This paper presents a hybrid parallel task-placement strategy that combines work stealing and work dealing to improve workload distribution across nodes in distributed shared-memory machines. Existing work-dealing-based load balancers suffer from large performance penalties resulting from excessive task migration and from excessive communication among the nodes to determine the target node for a migrated task. This work employs a simple heuristic to determine the load status of a node and also to detect a good target for migration of tasks. Experimental evaluations on applications chosen from the Cowichan and Lonestar suites demonstrate a speedup, with the proposed approach, in the range of 2% to 16% on a cluster of 128 cores over the state-of-the-art work-stealing scheduler. Copyright © 2013 ACM.