Visit additional Tabor Communication Publications
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:
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.
Jun 17, 2013 |
The advent of low-power mobile processors and cloud delivery models is changing the economics of computing. But just as an economy car is good at different things than a full size truck, an HPC workload still has certain computing demands that neither the fastest smartphone nor the most elastic cloud cluster can fulfill.
Jun 14, 2013 |
For all the progress we've made in IT over the last 50 years, there's one area of life that has steadfastly eluded the grasp of computers: understanding human language. Now, researchers at the Texas Advanced Computing Center (TACC) are utilizing a Hadoop cluster on its Longhorn supercomputer to move the state of the art of language processing a little bit further.
Jun 13, 2013 |
Titan, the Cray XK7 at the Oak Ridge National Lab that debuted last fall as the fastest supercomputer in the world with 17.59 petaflops of sustained computing power, will rely on its previous LINPACK test for the upcoming edition of the Top 500 list.
Jun 12, 2013 |
At 31 petaflops of sustained LINPACK capacity, the new Chinese Tianhe-2 supercomputer will be the fastest supercomputer in the world when this month's Top 500 list comes out, as we reported previously in HPCwire.
Jun 12, 2013 |
HPC system makers are lining up to announce compatibility with the new fourth generation Intel Core processor, codenamed "Haswell." The new Iris GPUs based on the Haswell architecture are giving Intel new credibility in the graphics processing department.
05/10/2013 | Cleversafe, Cray, DDN, NetApp, & Panasas | From Wall Street to Hollywood, drug discovery to homeland security, companies and organizations of all sizes and stripes are coming face to face with the challenges – and opportunities – afforded by Big Data. Before anyone can utilize these extraordinary data repositories, however, they must first harness and manage their data stores, and do so utilizing technologies that underscore affordability, security, and scalability.
04/15/2013 | Bull | “50% of HPC users say their largest jobs scale to 120 cores or less.” How about yours? Are your codes ready to take advantage of today’s and tomorrow’s ultra-parallel HPC systems? Download this White Paper by Analysts Intersect360 Research to see what Bull and Intel’s Center for Excellence in Parallel Programming can do for your codes.
Join HPCwire Editor Nicole Hemsoth and Dr. David Bader from Georgia Tech as they take center stage on opening night at Atlanta's first Big Data Kick Off Week, filmed in front of a live audience. Nicole and David look at the evolution of HPC, today's big data challenges, discuss real world solutions, and reveal their predictions. Exactly what does the future holds for HPC?
Join our webinar to learn how IT managers can migrate to a more resilient, flexible and scalable solution that grows with the data center. Mellanox VMS is future-proof, efficient and brings significant CAPEX and OPEX savings. The VMS is available today.