Deduplication has become one of the hottest topics in the field of data storage. Quite a few methods towards reducing disk I/O caused by deduplication have been proposed. Some methods also have been studied to accelerate computational sub-tasks in deduplication. However, the order of computational sub-tasks can affect overall deduplication throughput significantly, because computational sub-tasks exhibit quite different workload and concurrency in different orders and with different data sets. This paper proposes an adaptive pipelining model for the computational sub-tasks in deduplication. It takes both data type and hardware platform into account. Taking the compression ratio and the duplicate ratio of the data stream, and the compression speed and the fingerprinting speed on different processing units as parameters, it determines the optimal order of the pipeline stages (computational sub-tasks) and assigns each stage to the processing unit which processes it fastest. That is, “adaptive” refers to both data adaptive and hardware adaptive. Experimental results show that the adaptive pipeline improves the deduplication throughput up to 50% compared with the plain fixed pipeline, which implies that it is suitable for simultaneous deduplication of various data types on modern heterogeneous multi-core systems.