Utility HPC: What Every Enterprise Can Learn from Utility Supercomputing

By Jason Stowe

June 3, 2013

By Jason Stowe, CEO

I’m struck by the number of IT professionals I speak with that find unexpected uses for Utility Supercomputing. In fact, whether your compute demands warrant 50 cores of HPC, or 50,000 cores, the management challenges are quite similar. Cycle Computing is probably best known for the dramatic results our software has made possible in such compute-intensive applications as Computational Chemistry, Genome Sequencing, and Risk Analytics. For example, a Top 10 Pharma customer of ours built a 10,600 instance cluster – the equivalent of $44M in infrastructure – in just 2 hours, then used it to run 40 years of science in 11 hours for $4,372.

But many of our customers use our Utility HPC software to orchestrate workloads on clusters in the range of 10s – 100s of cores – most of which are hybrid clusters (private and public) at that. It turns out that the fact that we know how to prepare a cluster of 10,000 cores means we can actually create a small cluster that much more reliably.

So what lessons can any Enterprise IT organization learn from the experiences of their larger compute-consuming colleagues? 

1. Know thy workload to optimize costs.

Regardless of how large (or small) scale your compute requirements are, you must understand how parallelizable the applications you’ll be running on Cloud HPC resources are before you can  optimize the size and duration of the instances you procure to support them. For example, the more modular the job requests, the easier it is to parse them across low-cost spot instances of compute when they are available.

2. Play nice with others (Openness matters).

Proprietary approaches to orchestrating workloads across private and public HPC environments should be avoided in any enterprise. When looking for ways to automate and administer your HPC resources, why restrict your options in the areas of configuration or storage management, infrastructure and development tools? Even if you don’t anticipate your environment changing, it’s better to select an orchestration solution that is open and works with a wide range of supporting tools, such as Open Grid Scheduler, HTCondor and Chef, and works with applications developed in a variety of languages. Support for multiple cloud providers is also an important consideration.

3. Wherefore art thou, Data?

You don’t have to have “Big Data” requirements, to make data awareness an important part of your HPC orchestration strategy. Applications don’t live by compute alone – they inevitably require input and generate output, regardless of how large or small. Many organizations face a cost / latency tradeoff when it comes to data access: save money with lower cost, “cold storage” options such as Amazon’s Glacier which require users to wait days to restore archived data if and when they need it. Or store data in more expensive, readily available EC2 storage. It doesn’t have to be an all or nothing scenario however, if you have the ability to centrally orchestrate data movement and access as applications demand it. Another Top 10 Pharma customer is deploying Cycle’s DataManager to automate archival and retrieval of 75TB data sets to and from Amazon Glacier, without the need to re-code or disrupt their application’s workflow.

4. Manage from a central vantage point.

Not unlike an air traffic controller, Enterprise IT needs a 360⁰ view of the HPC resources at their disposal. The lack of visibility into what’s actually running where – whether on internal or external HPC clusters – forces many organizations to overprovision additional cores to handle unexpected peak loads. When organizations can manage all of their workloads from a central management console, they can readily harvest unused resources when needed. Additional resources can be cost-effectively provisioned on-demand, as required to support larger runs such as quarterly regulatory reporting risk analysis.

5. Security, Security, Security!

Security concerns are one of the most cited reasons organizations use to restrict which workloads they run in the cloud. The reality is that in fact, centrally orchestrated HPC environments afford greater control over how encryption is handled. User authentication can likewise be centrally governed and leverage existing LDAP or other established protocols to enforce consistent secure access to applications and data on-premise in the cloud. License keys are also centrally monitored so organizations can easily distinguish cloud-based access to licensed applications. This is a key benefit for IT organizations and ISVs looking to cloud-enable custom developed software without re-architecting their license key management approach. 

Applying these lessons proven successful in our real customer workloads, organizations can accelerate business analytic, product development, and scientific processes. Truly breaking through with utility HPC. For more information, please visit www.cyclecomputing.com or email info@cyclecomputing.com

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!

NSF Project Sets Up First Machine Learning Cyberinfrastructure – CHASE-CI

July 25, 2017

Earlier this month, the National Science Foundation issued a $1 million grant to Larry Smarr, director of Calit2, and a group of his colleagues to create a community infrastructure in support of machine learning research Read more…

By John Russell

DARPA Continues Investment in Post-Moore’s Technologies

July 24, 2017

The U.S. military long ago ceded dominance in electronics innovation to Silicon Valley, the DoD-backed powerhouse that has driven microelectronic generation for decades. With Moore's Law clearly running out of steam, the Read more…

By George Leopold

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in 2017 with scale-up production for enterprise datacenters and Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

HPE Servers Deliver High Performance Remote Visualization

Whether generating seismic simulations, locating new productive oil reservoirs, or constructing complex models of the earth’s subsurface, energy, oil, and gas (EO&G) is a highly data-driven industry. Read more…

Trinity Supercomputer’s Haswell and KNL Partitions Are Merged

July 19, 2017

Trinity supercomputer’s two partitions – one based on Intel Xeon Haswell processors and the other on Xeon Phi Knights Landing – have been fully integrated are now available for use on classified work in the Nationa Read more…

By HPCwire Staff

NSF Project Sets Up First Machine Learning Cyberinfrastructure – CHASE-CI

July 25, 2017

Earlier this month, the National Science Foundation issued a $1 million grant to Larry Smarr, director of Calit2, and a group of his colleagues to create a comm Read more…

By John Russell

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

Fujitsu Continues HPC, AI Push

July 19, 2017

Summer is well under way, but the so-called summertime slowdown, linked with hot temperatures and longer vacations, does not seem to have impacted Fujitsu's out Read more…

By Tiffany Trader

Researchers Use DNA to Store and Retrieve Digital Movie

July 18, 2017

From abacus to pencil and paper to semiconductor chips, the technology of computing has always been an ever-changing target. The human brain is probably the com Read more…

By John Russell

The Exascale FY18 Budget – The Next Step

July 17, 2017

On July 12, 2017, the U.S. federal budget for its Exascale Computing Initiative (ECI) took its next step forward. On that day, the full Appropriations Committee Read more…

By Alex R. Larzelere

Women in HPC Luncheon Shines Light on Female-Friendly Hiring Practices

July 13, 2017

The second annual Women in HPC luncheon was held on June 20, 2017, during the International Supercomputing Conference in Frankfurt, Germany. The luncheon provid Read more…

By Tiffany Trader

Satellite Advances, NSF Computation Power Rapid Mapping of Earth’s Surface

July 13, 2017

New satellite technologies have completely changed the game in mapping and geographical data gathering, reducing costs and placing a new emphasis on time series Read more…

By Ken Chiacchia and Tiffany Jolley

Intel Skylake: Xeon Goes from Chip to Platform

July 13, 2017

With yesterday’s New York unveiling of the new “Skylake” Xeon Scalable processors, Intel made multiple runs at multiple competitive threats and strategic Read more…

By Doug Black

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

Leading Solution Providers

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of Read more…

By Alex Woodie

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

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