Computing workload distribution is indispensable for resource sharing, cycle stealing and other modes of interaction in distributed systems/Grids. Computations should be arranged to adapt the capacity variation of system resources. Although computation migration is the essential mechanism to move computing tasks around, the decision making of which task should be relocated is even more critical, especially when multithreaded parallel programs are involved. Multiple threads might be treated as partial workload and moved together. Based on thread similarity, this paper proposes a novel Multiresolution Thread Grouping algorithm (MTG) to classify threads into hierarchical Thread Bundles (TB) some of which can be picked by Multiresolution Thread Selection scheme (MTS) for load distribution. During the process of MTG, global variables are reorded so that one-time migration cost and post-migration communication volume and frequency can be reduced. Experimental results demonstrate the effectiveness of MTS for parallel workload distribution.