LHC generally produces about 15 petabytes (15 million gigabytes) of data annually, but this year, they’re on track to reach 30 petabytes as the search for the Higgs boson particle has picked up steam. To analyze all this data, research partners from around the world rely on the Worldwide LHC Computing Grid (WLCG), a global grid network of more than 150 computing centers.

When asked what role if any Helix Nebula played in the preliminary Higgs boson discovery, the response from WLCG Project Leader Ian Bird was a qualified none:

“We did succeed in running some simulation work in production, and I dare say some of that resulting simulation was used in the analysis of the data that led to the announcement last week, but this was a very tiny fraction compared to the huge amount of data that had to be processed.”

Helix Nebula has gone from its initial stages of technology review to the point now where CloudSigma has completed all three proofs of concept for the flagship workloads. Based on that success, they’re now moving toward the next phase, which is to expand the proofs of concept and to begin to refine the commercial terms.

“They’re not only expanding the original proofs of concept, but opening the door to more demand-side flagship projects. Up to now, the researchers have not been overly pressed to understand TCO – total cost of ownership – so this may be something they’re struggling with,” suggests Higgins. “Like at CERN, IT doesn’t pay for electricity, so they would not know how much to factor in for their in-house server electricity costs.”

Higgins makes the case that cloud bursting is more suitable for science than grid or on-premise systems because very little science occurs 24/7, around the clock. There are times when ATLAS is not generating data and other times when there is an backlog of work. Oversubscription and underutilization are often the norm with designated resources, but bursting allows researchers to use only the resources they need when they need it.

Institutions are facing funding issues, explains Higgins, which means there is less hardware to evergreen or purchase new. Every day there is more compute demand, and the resources are strained. They have to look to the cloud for cost-savings or they have to find new sources of capital investment. Running workloads in the cloud on a pay-per-use basis erases the problem of buying a $10,000 platform and running it two weeks out of four.

While these arguments make sense, applying virtualization and cloud technologies to current grid resources is another avenue for boosting utilization rates and creating elasticity and scalability, and CERN is exploring these options in addition to the public cloud. Still, Bird notes that simply having a private cloud won’t work either, because the research depends on a federated connected cloud.

Grid Versus Cloud

Asked what will happen to the Worldwide LHC grid as cloud ramps up, Bird says that it will remain. He uses the opportunity to discuss current grid developments. They are running virtual machines on some of the sites, and they are in the process of deploying OpenStack. These projects are designed to improve their internal efficiency as well as the way they run services and provide services, and will also give them additional opportunities to interact with cloud sites.

Bird points to an important distinction between grid and cloud which is one of federation. Grid, despite being a networked collection of distributed computing systems, has evolved to become a highly-unified computing resource. Whereas using multiple cloud providers essentially means you have a collection of disparate resources that are difficult to integrate, even when they’re working together as with Helix Nebula. In addition to the API headaches, there are a myriad of standards and integration pain points to contend with, he says, elaborating further on the grid/cloud dichotomy:

The reason why we used the grid in the first place is because the computing resources that we have access to which are provided by the science funding agencies are physically distributed around the world and we have to have a way of putting these together, so that we did with grid technologies. So for us, the grid is a way of sharing resources and collaborating, while the cloud isn’t really that, it’s more to do with economies of scale. It’s distributed in the way it’s remote from you, but it’s really a different concept. One of the interesting things is how much of that [cloud] technology can we use to improve the way we run our own computer centers simply by not having to support grid infrastructure, but switching that to some cloud technology and how much can we do by buying resources from commercial resources?

As for comparisons between the grid I/O problem versus the cloud I/O problem, Bird observes that while these are similar, this is an area that has received a lot of investment on the grid side. Over the years, the partner institutions have developed dedicated optical private networks between the servers and their large compute centers and they also make significant use of specialized academic networks. When asked if he sees similar developments happening in cloud, Bird is doubtful they’ll happen in the near term, and points to another question for Helix Nebula, which is what is the connectivity of these partners and can we reach them over the academic networks? He says these are among the types of issues they want to pin down in the next couple of years.

On the positive side, Bird notes that networking has changed a lot since the deployment of grid. Prices have come down and data management techniques have become more effective. These developments will be applied to the Helix Nebula project.

