Adaptive algorithms are an important technique to achieve portable high performance. They choose among solution methods and optimizations according to expected performance on a particular machine. Grid environments make the adaptation problem harder, because the optimal decision may change across runs and even during runtime. Therefore, the performance model used by an adaptive algorithm must be able to change decisions without high overhead. In this paper, we present work that is modifying previous research into rapid performance modeling to support adaptive grid applications through sampling and high granularity modeling. We also outline preliminary results that show the ability to predict differences in performance among algorithms in the same program.
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We present a purely functional implementation of the computational differentiation tools — the well known numeric (i.e., not symbolic) techniques which permit one to compute point-wise derivatives of functions defined by computer programs economically and exactly (with machine precision). We show how the use of lazy evaluation permits a transparent and elegant construction of the entire infinite tower of derivatives of higher order for any expressions present in the program. The formalism may be useful in various problems of scientific computing which often demand a hard and ungracious human preprocessing before writing the final code. Some concrete examples are given.
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In general, developing fast applications for Intel Architecture (IA) processors is not difficult. An understanding of the architecture and good development practices make the difference between a fast application and one that runs significantly slower than its full potential. Of course, applications developed for the 8086/8088, 80286, Intel386™ (DX or SX), and Intel486™ processors will execute on the Pentium ®, Pentium Pro and Pentium II processors without any modification or recompilation. However, the following code optimization techniques and architectural information will help you tune your application to its greatest potential.
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