Increasingly complex consumer electronics applications call for embedded processors with higher performance. Multi-cores are capable of delivering the required performance. However, many of these embedded applications must meet some form of soft real-time constraints, and program behavior on multi-cores is even harder to predict than on single-cores. In this article, we highlight the greater performance variability of irregular applications (non-regular control flow and/or data structures) across data sets when parallelized and run on a multi-core. We then show that a proper parallelization approach coupled with a lightweight run-time system can drastically reduce this performance variability without sacrificing their performance. This approach requires no complex program or architecture analysis or modeling. Moreover, we show that parallel program performance becomes stable enough that it is possible to reasonably and accurately predict it by sampling a few training runs.