Regarding the more specific process of transferring workloads from the grid to the cloud, CloudSigma’s Higgins explains that some of them ported over without too much work, while others have required more extensive retooling due to the numerous changes in the software design practices and machine architectures since these applications were first written. As an illustration, many of the apps in use today were written prior to JAVA and NoSQL databases.

Bird has a somewhat different take on the nitty-gritty details of the cloud migration, saying that they did not need to change the code at all. He explains that the LHC codes fall into the “high-throughput computing” model, where the different pieces of the running code do not need to communicate with each other. The grid resource and cloud resource are basically the same, he notes, i.e., a big cluster of Linux machines. The main difference is how you access this resource and how you move data in and out, however “at the level of real-code running on the machine, it’s the same,” he says.

A Cloud is Not a Cloud

From Bird’s point of view, CERN saw the successful completion of its three proofs of concept. The process entailed running the same simulation workload with the three different cloud providers. The conclusion Bird draws from this, is that while successful, “a cloud is not a cloud is not a cloud.” You cannot just write-once, run-anywhere; there are integration headaches.

According to the grid expert, they absolutely will need an adapter layer that knows how to talk to the different providers. This is essential if they want to use these resources in a dynamic way that involves moving between cloud providers. When asked about a possible performance penalty, he responds that since this is mainly a way to get data into the cloud, any overhead would be likely be negligible. He adds these so-called cloud broker solutions already exist in the open source domain; Deltacloud and libcloud are examples. While this layer adds complexity and could even introduce faults, it’s unavoidable at this stage of the game if you value transparency and interoperability.

Up until now, CERN has been running cloud-friendly workloads with little network I/O dependency. When asked about the HPC cloud bandwidth issue, i.e., the limitations of getting data in and out of the cloud, Bird said this absolutely could be a problem. Their normal data processing workloads involve transferring petabytes of data. During the two-year pilot phase, they will address several issues related to data movement: whether they can move data in and out of the cloud at this scale, whether they can afford to do this, and possible policy issues involved with moving academic data into the commercial domain.

Bird returns to the bottom line, which is cost: “Can we afford to move data in and out and can we afford to store data in the cloud?” he asks.

“There are many different use cases. I think we can overcome the technical issues; the most interesting question is what’s the real cost of doing this and how does it compare with the infrastructure that we have currently?”

Bird gives the impression that while cloud migration has potential, it’s not a “sure thing” and by no means a panacea. How do you get a collection of cloud providers to behave as a federated resource? The first steps involve supplementing the existing grid resources with a few cloud providers, says Bird, which allows those involved to begin the process of learning how to integrate the various pieces.

New Science

CloudSigma’s Higgins is most excited about the new science that can be enabled by the cloud as more and more science databases are migrated over. Right now, there are three databases that do not combine to support any practical implications, but the possibilities for meta-analysis are intriguing. For example, with ESA’s earth observation data stored in the cloud, a researcher could ask the World Health Organization for the mosquito outbreak data. This would create a platform where both databases would be available, allowing scientists to expand their research horizons.

This kind of integration requires a lot of work because each database has its own schema. Right now CloudSigma is working on an initiative that is attempting to create master schemas. For example, the earth observation data is linked to latitude and longitude, whereas the mosquito outbreak data is based on distance from a known point on a compass bearing. So the question then is how to marry these distinct data points. The new effort is hammering out a global schema which can make sense of these disparate units so that it becomes a useful tool for researchers. At that point, a scientist could answer queries such as “six miles north of Nairobi, how wet is it and how many mosquitos are we expecting to break out?”

Higgins is confident that creating a rich ecosystem of multiple scientific databases will draw new researchers to the cloud.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Kyoto University ACCMS Implements Fine-grained Power Management

September 19, 2018

Data center power management is a ubiquitous challenge and in few places is it more so than at Kyoto University Academic Center for Computing and Media Studies (ACCMS)) where power consumption limits were imposed followi Read more…

By Staff

What’s New in HPC Research: September (Part 1)

September 18, 2018

In this new bimonthly feature, HPCwire will highlight newly published research in the high-performance computing community and related domains. From exascale to quantum computing, the details are here. Check back every Read more…

By Oliver Peckham

House Passes $1.275B National Quantum Initiative

September 17, 2018

Last Thursday the U.S. House of Representatives passed the National Quantum Initiative Act (NQIA) intended to accelerate quantum computing research and development. Among other things it would establish a National Quantu Read more…

