that they are hampered by the difficulty of developing new HPC software. The programming languages, libraries, and application development environments used in HPC are generally less advanced than those used by the broad software industry, even though the problems are much harder. A software engineering discipline geared to the unique needs of technical computing and high-performance computing is yet to emerge. In addition, a common software environment for scientific computation encompassing desktop to high-end systems will enhance productivity gains by promoting ease of use and manageability of systems.

Extrapolating current trends in supercomputer software, it is hard to see whether there will be any major changes in the software stack used for supercomputers in the coming years. Languages such as UPC, CAF, and Titanium are likely to be increasingly used. However, UPC and CAF do not support object orientation well, and all three languages have a static view of parallelism (the crystalline model) and give good support to only some application paradigms. The DARPA HPCS effort emphasizes software productivity, but it is vendor driven and hardware focused and has not generated a broad, coordinated community effort for new programming models. Meanwhile, larger and more complex hardware systems continue to be put in production, and larger and more complex application packages are developed. In short, there is an oncoming crisis in HPC software created by barely adequate current capabilities, increasing requirements, and limited investment in solutions.

In addition to the need for software research, there is a need for software development. Enhanced mechanisms are needed to turn prototype tools into well-developed tools with a broad user base. The core set of tools available on supercomputers—operating systems, compilers, debuggers, performance analysis tools—is not up to the standards of robustness and performance expected for commercial computers. Tools are nonexistent or, even worse, do not work. Parallel debuggers are an oftencited example. Parallel math libraries are thought to be almost as bad, although math libraries are essential for building a mature application software base for parallel computing. Third-party commercial and public domain sources have tried to fill the gaps left by the computer vendors but have had varying levels of success. Many active research projects are also producing potentially useful tools, but the tools are available only in prototype form or are fragmented and buried inside various application efforts. The supercomputer user community desperately needs better means to develop these technologies into effective tools.

Although the foregoing discussion addresses the need for technical innovation and the technical barriers to progress, there are significant policy issues that are essential to achieving that progress. These topics are taken up in subsequent chapters.



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