HPC Clouds — Alto Cirrus or Cumulonimbus

By Thomas Sterling and Dylan Stark

November 21, 2008

The “cloud” model of exporting user workload and services to remote, distributed and virtual environments is emerging as a powerful paradigm for improving efficiency of client and server operations, enhancing quality of service, and enabling early access to unprecedented resources for many small enterprises. From single users to major commercial organizations, cloud computing is finding numerous niche opportunities, often by simplifying rapid availability of new capabilities, with minimum time to deployment and return on requirements. Yet, one domain that challenges this model in its characteristics and needs is high performance computing (HPC).

The unique demands and decades’ long experiences of HPC on the one hand hunger for the level of service that clouds promise while on the other hand impose stringent properties, at least in some cases, that may be beyond the potential of this otherwise remarkable trend. The question is, can cloud computing reach the ethereal heights of Alto Cirrus for HPC, or will it inflict the damaging thunderclap of cumulonimbus?

While HPC immediately invokes images of TOP500 machines, the petaflops performance regime, and applications that boldly compute where no machine has calculated before, in truth this domain is multivariate with many distinct class of demand. The potential role and impact of cloud computing to HPC must be viewed across the range of disparate uses embodied by the HPC community. One possible delineation of the field (in order of most stringent first) is:

  1. Highest possible delivered capability performance (strong scaling).
  2. Weak scaling single applications.
  3. Capacity, or throughput job-stream, computing.
  4. Management of massive data sets, possibly geographically distributed.
  5. Analysis and visualization of data sets.
  6. Management and administrative workloads supporting the HPC community.

Consideration of these distinct workflows exposes opportunities for the potential exploitation of the cloud model and the benefits this might convey. Starting from the bottom of the list, the HPC community involves many everyday data processing requirements that are similar to any business or academic institution. Already some of the general infrastructure needs are quietly being outsourced to cloud-like services including databases, email, web-management, information retrieval and distribution, and other routine but critical functions. However, many of these tasks can be provided by the local set of distributed workstation and small enterprise servers. Therefore the real benefit is in reducing cost of software maintenance and per head cost of software licenses, rather than reduction of cost of hardware facilities.

Offloading tasks directly associated with doing computational science, such as data analysis and visualization, are appropriate to the use of cloud services in certain cases. This is particularly true for smaller organizations that do not have the full set of software systems that are appropriate to the local requirements. Occasionally, availability of mid-scale hardware resources, such as enterprise servers, may be useful as well if queue times do not impede fast turnaround. This domain can be expanded to include the frequent introduction of new or upgraded software packages not readily available at the local site, even if open source. Where such software is provided by ISVs, the cost of ownership or licensing may exceed the budget or even the need of occasional use.

Offerings by cloud providers may find preferable incentives for use of such software. It also removes the need for local expertise in installing, tuning, and maintaining such arcane packages. This is particularly true for small groups or individual researchers. However, a recurring theme is that HPC users tend to be in environments that incorporate high levels of expertise including motivated students and young researchers, and therefore are more likely to have access to such capabilities. The use of clouds in this case will be determined by the peculiarities of the individual and his/her situation.

Although HPC is often equated to FLOPS, it is as dependent, even sometimes more so, on bytes. Much science is data oriented, comprising data acquisition, product generation, organization, correlation, archiving, mining, and presentation. Massive data sets, especially those that are intrinsically distributed among many sites are a particularly rich target for cloud services. Maintenance of large tertiary storage facilities is particularly difficult and expensive, even for the most facilities rich environments. Data management is one area of HPC in which commercial enterprises are significantly advanced, even with respect to scientific computing expertise, with significant commercial investment being applied compared to the rarified boutique scientific computing community.

One very important factor is that confidence in data integrity of large archives may ultimately be higher among cloud resource suppliers both because of their potentially distributed nature removes issues of single point failure (like hurricanes, lightening strikes, floods), and their ability to exploit substantial investments available due to economy of scale. But one, perhaps insurmountable, challenge may impose fundamental limits in the use of clouds for data storage for some mission-critical HPC user agencies and commercial research institutions: data security. Where the potential damage for leakage or corruption of data would be strategic in nature for national security or intellectual property protection, it may be implausible that such data, no matter what the quantity or putative guarantees, will be trusted to remote and sometimes unspecified service entities.

Throughput computing is an area of strong promise for HPC in the exploitation of the emerging cloud systems. Cloud services are particularly well suited for the provisioning of resources to handle application loads of many sequential or slightly parallel (everything will have to become multicore) application tasks limited to size-constrained SMP units, such as for moderate duration parametric studies. In this case, cloud services have the potential to greatly enhance an HPC institution’s available resources and operational flexibility while improving efficiency and reducing overall cost of equipment and maintenance personnel. By offloading throughput computing workloads to cloud resources, HPC investments may be better applied to those resources unique to the needs of STEM applications not adequately served by the widely-available cloud-class processing services. However, this is tempered by the important constraint discussed above related to workloads that are security or IP sensitive.

