Biomedical data is known for its large size, which presents significant computational and storage challenges for its processing. Accelerating biomedical applications will be of height interest to the research community. This paper proposes a novel approach for biomedical syntactic resolution based on OpenCL, an open and versatile parallel computing framework, to accelerate the computational aspects of syntactic resolution in the medical domain. OpenCL's ability to leverage heterogeneous hardware architectures, including CPUs, GPUs, and FPGAs, provides flexibility and cost-saving opportunities. Within biomedical research, Syntactic resolution serves as a subtask in Named Entity Disambiguation (NED), an essential process for mapping ambiguous or variant mentions of biomedical entities to standardized concepts. One of the critical challenges in named entity normalization is achieving proper and fast syntactic resolution in large biomedical vocabulary. Our approach aims to improve the precision and relevance of search results in the context of biomedical named entity disambiguation. Through experimental evaluations, we demonstrate the effectiveness of our biomedical syntactic resolution approach in disambiguating biomedical terms with the application of OpenCL ensuring rapid execution. The results highlight the potential of OpenCL as a valuable tool for enhancing the performance and efficiency of biomedical NLP tasks, opening up new possibilities for accelerating other tasks in the biomedical domain.