This paper provides an overview of research developments toward nanometer-scale electronic switching devices for use in building ultra-densely integrated electronic computers. Specifically, two classes of alternatives to the field-effect transistor are considered: 1) quantum-effect and single-electron solid-state devices and 2) molecular electronic devices. A taxonomy of devices in each class is provided, operational principles are described and compared for the various types of devices, and the literature about each is surveyed. This information is presented in nonmathematical terms intended for a general, technically interested readership

ormance delivered to applications follows from a combination of advances, each making the system more scalable [5]: . The microprocessor building block became much faster (especially for floating-point operations) through greater internal parallelism, use of huge (1--4MB) multilevel caches, and faster clock speeds. . Schemes were devised for effectively connecting larger numbers of processors and memories. . Computational researchers learned to use large numbers of processors and deep memory hierarchies more effectively. . System software and tools improved through experience and the leveraging of mainstream advances. Systems with hundreds of processors were built in the 1980s using commercial or custom microprocessors, but these processors were much slower than the fastest available at the time (in vector supercomputers) . Since then, microprocessor performance has advanced at a tremendous rate, so today the processor module that can

Parallel computing is maturing to become a viable approach for increasing CFD mesh resolution and reducing turnaround times -- many of todays commercial programs are already available for parallel architecture machines, and software tools exist to help with automated parallelization of irregular (unstructured) scientific problems. However, despite numerous impressive demonstrations of applications, it is still not exactly clear when and how these will really impact on the way we do turbomachinery CFD. The current paper does not even attempt to answer such a general question, but instead presents the authors experience and views obtained while developing parallel codes for both industry and in conjunction with their own research work. We give a brief review of events leading up to the present situation, and explain why parallel processing is the accepted route to follow. We then describe our parallel unstructured code; discuss practical issues relating to parallel performance; present some sample test case calculations; and nally consider a possible scenario regarding the role and requirements for similar computations in the future.