The final two regimes of the HPC scientific and technical computing arena prove more problematic for clouds. Although weak scaling applications, where the problem size grows with the system scale such that granularity of concurrency remains approximately constant, may be suitable for a subset of the class of machines available within a cloud, the virtualization demanded by the cloud environment will preclude the hardware-specific performance tuning essential to effective HPC application execution. Virtualization is an important means of achieving user productivity, but as yet it is not a path to optimal performance, especially for high scale supercomputer grade commodity clusters (e.g., Beowulf) and MPPs (e.g., Cray XT3/4/5 and IBM BG/L/P/Q). And, while auto-tuning (as part of an autonomic framework) may one day offer a path to scalable performance, current practices at this time by users of major applications demand hands-on access to the detailed specifics of the physical machine.

Where the HPC community is already plagued with sometimes single-digit efficiencies for highly-tuned codes that may run for weeks or months to completion, the loss of substantially more performance to virtualization is untenable in many cases. An additional challenge relates to I/O bandwidth, which is sometimes a serious bottleneck if not balanced with the application needs that cannot be ensured by the abstraction of the cloud. Also, the problem of checkpoint and restart is critical to major application runs but may not be a robust service incorporated as part of most cloud systems. Therefore, a suitable system would need to make appropriate guarantees with respect to the availability of hardware and software configurations that would not be representative of the broad class of clouds.

Finally, the most challenging aspect of HPC is the constantly advancing architecture and application of capability computing systems. In their most pure form they enable strong scaling where response time is reduced for fixed sized applications with increasing system scale. Such systems imply a premium cost not just because of their mammoth size comprising upwards of a million cores and tens of terabytes of main memory, but also because of their unique design and limited market, which results in the loss of economy of scale. Even when integrating many commodity devices such as microprocessors and DRAM components, the cost of such systems may be tens of millions to over a hundred million dollars.

With the very high bandwidth, low latency internal networks with specialized functionality (e.g., combining networks) and high bandwidth storage area networks for attached secondary storage, there are few commercial user domains that can help offset the NRE costs of such major and optimized computing systems. It is unlikely that a business model can be constructed that would justify such systems being made available through cloud economics. Added to this are the same issues with virtualization versus performance optimization through hands-on performance tuning as described above. Therefore, it is unlikely that clouds will satisfy capability computing challenges for computational science in the foreseeable future.

The evolution of the cloud paradigm is an important maturing of the power of microelectronics, distributed computing, the Internet, and the rapidly expanding role of computing in all aspects of human enterprise and social context. The HPC and scientific computing community will benefit in tangential ways from the cloud environments as they evolve and where appropriate. However, challenges of virtualization and performance optimization, security and intellectual property protection, and unique requirements of scale and functionality, will result in certain critical aspects of the requirements of HPC falling outside the domain of cloud computing, relying instead on the strong foundation upon which HPC is well grounded.

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!

HPE Launches Apollo 6500 Gen10 System as Part of AI Solution Push

March 21, 2018

HPE today announced the latest rev of its HPE Apollo 6500 platform, Gen10, along with a spate of new AI-oriented offerings designed to help customers optimize and scale up their AI and deep learning usage. Like is Gen Read more…

By Tiffany Trader

IBM Touts OpenPOWER Ecosystem, Announces New Customers, Products for AI and Hyperscale

March 20, 2018

At SC17 in Denver four months ago, Ken King, GM, OpenPOWER, IBM Systems Group, told a somewhat jaundiced trio of journalists that 2018 would, finally, after several years of expectations, be the year OpenPOWER and IBM’ Read more…

By Doug Black

Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate scientists the ability to use machine learning to identify e Read more…

By Rob Farber

HPE Extreme Performance Solutions

Harness the Full Power of HPC Servers with an Effective Cooling Approach

High performance computing (HPC) innovation is rapidly transforming the way we operate – with an onslaught of cutting-edge technologies designed to optimize applications and workloads, increase productivity, and enable better business outcomes. Read more…

IBM Unveils New Cloud for Data Science and Engineering

March 19, 2018

Days ahead of its inaugural IBM Think mega-event, the multinational tech mainstay on Friday (March 16) unveiled a new cloud offering called Cloud Private Data that’s designed to help organizations utilize data science Read more…

By Alex Woodie

HPE Launches Apollo 6500 Gen10 System as Part of AI Solution Push

March 21, 2018

HPE today announced the latest rev of its HPE Apollo 6500 platform, Gen10, along with a spate of new AI-oriented offerings designed to help customers optimize a Read more…

By Tiffany Trader

IBM Touts OpenPOWER Ecosystem, Announces New Customers, Products for AI and Hyperscale

March 20, 2018

At SC17 in Denver four months ago, Ken King, GM, OpenPOWER, IBM Systems Group, told a somewhat jaundiced trio of journalists that 2018 would, finally, after sev Read more…

