A methodology for automated lookup table optimization of scientific applications
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Tuning the performance of scientific codes is challenging because of their math-intensive nature. Applications such as climate modeling and molecular biology simulate the behavior of natural systems based on scientific equations. Translating these equations into code can often yield expressions that are expensive to evaluate. Trigonometric, logarithmic, and exponential elementary functions are especially problematic because of their high cost relative to ordinary arithmetic. Lookup table (LUT) transformation can expedite function evaluation by precomputing and storing function results, thereby ...