Therefore, since irregular programs represent a large and important fraction of applications, we advocate the development of an automatable framework for run-time parallelization. Since fully parallel loops arise frequently in practice and their parallelism can be exploited in the most efficient and scalable manner, as a component of this framework we propose a novel strategy for their identification: speculatively execute the loop as a doall, and then apply a fully parallel data dependence test to determine if it had any cross-iteration dependences; if the test fails, then the loop is re-executed serially. To increase the exploitable parallelism in the loop, our methods can speculatively apply the privatization and reduction parallelization transformations and check their validity at run-time. We present experimental results on loops from the PERFECT Benchmarks which show that these techniques can indeed yield significant speedups.