The Cloud-Enabled Space Weather Platform

By Everett Toews

February 14, 2011

Space weather is the result of solar winds interacting with the Earth’s magnetosphere. The most visible effect of space weather is the phenomenon of the Aurora Borealis (i.e. the Northern Lights). Space weather research is diverse in scope and includes, among a host of related questions to explore, the study of the impact of space weather on satellites orbiting Earth. Now more than ever scientists require a scalable, robust platform to study the magnetosphere.

The purpose of the Cloud-Enabled Space Weather Platform (CESWP) project is to bring the power and flexibility of cloud computing to space weather physicists.

The goal is to lower the barriers for the physicists to conduct their science–that is, to make it easier to collaborate with other scientists, develop space weather models, run simulations, produce visualizations and enable provenance. Success of the project is measured by the broad acceptance and use of the platform by the space weather science community.

The community of platform users includes space weather physicists who are developing models to help us better understand space weather and the magnetosphere. The principal investigator for CESWP is Dr. Robert Rankin, Professor in the Department of Physics at the University of Alberta. In addition to the University of Alberta, the institutions that are connected are Peking University, the University of California Los Angeles (UCLA), the University of New Brunswick, and Sharcnet, which is a high performance computing center run out of the University of Windsor. The availability zone at the University of Alberta acts as the CESWP Cloud Controller, initially handling all requests to operate on cloud resources.

The CESWP application itself is running in a virtual machine on a node controller in the CESWP cloud. Users visit the application as they would any normal web site from a web browser on their desktop machine, laptop, tablet or smart phone. Users of the cloud platform are presented with a view that is a simple HTML web page rendered in their browser and interact with the application by submitting requests to the controller from the view. Depending on the nature of the request, the controller may load models from the database or initiate an asynchronous call to the CloudService to perform a cloud-based operation. The results of the request are then passed to a view, which is sent back to the users as the response.

In essence, this project is building a cloud for this international community of physicists and given the nature of cloud computing, infrastructure can be geographically distributed. For the purposes of this project, a wide area network (WAN) is required to carry the traffic. For the Cloud-Enabled Space Weather Platform (CESWP), Canada’s Advanced Research and Innovation Network (CANARIE) fills this role. CANARIE is a dedicated network of high-speed, fiber optic cable that stretches across Canada and links researchers throughout Canada and around the world.

To operate an IaaS cloud you require a software framework on which it will run. For CESWP, Eucalyptus was initially selected as the cloud framework but the Virtual Computing Lab, OpenNebula, Nimbus and Eucalyptus were also considered during the survey of cloud management software at the project outset during the end of 2009.

Ultimately, Eucalyptus was selected based on both technical merits and long-term prospects. Among the technical reasons was its support for the Kernel Virtual Machine (KVM) and the Amazon Web Services (AWS) application programming interface (API). Support for the AWS API was particularly attractive, as the option to operate as a hybrid cloud with AWS was important.

Infrastructure as a Service is a complex issue and, as a consequence, Eucalyptus is a complex piece of software. Eucalyptus provided an environment to experiment with IaaS, however, given that IaaS is still a relatively green field, new prospects, such as OpenStack, did not exist at the time of the initial survey even though they still warrant an evaluation.

The scientists can connect to their virtual machines via secure shell (SSH) or NoMachine.  NoMachine is remote access software with support for a graphical user interface (GUI). This means that researchers can use NoMachine to access their VMs and be presented with a GUI desktop as if they were sitting in front of a physical machine. NoMachine has been of significant value to the CESWP project and for these purposes has been a good choice for connecting to cloud based VMs with a graphical user interface.

To ease the interaction with the CESWP cloud a web application written in Groovy and Grails was developed. Groovy is an open source dynamic language for the Java Virtual Machine that is fully compatible with the Java language itself. Grails is an open source web application framework that applies principles like convention over configuration to improve development productivity.
To develop CESWP, the team followed the principles of Scrum. Scrum is an agile software development methodology focused on the iterative development of a product. Iterations are organized into a three-week “sprint” wherein the team creates a working product increment. That product increment is delivered to the researchers as a demonstration in order to solicit feedback.

Technically speaking, CESWP itself is composed of three distinct parts. First, there is the CESWP cloud.  The cloud is comprised of servers, network and Eucalyptus. These resources combine together to provide the Infrastructure as a Service that the physicists will use to conduct their science.

Second, there is the CESWP toolkit. This toolkit is included on every VM image in the cloud and is comprised of scripts, both bash and Python, which ease interaction with the CESWP cloud.  These scripts perform such functions as running simulations in parallel to perform parameter sweeps, bundling VM images and enabling provenance.

Third, there is the CESWP application. The application is primarily developed with of the Groovy programming language, the Grails web framework and the Scrum agile development methodology. These resources combine together to create a GUI web application with a wizard-based interface that the physicists will use to interact with the CESWP cloud.

