With the aim to address the increasing difficulty of efficiently using large number of cores in many-core processors, a core-partitioned adaptive scheduling algorithm, named CASM (core-partitioned adaptive scheduling for many-core systems), is proposed. CASM dynamically aggregates cores into different partitions by splitting or merging task-clusters, which ensures the efficiency of isolated accessing in these core partitions. To improve the scheduling efficiency of CASM, equi-partitioning scheduling algorithm is adopted to reallocate the cores among task-clusters, and the feedback-driven adaptive scheduling algorithm is implemented within the task-clusters. Online competitive analysis shows that CASM achieves 2-competitiveness ratio with respect to the execution time of parallel jobs, which indicates that CASM has better performance and scalability. The experimental results demonstrate that compared with WS (work-stealing), AGDEQ (adaptive greedy dynamic equi-partitioning) and EQUI?EQUI, CASM reduces the execution time of the same workload by nearly 46%, 32% and 15% respectively. Under the same power consumption, CASM greatly enhances the system throughput.