Many-Task Computing (MTC) is a widely used computing paradigm for large-scale task-parallel processing. One of the key issues in MTC is to schedule a large number of independent tasks onto heterogeneous resources. Traditional task-level scheduling heuristics, like Min-Min, Sufferage and MaxStd, cannot readily be applied in this scenario. As most of MTC tasks are usually fine-grained, the resource management overhead would be prominent and the multi-core nodes might become hard to be fully utilized. In this paper we propose an application-level scheduling with task bundling approach that utilizes the knowledge of both applications and tasks to overcome these difficulties. Furthermore we adapt the traditional task-level heuristics to our model for MTC scheduling. Experimental results show that these application-level scheduling approaches, when equipped with task bundling, can deliver good performance for Many-Task Computing in terms of both Makespan and Flowtime.