Today's hardware is becoming more and more parallel. While embarrassingly parallel codes, such as high-performance computing ones, can readily take advantage of this increased number of cores, most other types of code cannot easily scale using traditional data and/or task parallelism and cores are therefore left idling resulting in lost opportunities to improve performance. The opportunistic computing paradigm, on which this thesis rests, is the idea that computations should dynamically adapt to and exploit the opportunities that arise due to idling resources to enhance their performance or quality. In this thesis, I propose to utilize algorithmic properties to develop programming models that leverage this idea thereby providing models that increase and improve the parallelism that can be exploited. I exploit three distinct algorithmic properties: i) algorithmic diversity, ii) the semantic content of data-structures, and iii) the variable nature of results in certain applications. This thesis presents three main contributions: i) the N-way model which leverages algorithmic diversity to speed up hitherto sequential code, ii) an extension to the N-way model which opportunistically improves the quality of computations and iii) a framework allowing the programmer to specify the semantics of data-structures to improve the performance of optimistic parallelism.