By Doug Black

Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate Read more…

By Rob Farber

How the Cloud Is Falling Short for HPC

March 15, 2018

The last couple of years have seen cloud computing gradually build some legitimacy within the HPC world, but still the HPC industry lies far behind enterprise I Read more…

By Chris Downing

Stephen Hawking, Legendary Scientist, Dies at 76

March 14, 2018

Stephen Hawking passed away at his home in Cambridge, England, in the early morning of March 14; he was 76. Born on January 8, 1942, Hawking was an English theo Read more…

By Tiffany Trader

Hyperion Tackles Elusive Quantum Computing Landscape

March 13, 2018

Quantum computing - exciting and off-putting all at once - is a kaleidoscope of technology and market questions whose shapes and positions are far from settled. Read more…

By John Russell

Part Two: Navigating Life Sciences Choppy HPC Waters in 2018

March 8, 2018

2017 was not necessarily the best year to build a large HPC system for life sciences say Ari Berman, VP and GM of consulting services, and Aaron Gardner, direct Read more…

By John Russell

Google Chases Quantum Supremacy with 72-Qubit Processor

March 7, 2018

Google pulled ahead of the pack this week in the race toward "quantum supremacy," with the introduction of a new 72-qubit quantum processor called Bristlecone. Read more…

By Tiffany Trader

Inventor Claims to Have Solved Floating Point Error Problem

January 17, 2018

"The decades-old floating point error problem has been solved," proclaims a press release from inventor Alan Jorgensen. The computer scientist has filed for and Read more…

By Tiffany Trader

Japan Unveils Quantum Neural Network

November 22, 2017

The U.S. and China are leading the race toward productive quantum computing, but it's early enough that ultimate leadership is still something of an open questi Read more…

By Tiffany Trader

Researchers Measure Impact of ‘Meltdown’ and ‘Spectre’ Patches on HPC Workloads

January 17, 2018

Computer scientists from the Center for Computational Research, State University of New York (SUNY), University at Buffalo have examined the effect of Meltdown Read more…

By Tiffany Trader

IBM Begins Power9 Rollout with Backing from DOE, Google

December 6, 2017

After over a year of buildup, IBM is unveiling its first Power9 system based on the same architecture as the Department of Energy CORAL supercomputers, Summit a Read more…

By Tiffany Trader

Fast Forward: Five HPC Predictions for 2018

December 21, 2017

What’s on your list of high (and low) lights for 2017? Volta 100’s arrival on the heels of the P100? Appearance, albeit late in the year, of IBM’s Power9? Read more…

By John Russell

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. 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

Chip Flaws ‘Meltdown’ and ‘Spectre’ Loom Large

January 4, 2018

The HPC and wider tech community have been abuzz this week over the discovery of critical design flaws that impact virtually all contemporary microprocessors. T Read more…

By Tiffany Trader

Leading Solution Providers

GlobalFoundries, Ayar Labs Team Up to Commercialize Optical I/O

December 4, 2017

GlobalFoundries (GF) and Ayar Labs, a startup focused on using light, instead of electricity, to transfer data between chips, today announced they've entered in Read more…

By Tiffany Trader

How Meltdown and Spectre Patches Will Affect HPC Workloads

January 10, 2018

There have been claims that the fixes for the Meltdown and Spectre security vulnerabilities, named the KPTI (aka KAISER) patches, are going to affect applicatio Read more…

By Rosemary Francis

Perspective: What Really Happened at SC17?

November 22, 2017

SC is over. Now comes the myriad of follow-ups. Inboxes are filled with templated emails from vendors and other exhibitors hoping to win a place in the post-SC thinking of booth visitors. Attendees of tutorials, workshops and other technical sessions will be inundated with requests for feedback. Read more…

By Andrew Jones

V100 Good but not Great on Select Deep Learning Aps, Says Xcelerit

November 27, 2017

Wringing optimum performance from hardware to accelerate deep learning applications is a challenge that often depends on the specific application in use. A benc Read more…

By John Russell

Lenovo Unveils Warm Water Cooled ThinkSystem SD650 in Rampup to LRZ Install

February 22, 2018

This week Lenovo took the wraps off the ThinkSystem SD650 high-density server with third-generation direct water cooling technology developed in tandem with par Read more…

By Tiffany Trader

AMD Wins Another: Baidu to Deploy EPYC on Single Socket Servers

December 13, 2017

When AMD introduced its EPYC chip line in June, the company said a portion of the line was specifically designed to re-invigorate a single socket segment in wha Read more…

By John Russell

World Record: Quantum Computer with 46 Qubits Simulated

December 18, 2017

Scientists from the Jülich Supercomputing Centre have set a new world record. Together with researchers from Wuhan University and the University of Groningen, Read more…

New Blueprint for Converging HPC, Big Data

January 18, 2018

After five annual workshops on Big Data and Extreme-Scale Computing (BDEC), a group of international HPC heavyweights including Jack Dongarra (University of Te Read more…

By John Russell

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