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Getting Up to Speed: The Future of Supercomputing (2004)
Computer Science and Telecommunications Board (CSTB)

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. "6 Supercomputing Infrastructures and Institutions." Getting Up to Speed: The Future of Supercomputing. Washington, DC: The National Academies Press, 2004.

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Getting up to Speed the Future of Supercomputing
  • Vector architectures, vectorizing compilers, and applications tuned for the use of vector hardware;

  • Shared memory architectures, scalable operating systems, OpenMP-compliant compilers and run-time systems, and applications that can take advantage of shared memory; and

  • Message passing architectures, parallel software tools and libraries, and applications that are designed to use this programming model.

The organism space tends to group according to the architecture class, the programming language and models used on the system, the algorithms, the set of applications and how the code is being tuned (e.g., vector version versus cache version), what application program packages are available, and so on. The success of supercomputer architectures is highly dependent on the organisms that form around them.

The architecture and the balance among its key configuration parameters (such as number of processors or memory size) are the dominant factors in determining the nature of the technologies in the ecosystem. For example, early supercomputers such as the CDC 7600 had rather small memories compared with their computing speed and the requirements of the applications that users wanted to run. That characteristic led to the development of system tools to reuse memory during execution (overlay management software) and to the use of different algorithms in certain cases, and it excluded certain classes of applications, together with a part of the user community. A second example is the speed of I/O to local disk, which can have a major impact on the design of application programs. For example, in a number of chemistry applications, if the ratio of I/O speed to computation performance is below a certain (well-understood) level, the application will run faster by recomputing certain quantities instead of computing them once, writing them to disk, and then reading them in subsequent phases of the job. Some widely used chemistry programs use this recompute strategy. Another common example of the impact of system performance characteristics on programming is that a message passing programming style is most often used when shared memory performance is below some threshold, even if shared memory programming tools are provided.

Because all current supercomputers have highly parallel architectures, the system software and the algorithms used have to be designed or adapted to function on such machines. As discussed in Chapter 5, the characteristics of the processors and the interconnection network (latency and bandwidth of access to memories, local and remote) are key features and determine to a large extent the algorithms and classes of applications that will execute efficiently on a given machine. Low latency and high-bandwidth access to memory not only yield higher performance—much

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