HPCwire: HPC Clouds -- Alto Cirrus or Cumulonimbus

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November 21, 2008

HPC Clouds -- Alto Cirrus or Cumulonimbus

by Thomas Sterling and Dylan Stark, Louisiana State University

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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:

Highest possible delivered capability performance (strong scaling).
Weak scaling single applications.
Capacity, or throughput job-stream, computing.
Management of massive data sets, possibly geographically distributed.
Analysis and visualization of data sets.
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.

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