Since frequent communication between the applications took place in high speed networks, deep packet inspection (DPI) plays an important role to the network application awareness. The signature-based network intrusion detection system (NIDS) contains the DPI technique that examines the incoming packet payloads by employing the pattern matching algorithm, which dominates the overall inspection performance. Existing studies focused on implementing efficient pattern matching algorithms by parallel programming on software platform because of the advantages of lower cost and higher scalability. Either the central processing unit (CPU) or the graphic processing unit (GPU) was involved. Our studies focused on designing a pattern matching algorithm based on the cooperation between both CPU and GPU. In this paper, we present an enhanced design for our previous work and introduce this novel method, a length-bounded hybrid CPU/GPU pattern matching algorithm (LHPMA). In the preliminary experiment, the performance and comparison with the previous work are displayed, and the results show that the LHPMA achieves higher throughput than other tested algorithms.