Many algorithms have been proposed to efficiently schedule parallel jobs on a multicore and/or multiprocessor machine to minimize average flow time, and the complexity of the problem is well understood. In practice, the problem is far from being understood. A reason for the gap between theory and practice is that all theoretical algorithms have prohibitive overheads in actual implementation including using many preemptions. One of the flagship successes of scheduling theory is the work-stealing scheduler. Work-stealing is used for optimizing the flow time of a single parallel job executing on a single machine with multiple cores and has a strong performance in theory and in practice. Consequently, it is implemented in almost all parallel runtime systems. This paper seeks to bridge theory and practice for scheduling parallel jobs that arrive online, by introducing an adaptation of the work-stealing scheduler for average flow time. The new algorithm Distributed Random Equi-Partition (DREP) has strong practical and theoretical performance. Practically, the algorithm has the following advantages: (1) it is non-clairvoyant; (2) all processors make scheduling decisions in a decentralized manner requiring minimal synchronization and communications; and (3) it requires a small and bounded number of preemptions. Theoretically, we prove that DREP is (4 + ϵ)-speed O(ϵ 13 )- competitive for average flow time. We have empirically evaluated DREP using both simulations and actual implementation by modifying the Cilk Plus work-stealing runtime system. The evaluation results show that DREP performs well compared to other scheduling strategies, including those that are theoretically good but cannot be faithfully implemented in practice.