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In recent years, the number of hardware supported threads in desktop processors has increased dramatically. All but the very lowest cost netbooks and embedded processors now have at least dual cores and soon systems supporting upwards of 8 to 16 hardware threads are likely to be commonplace. Unfortunately, it will be difficult to take full advantage of the parallelism emerging processors will be able to provide. To help address this issue, we are investigating mechanisms to pre-compute function results in separate threads running concurrently with the main program thread. The concurrent threads are forked automatically and without program modification. A critical component for the success of this idea is an ability to build a background thread that can pre-compute usable results in some effective manner. For some support functions (dynamic memory) exact arguments predictions for the function pre-computation are not necessary, for others (trigonometric functions) they are. In work with dynamic memory, we are able to pre-compute memory blocks and show modest speedup: saving approximately 25% of the dynamic memory costs. In studies with predicting argument values to trigonometric functions, we show that learning algorithms are able to successfully predict the next argument values approximately 44% of the time.