The primary motivation of the Cloud-Enabled Space Weather Platform is to lower the barriers for the physicists to conduct their science. There are a number of factors that obstruct the space weather physicists from concentrating on their science. The CESWP cloud paired with the CESWP application and toolkit addresses each of these barriers.

First, the specification and acquisition of hardware is consolidated in the CESWP cloud. Instead of specifying and purchasing desktop or server hardware for individual researchers on a piecemeal basis, the purchasing can be done in bulk for the benefit of the entire group. The procurement of this bulk hardware could still take weeks or months but is done only once per period as opposed to multiplying this effort for every researcher whenever the need arises.

Second, the maintenance of the hardware is consolidated in the CESWP cloud. Naturally, the scientists will always require hardware with which to access the cloud resources. However, there will no longer be a need for each researcher to have a laptop on which to develop their models and a high-powered desktop machine on which to run their simulations. The cloud effectively replaces the need for each researcher to maintain separate sets of hardware.

Third, the CESWP cloud has currently limited the selection of operating systems to Ubuntu and CentOS on the virtual machine images. This prevents a proliferation of operating systems and enables the cloud administrator to focus support effort on these two. In our experience, the physicists do not particularly care what operating system they are running, as long as it comes with the precompiled and preconfigured software that they prefer to use so we tailored the operating system VM images built for our community of users.

Fourth, one of the most time consuming aspects of the scientists work is getting code to compile properly. Be it their code when they have moved to a new machine or the code of a framework or tool they would like to use. The CESWP cloud can reduce the amount of time consumed in both cases. Once a researcher has compiled their own code in a VM, they can then bundle that VM into another VM image. The new image can be instantiated any number of times without having to recompile the code.  Likewise, once a framework (e.g. the Space Weather Modeling Framework) or tool has been compiled on a VM, it to can be bundled and made available to all of the physicists without recompilation.

Fifth, when a researcher’s model has reached a certain level of maturity he may want to run that model as a simulation on a grid or cluster to take advantage of high performance computing.  This is of great benefit to the researchers as their code can be processed many times more quickly than on their local machine. However, grid resources are in high demand and, therefore, scientists often must submit their jobs to a queue in order to be processed. The wait time can be hours to weeks. Instead, utilizing the CESWP toolkit, the researchers can run their simulations on the CESWP cloud now. If the CESWP cloud is low on resources the researchers can burst onto the Amazon Web Services (AWS) cloud, for a price.

In addition to lowering barriers CESWP also enables global access to a researcher’s virtual machine and access to their VM from a variety of devices. By default the virtual machines are globally accessible via secure channels. The VMs can be secured with either usernames and passwords or usernames and private keys. All traffic to and from the VM is encrypted. This global access can come from a variety of devices such as smart phones, tablets, laptops, netbooks or desktops.  Global access also enables collaboration as the researcher’s colleagues can be invited to access the VM to share data and collaborate on model development.

The space weather physics community is internationally distributed. Having cloud computing resources geographically close to its users lowers latency. This improves their experience interacting with the cloud. In addition, having all of the researchers using a single internationally distributed cloud will make it easier to share resources and collaborate.

Provenance is also enabled in the CESWP cloud. When a VM is bundled for provenance, using the CESWP toolkit, all of a physicist’s model code and data are bundled along with it. That is, the virtual machine’s file system is saved into another VM image. Also, a snapshot is taken of all of the virtual machine’s attached storage.Thus the preserved instance becomes a reproducible record of the scientists work. It is exact, complete, and unambiguous.

Work continues on Cloud-Enabled Space Weather Platform. The CESWP application is at the prototype stage and requires further refinement for production usage. The CESWP toolkit has only begun and scripts are routinely being added to enhance its utility. Naturally, we would like to expand the CESWP cloud by adding compute and storage capacity. The projected date for putting the Cloud-Enabled Space Weather Platform into production is June 2011.

Although this is the Cloud-Enabled Space Weather Platform, what we are building is not necessarily specific to space weather. Ultimately, we hope to build a platform that could potentially be used by other scientific disciplines.

About the Author

Everett Toews received his Bachelor of Science in Computing Science from the University of Alberta and has been working in the software development industry for the past decade. 

Everett has been working at Cybera for the past year and a half where he is building a cloud for use by space weather physicists at the University of Alberta on a project that goes by the name CESWP.

More available about Cybera can be found here.

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!