By John Russell

HPE Extreme Performance Solutions

Introducing the First Integrated System Management Software for HPC Clusters from HPE

How do you manage your complex, growing cluster environments? Answer that big challenge with the new HPC cluster management solution: HPE Performance Cluster Manager. Read more…

IBM Accelerated Insights

A Crystal Ball for HPC

People are notoriously bad at predicting the future.  This very much includes experts. In the Forbes article “Why Most Predictions Are So Bad” Philip Tetlock discusses the largest and best-known test of the accuracy of expert predictions which show that any experts would do better if they make random guesses. Read more…

Nvidia Accelerates AI Inference in the Datacenter with T4 GPU

September 14, 2018

Nvidia is upping its game for AI inference in the datacenter with a new platform consisting of an inference accelerator chip--the new Turing-based Tesla T4 GPU--and a refresh of its inference server software packaged as Read more…

By George Leopold

House Passes $1.275B National Quantum Initiative

September 17, 2018

Last Thursday the U.S. House of Representatives passed the National Quantum Initiative Act (NQIA) intended to accelerate quantum computing research and developm Read more…

By John Russell

Nvidia Accelerates AI Inference in the Datacenter with T4 GPU

September 14, 2018

Nvidia is upping its game for AI inference in the datacenter with a new platform consisting of an inference accelerator chip--the new Turing-based Tesla T4 GPU- Read more…

By George Leopold

DeepSense Combines HPC and AI to Bolster Canada’s Ocean Economy

September 13, 2018

We often hear scientists say that we know less than 10 percent of the life of the oceans. This week, IBM and a group of Canadian industry and government partner Read more…

By Tiffany Trader

Rigetti (and Others) Pursuit of Quantum Advantage

September 11, 2018

Remember ‘quantum supremacy’, the much-touted but little-loved idea that the age of quantum computing would be signaled when quantum computers could tackle Read more…

By John Russell

How FPGAs Accelerate Financial Services Workloads

September 11, 2018

While FSI companies are unlikely, for competitive reasons, to disclose their FPGA strategies, James Reinders offers insights into the case for FPGAs as accelerators for FSI by discussing performance, power, size, latency, jitter and inline processing. Read more…

By James Reinders

Update from Gregory Kurtzer on Singularity’s Push into FS and the Enterprise

September 11, 2018

Container technology is hardly new but it has undergone rapid evolution in the HPC space in recent years to accommodate traditional science workloads and HPC systems requirements. While Docker containers continue to dominate in the enterprise, other variants are becoming important and one alternative with distinctly HPC roots – Singularity – is making an enterprise push targeting advanced scale workload inclusive of HPC. Read more…

By John Russell

At HPC on Wall Street: AI-as-a-Service Accelerates AI Journeys

September 10, 2018

AIaaS – artificial intelligence-as-a-service – is the technology discipline that eases enterprise entry into the mysteries of the AI journey while lowering Read more…

By Doug Black

No Go for GloFo at 7nm; and the Fujitsu A64FX post-K CPU

September 5, 2018

It’s been a news worthy couple of weeks in the semiconductor and HPC industry. There were several HPC relevant disclosures at Hot Chips 2018 to whet appetites Read more…

By Dairsie Latimer

TACC Wins Next NSF-funded Major Supercomputer

July 30, 2018

The Texas Advanced Computing Center (TACC) has won the next NSF-funded big supercomputer beating out rivals including the National Center for Supercomputing Ap Read more…

By John Russell

IBM at Hot Chips: What’s Next for Power

August 23, 2018

With processor, memory and networking technologies all racing to fill in for an ailing Moore’s law, the era of the heterogeneous datacenter is well underway, Read more…

By Tiffany Trader

Requiem for a Phi: Knights Landing Discontinued

July 25, 2018

On Monday, Intel made public its end of life strategy for the Knights Landing "KNL" Phi product set. The announcement makes official what has already been wide Read more…

By Tiffany Trader

CERN Project Sees Orders-of-Magnitude Speedup with AI Approach

August 14, 2018

An award-winning effort at CERN has demonstrated potential to significantly change how the physics based modeling and simulation communities view machine learni Read more…

By Rob Farber

ORNL Summit Supercomputer Is Officially Here

June 8, 2018

Oak Ridge National Laboratory (ORNL) together with IBM and Nvidia celebrated the official unveiling of the Department of Energy (DOE) Summit supercomputer toda Read more…

By Tiffany Trader

New Deep Learning Algorithm Solves Rubik’s Cube

July 25, 2018

Solving (and attempting to solve) Rubik’s Cube has delighted millions of puzzle lovers since 1974 when the cube was invented by Hungarian sculptor and archite Read more…

By John Russell

AMD’s EPYC Road to Redemption in Six Slides

June 21, 2018

A year ago AMD returned to the server market with its EPYC processor line. The earth didn’t tremble but folks took notice. People remember the Opteron fondly Read more…

By John Russell

MLPerf – Will New Machine Learning Benchmark Help Propel AI Forward?

