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!

TACC Helps ROSIE Bioscience Gateway Expand its Impact

April 26, 2017

Biomolecule structure prediction has long been challenging not least because the relevant software and workflows often require high-end HPC systems that many bioscience researchers lack easy access to. Read more…

By John Russell

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

IBM, Nvidia, Stone Ridge Claim Gas & Oil Simulation Record

April 25, 2017

IBM, Nvidia, and Stone Ridge Technology today reported setting the performance record for a “billion cell” oil and gas reservoir simulation. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Remote Visualization Optimizing Life Sciences Operations and Care Delivery

As patients continually demand a better quality of care and increasingly complex workloads challenge healthcare organizations to innovate, investing in the right technologies is key to ensuring growth and success. Read more…

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 a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

Musk’s Latest Startup Eyes Brain-Computer Links

April 21, 2017

Elon Musk, the auto and space entrepreneur and severe critic of artificial intelligence, is forming a new venture that reportedly will seek to develop an interface between the human brain and computers. Read more…

By George Leopold

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

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

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 a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Hyperion (IDC) Paints a Bullish Picture of HPC Future

April 20, 2017

Hyperion Research – formerly IDC’s HPC group – yesterday painted a fascinating and complicated portrait of the HPC community’s health and prospects at the HPC User Forum held in Albuquerque, NM. HPC sales are up and growing ($22 billion, all HPC segments, 2016). Read more…

By John Russell

Knights Landing Processor with Omni-Path Makes Cloud Debut

April 18, 2017

HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" processors. The infrastructure is based on the 68-core Intel Knights Landing processor with integrated Omni-Path fabric (the 7250F Xeon Phi). Read more…

By Tiffany Trader

CERN openlab Explores New CPU/FPGA Processing Solutions

April 14, 2017

Through a CERN openlab project known as the ‘High-Throughput Computing Collaboration,’ researchers are investigating the use of various Intel technologies in data filtering and data acquisition systems. Read more…

By Linda Barney

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of “quantum supremacy,” researchers are stretching the limits of today’s most advanced supercomputers. Read more…

By Tiffany Trader

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 phase of neural networks (NN). 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. Read more…

By John Russell

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 campaign. 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 assets. 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

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

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

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

Leading Solution Providers

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

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

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

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

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

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

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

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

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