Advancing Modular Supercomputing with DEEP and DEEP-ER Architectures

February 24, 2017

Knowing that the jump to exascale will require novel architectural approaches capable of delivering dramatic efficiency and performance gains, researchers around the world are hard at work on next-generation HPC systems. Read more…

By Sean Thielen

Weekly Twitter Roundup (Feb. 23, 2017)

February 23, 2017

Here at HPCwire, we aim to keep the HPC community apprised of the most relevant and interesting news items that get tweeted throughout the week. Read more…

By Thomas Ayres

HPE Server Shows Low Latency on STAC-N1 Test

February 22, 2017

The performance of trade and match servers can be a critical differentiator for financial trading houses. Read more…

By John Russell

HPC Financial Update (Feb. 2017)

February 22, 2017

In this recurring feature, we’ll provide you with financial highlights from companies in the HPC industry. Check back in regularly for an updated list with the most pertinent fiscal information. Read more…

By Thomas Ayres

HPE Extreme Performance Solutions

Manufacturers Reaping the Benefits of Remote Visualization

Today’s manufacturers are operating in an ever-changing atmosphere, and finding new ways to boost productivity has never been more vital.

This is why manufacturers are ramping up their investments in high performance computing (HPC), a trend which has helped give rise to the “connected factory” and Industrial Internet of Things (IIoT) concepts that are proliferating throughout the industry today. Read more…

Rethinking HPC Platforms for ‘Second Gen’ Applications

February 22, 2017

Just what constitutes HPC and how best to support it is a keen topic currently. Read more…

By John Russell

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

ExxonMobil, NCSA, Cray Scale Reservoir Simulation to 700,000+ Processors

February 17, 2017

In a scaling breakthrough for oil and gas discovery, ExxonMobil geoscientists report they have harnessed the power of 717,000 processors – the equivalent of 22,000 32-processor computers – to run complex oil and gas reservoir simulation models. Read more…

By Doug Black

Advancing Modular Supercomputing with DEEP and DEEP-ER Architectures

February 24, 2017

Knowing that the jump to exascale will require novel architectural approaches capable of delivering dramatic efficiency and performance gains, researchers around the world are hard at work on next-generation HPC systems. Read more…

By Sean Thielen

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

Drug Developers Use Google Cloud HPC in the Fight Against ALS

February 16, 2017

Within the haystack of a lethal disease such as ALS (amyotrophic lateral sclerosis / Lou Gehrig’s Disease) there exists, somewhere, the needle that will pierce this therapy-resistant affliction. Read more…

By Doug Black

Azure Edges AWS in Linpack Benchmark Study

February 15, 2017

The “when will clouds be ready for HPC” question has ebbed and flowed for years. Read more…

By John Russell

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

US, China Vie for Supercomputing Supremacy

November 14, 2016

The 48th edition of the TOP500 list is fresh off the presses and while there is no new number one system, as previously teased by China, there are a number of notable entrants from the US and around the world and significant trends to report on. Read more…

By Tiffany Trader

Lighting up Aurora: Behind the Scenes at the Creation of the DOE’s Upcoming 200 Petaflops Supercomputer

December 1, 2016

In April 2015, U.S. Department of Energy Undersecretary Franklin Orr announced that Intel would be the prime contractor for Aurora: Read more…

By Jan Rowell

D-Wave SC16 Update: What’s Bo Ewald Saying These Days

November 18, 2016

Tucked in a back section of the SC16 exhibit hall, quantum computing pioneer D-Wave has been talking up its new 2000-qubit processor announced in September. Forget for a moment the criticism sometimes aimed at D-Wave. This small Canadian company has sold several machines including, for example, ones to Lockheed and NASA, and has worked with Google on mapping machine learning problems to quantum computing. In July Los Alamos National Laboratory took possession of a 1000-quibit D-Wave 2X system that LANL ordered a year ago around the time of SC15. Read more…

By John Russell

Enlisting Deep Learning in the War on Cancer

December 7, 2016

Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. Read more…

By John Russell

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

Leading Solution Providers

CPU Benchmarking: Haswell Versus POWER8

June 2, 2015

With OpenPOWER activity ramping up and IBM’s prominent role in the upcoming DOE machines Summit and Sierra, it’s a good time to look at how the IBM POWER CPU stacks up against the x86 Xeon Haswell CPU from Intel. Read more…

By Tiffany Trader

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

Nvidia Sees Bright Future for AI Supercomputing

November 23, 2016

Graphics chipmaker Nvidia made a strong showing at SC16 in Salt Lake City last week. Read more…

By Tiffany Trader

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

Dell Knights Landing Machine Sets New STAC Records

November 2, 2016

The Securities Technology Analysis Center, commonly known as STAC, has released a new report characterizing the performance of the Knight Landing-based Dell PowerEdge C6320p server on the STAC-A2 benchmarking suite, widely used by the financial services industry to test and evaluate computing platforms. The Dell machine has set new records for both the baseline Greeks benchmark and the large Greeks benchmark. Read more…

By Tiffany Trader

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

What Knights Landing Is Not

June 18, 2016

As we get ready to launch the newest member of the Intel Xeon Phi family, code named Knights Landing, it is natural that there be some questions and potentially some confusion. Read more…

By James Reinders, Intel

  • arrow
  • Click Here for More Headlines
  • arrow
Share This