ISC Cloud 2012 BOFs: Applications/Software, Reference Architectures and Data Transfer

By Nicole Hemsoth

October 2, 2012

At ISC Cloud 2012, talking points for the Birds of a Feather sessions were hand-picked by the participants. While the importance of security was a key theme throughout the two-day event, several other salient topics emerged during the voting process. The finalized BoF roster included “Applications and software in the cloud,” “HPC Cloud Reference Architectures” and “Data Transfer in/out of Clouds” to be held in parallel. Each group had about 10-15 participants discussing the challenges and implications of their chosen topic. After the conference, the panel moderators each submitted their notes on their findings.

BOF 1: Applications/Software in the Cloud

Moderator: David Wallom, Oxford eResearch Centre

The discussion was started with the consideration of how cloud computing could change the supply of application software with the possibility of ISV partnering with cloud providers to change the delivery model. This would allow application flexibility, but it was pointed out that there is an inherent unpredictability of a pay-as-you-go (PAYG) model. It may be an issue for those groups that have been previously subjected to a fairly stable cost model, though in many other areas PAYG is becoming more normal. A problem is that in current IaaS cloud models, costing is not simple and there may be resistance to the introduction of new business models from long-term users.

It was pointed out that it isn’t just the end-user applications but also all other components. An illustration of PAYG for areas other than end-user applications, which clearly shows one of the problems with other models is where LSF is an annual license, even though it may only be required a few times (less than 10).

With this change of model, how do we support the legacy application? This will depend on the type: Community applications that are open source will have to rely on their community and commercial applications will require their users to ‘gang up’ as it were. However, there are problems with a SaaS delivery mechanism since there could be resultant legacy version support required as many commercial customers want longevity. Over the longer term, cloud migration means users will have to be more used to version migration, and if so, application providers will have to make sure version migration is easier.

The level of cloud utilization will depend on the different application communities and different maturities of software. The possibility of flexibility is strongest where software is newest, i.e., application users do not favor one model over another. It is unlikely that cloud will affect the application design model to change MPI, and thus OpenMP will still need to be supported. On a longer term, the different types of interconnection software (MPI/OpenMP) won’t matter as the hardware will catch up with newer ideas.

We mustn’t forget that software isn’t just the application but also the networks that exist around it: Community-as-a-Service and Support-as-a-Service.

Of course, less data means that it is easier to move to the cloud, but if you can do more operations on your data in the cloud then this becomes less important, for example, only downloading the important result although this may require workflow in the cloud.

With the emergence of standard APIs for different components, the time is right for application designers to accept these changes in models by moving to the most advantageous cloud provider. We must ensure that application designers learn lessons from the previous instances where public cloud providers changed their models and made previous design decisions irrelevant or less than optimal.

  • It is a whole ecosystem. Remember that:

    • The user decides on the software that best solves their problem. End users don’t care, and they just want solutions.

    • Hardware licensing versus software licensing costs can be decisive.

    • Optimization for many different types of use cases can lead to different types of hardware solutions.

    • Cloud provider chooses hardware, software, interconnects, .i.e., the most efficient solution.

    • Community clouds targeted to different communities are not inevitable but likely as different ISV and communities get together to best optimize their requirements and solutions together.

    • Whatever use of cloud or otherwise we decide on has to fit with other parts of the business model/activity.

Cloud providers have the opportunities to get away from unnecessary user complications and also support their users with new models. There are good opportunities for long term relationships between ISV and cloud providers.

Finally the difference between the cloud and Application Service Provider (which we have had for around ten years) was discussed. It was brought to light that the quality/ubiquity of the network resources and the sheer number of resource types have changed.

BOF 2: Reference Architecture

Moderator: Josh Simons, VMware

Two basic models for moving HPC workloads into a cloud environment:

1. Virtual clusters formed by creating a persistent set of virtual machines on demand. Each virtual machine runs the same software stack (OS, libraries, batch scheduler, etc.) as was used in a bare-metal environment. This is desirable because from an end-user/scientist perspective, the interface to the compute interface remains the same: they use the same batch scheduler interfaces. The use of virtual machines is transparent to the end-user.

2. Virtual machines are created on demand to run each job and they persist only for the lifetime of the job. This allows each job to run with its own custom software stack, for individual jobs to be migrated dynamically across the virtual infrastructure for load-balancing, resiliency, or power management. This is not an evolutionary model in that the end-user would need to interact with either a new software layer that understands how to launch VMs rather than scheduling onto existing cluster nodes. This could be an entirely new layer or an augmented version of existing job schedulers.

It was noted that hybrids of the above two approaches could be used as well.

The following components were identified as being critical pieces of reference architecture for HPC in the cloud. (Not an exhaustive list.)