May 2, 2018

Let the AI benchmarking wars begin. Today, a diverse group from academia and industry – Google, Baidu, Intel, AMD, Harvard, and Stanford among them – releas Read more…

By John Russell

Leading Solution Providers

SC17 Booth Video Tours Playlist

Altair @ SC17

Altair

AMD @ SC17

AMD

ASRock Rack @ SC17

ASRock Rack

CEJN @ SC17

CEJN

DDN Storage @ SC17

DDN Storage

Huawei @ SC17

Huawei

IBM @ SC17

IBM

IBM Power Systems @ SC17

IBM Power Systems

Intel @ SC17

Intel

Lenovo @ SC17

Lenovo

Mellanox Technologies @ SC17

Mellanox Technologies

Microsoft @ SC17

Microsoft

Penguin Computing @ SC17

Penguin Computing

Pure Storage @ SC17

Pure Storage

Supericro @ SC17

Supericro

Tyan @ SC17

Tyan

Univa @ SC17

Univa

Pattern Computer – Startup Claims Breakthrough in ‘Pattern Discovery’ Technology

May 23, 2018

If it weren’t for the heavy-hitter technology team behind start-up Pattern Computer, which emerged from stealth today in a live-streamed event from San Franci Read more…

By John Russell

Sandia to Take Delivery of World’s Largest Arm System

June 18, 2018

While the enterprise remains circumspect on prospects for Arm servers in the datacenter, the leadership HPC community is taking a bolder, brighter view of the x86 server CPU alternative. Amongst current and planned Arm HPC installations – i.e., the innovative Mont-Blanc project, led by Bull/Atos, the 'Isambard’ Cray XC50 going into the University of Bristol, and commitments from both Japan and France among others -- HPE is announcing that it will be supply the United States National Nuclear Security Administration (NNSA) with a 2.3 petaflops peak Arm-based system, named Astra. Read more…

By Tiffany Trader

D-Wave Breaks New Ground in Quantum Simulation

July 16, 2018

Last Friday D-Wave scientists and colleagues published work in Science which they say represents the first fulfillment of Richard Feynman’s 1982 notion that Read more…

By John Russell

Intel Pledges First Commercial Nervana Product ‘Spring Crest’ in 2019

May 24, 2018

At its AI developer conference in San Francisco yesterday, Intel embraced a holistic approach to AI and showed off a broad AI portfolio that includes Xeon processors, Movidius technologies, FPGAs and Intel’s Nervana Neural Network Processors (NNPs), based on the technology it acquired in 2016. Read more…

By Tiffany Trader

Intel Announces Cooper Lake, Advances AI Strategy

August 9, 2018

Intel's chief datacenter exec Navin Shenoy kicked off the company's Data-Centric Innovation Summit Wednesday, the day-long program devoted to Intel's datacenter Read more…

By Tiffany Trader

TACC’s ‘Frontera’ Supercomputer Expands Horizon for Extreme-Scale Science

August 29, 2018

The National Science Foundation and the Texas Advanced Computing Center announced today that a new system, called Frontera, will overtake Stampede 2 as the fast Read more…

By Tiffany Trader

GPUs Power Five of World’s Top Seven Supercomputers

June 25, 2018

The top 10 echelon of the newly minted Top500 list boasts three powerful new systems with one common engine: the Nvidia Volta V100 general-purpose graphics proc Read more…

By Tiffany Trader

The Machine Learning Hype Cycle and HPC

June 14, 2018

Like many other HPC professionals I’m following the hype cycle around machine learning/deep learning with interest. I subscribe to the view that we’re probably approaching the ‘peak of inflated expectation’ but not quite yet starting the descent into the ‘trough of disillusionment. This still raises the probability that... Read more…

By Dairsie Latimer

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This