Packet classification is a core function in network and security systems; hence, hardware-based solutions, suchas packet classification accelerator chips or T-CAM (Ternary Content Addressable Memory), have been widely adopted forhigh-performance systems. With the rapid improvement of general hardware architectures and growing popularity of multi-coremulti-threaded processors, software-based packet classification algorithms are attracting considerable attention, owing to theirhigh flexibility in satisfying various industrial requirements for security and network systems. For high classification speed, thesealgorithms internally use large tables, whose size increases exponentially with the ruleset size; consequently, they cannot beused with a large rulesets. To overcome this problem, we propose a new software-based packet classification algorithm thatsimultaneously supports high scalability and fast classification performance by merging partition decision trees in a search table.While most partitioning-based packet classification algorithms show good scalability at the cost of low classification speed, ouralgorithm shows very high classification speed, irrespective of the number of rules, with small tables and short table buildingtime. Our test results confirm that the proposed algorithm enables network and security systems to support heavy traffic in themost effective manner.