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September 28, 2007
Avoiding Application Porting Pitfalls
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Historically, compute clusters have emerged as a less expensive, more practical option for harnessing high performance computing power using systems that were already available in-house. In highly technical settings, such as academia and national laboratories, many researchers and IT managers could not afford to purchase supercomputers so they networked systems together to creatively solve complex computational problems.
The original Beowulf cluster built at NASA in the late 1990s is the epitome of this paradigm shift. Over the last decade, with the evolution of programming standards, refinements in packaging, and improvements in interconnect technology, compute clusters are becoming increasingly attractive to commercial companies. Commercial organizations are choosing cluster environments, not just for financial reasons, but also for their computational scalability. When companies need more power and reach, they simply add another server to their cluster. As a result, clusters have become more appealing to certain high-growth commercial sectors, such as financial services, as a viable alternative for high performance computing.
While there are many companies interested in taking advantage of clusters, many have not yet made the leap. One of the primary reasons for not moving to a cluster environment is that many of these organizations have legacy applications that run well on a traditional server. The cost to migrate the application to a cluster is too high. To further complicate the situation, the application may be a mission-critical asset and to attempt to migrate it to a new system is considered too risky.
Many programs that continue to run on VMS-based platforms fall into this category. The system is reliable and the application executes properly. So despite being considered by many as outdated technology, the organization relying on the VMS-based program would have no short-term migration plan.
However, there are factors driving change. From a performance perspective, compute clusters are becoming more powerful, so organizations are sacrificing performance by staying with outdated platforms. From a personnel perspective, keeping applications on older platforms is becoming riskier, as there are fewer and fewer trained experts in these older technology areas.
When applications are initially developed, there are steps that can be taken to make a future migration less painful and risky. For existing applications that require migration or porting, such as ones being moved to a cluster environment, there are a number of potential porting issues to address along the way. The following provides a brief overview of those issues and how companies can avoid them before they get started.
Preserving Computational Accuracy While Porting Proprietary Applications
While using standard software solutions in a compute cluster helps companies ensure application compatibility with minimum conversion issues, the truth is that many companies have a number of custom, proprietary applications that need to be ported. When porting proprietary applications, the real challenge is to ensure that computational accuracy stays intact when the process is complete.
One of the most reliable sources of computational integrity is commercial numerical libraries. Commercial libraries utilize the numerical representation of the architecture for computational consistency. For example, the convergence criteria for a nonlinear least squares optimization algorithm may be based on a system-specific parameter, such as the largest relative floating point spacing versus a hard-coded value. The hard-coded value may work fine on the original development system, but when porting that algorithm to a system with a different floating point representation, there is a high likelihood that the algorithm will not perform as expected. Relying on the commercial version of the algorithm will avoid these potential problems and significantly reduce the amount of debugging time needed when porting applications to a new environment.
If proprietary applications are already developed, companies can retrofit them with commercial libraries before porting to a new platform. If the proprietary application is "home-grown," an organization may consider substituting algorithms from a commercial library for algorithms that were developed in-house or obtained as open source. There are a variety of reasons why the home-grown application may not perform reliably on a new platform. The algorithm from a commercial library is designed to execute consistently across all supported platforms.
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