Associative Graph Data Structures (AGDS) enable efficient data inference by directly encoding complex relationships, achieving access times that scale logarithmically or even remain constant. However, the inherently parallel nature of AGDS is undermined when implemented on sequential processors, particularly during associative inference operations. This paper introduces the Associative Graph Data Neural Network (AGDNN), a parallelized extension of AGDS. It explores multiple parallelization strategies, including GPU acceleration, the MPI programming model, and Erlang-based concurrency. We select Erlang as the most effective approach due to its lightweight process model and efficient message passing. A highly concurrent Erlang-based algorithm for associative inference is proposed, addressing key challenges such as efficiently determining inference termination. The reference implementation demonstrates near-linear speedup across multiple cores. Experimental results confirm that AGDNN significantly enhances inference efficiency, paving the way for scalable, high-performance associative reasoning in graph-based neural models.