  • Self-service capabilities to enable end-users to create clusters on the fly.

  • A catalogue of virtual machines and software stacks that can be used to create these virtual clusters.

  • A provisioning engine to instantiate these virtual machines (it was noted that Open Stack work on “placement groups” is relevant).

  • An ability to elastically flex compute resources up and down as needs change.

  • A monitoring component to watch the health and performance of the infrastructure.

  • Billing and chargeback.

  • Data staging components – to move data in and out of the cloud.

  • Policy-based resource control mechanisms to mediate access to hardware resources between multiple cloud tenants.

  • Security – data security and protection and secure isolation of workloads in a multi-tenant environment.

It was noted that a “cloud” might not be virtualized, though virtualization was seen to make a number of the above functions easier to deliver.

It was posited that once HPC moves into the cloud, there will be a need to support complex applications that require cross-cloud workflows, similar to some of the meta-computing concepts developed within the grid computing realm. It was noted that if “cloud” is the follow-on to grid computing, then it would be useful to examine grid architectures closely, to determine which features should be brought forward into mainstream cloud architectures.

There are problems still to be solved if HPC is to move into the cloud. Some are technical – end-to-end automation of the use of HPC in the cloud. Others are business related: licensing, politics, and budgetary. The budgetary issue is particularly interesting: In the face of “unlimited” compute resources, how does an organization control access to limit its budgetary spend? This is particularly important for HPC workloads, which as we know can consume all available resources at a site. What happens when such users get access to unlimited resources in the cloud? Answering these questions will likely uncover additional required components for an HPC cloud reference architecture.

BOF 3: Data Transport

Moderator: Rolf Sperber, Alcatel-Lucent

Size Matters

There has to be a differentiation concerning the size of datasets to be transported in and out of the cloud. The target is optimized access – it can be achieved for small amount of data if there is a predictable way of accessing required data or moving data in or out of the cloud. For large datasets to be transported, the quality of service will have to be guaranteed for longer periods of time.

Small Data

To have instant access to data in a cloud, current metadata will not be sufficient. A software that has knowledge of the network infrastructure and defines a virtual network on demand is required. Multiple carrier and in consequence multiple vendor environments will have to be taken into account.

Big Data

This is about huge datasets to be transported over long distances. Final target is to have predictable transfer times for multiple datasets to be transported to a single location.

First Iteration

  • Federation of folders into a single folder with a metadata server to keep track of size, locality, etc.

  • Optimize transport by means of adequate transfer software. Here we are talking about software products (most of them commercial) that help solve the TCP problem

  • Optimize access by proactive distribution if possible. Here settled paradigms of work will have to be overcome.

Second Iteration

  • Optimize transport requirements with respect to site of computation.

  • Provide network control to enable clients to define an appropriate virtual network.

    • Multiple carriers with heterogeneous environments to be taken into account.

    • Charging models to be implemented.

Third iteration or Target

  • Further optimize applications to minimize transport requirements.

  • Integrate network control into applications.

    • Federation

    • Software defined networking taking care of both dedicated instance of time when transfer starts and duration of transfer in relation to size.

    • SDN calculating both routes and time of reservation.

    • SDN calculating total duration.

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!

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

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

HPE Extreme Performance Solutions

O&G Companies Create Value with High Performance Remote Visualization

Today’s oil and gas (O&G) companies are striving to process datasets that have become not only tremendously large, but extremely complex. And the larger that data becomes, the harder it is to move and analyze it – particularly with a workforce that could be distributed between drilling sites, offshore rigs, and remote offices. Read more…

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

Weekly Twitter Roundup (Feb. 16, 2017)

February 16, 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

Alexander Named Dep. Dir. of Brookhaven Computational Initiative

February 15, 2017

Francis Alexander, a physicist with extensive management and leadership experience in computational science research, has been named Deputy Director of the Computational Science Initiative at the U.S. Read more…

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

Cray Posts Best-Ever Quarter, Visibility Still Limited

February 10, 2017

On its Wednesday earnings call, Cray announced the largest revenue quarter in the company’s history and the second-highest revenue year. Read more…

By Tiffany Trader

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

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

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

Leading Solution Providers

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

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

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

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

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

KNUPATH Hermosa-based Commercial Boards Expected in Q1 2017

December 15, 2016

Last June tech start-up KnuEdge emerged from stealth mode to begin spreading the word about its new processor and fabric technology that’s been roughly a decade in the making. Read more…

By John Russell

Intel and Trump Announce $7B for Fab 42 Targeting 7nm

February 8, 2017

In what may be an attempt by President Trump to reset his turbulent relationship with the high tech industry, he and Intel CEO Brian Krzanich today announced plans to invest more than $7 billion to complete Fab 42. Read more…

By John Russell

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