Abstract: Scalability is an issue that needs to be considered when designing any reasoner for dealing with large and complex ontologies and large data sets. The practical usage of parallel computation techniques in reasoning is an important premise for the adoption of dynamic description logics (DDL) in a real-world setting. In this paper we describe two possible approaches for applying parallel computation techniques to DDL reasoning. The first approach is to design a logical framework of distributed dynamic description logics (D3L), which is composed of a set of stand-alone DDLs pairwise interrelated with each other via collection of bridge rules. We present a tableau-based distributed reasoning procedure for providing the capability of global reasoning in D3L and decomposing large reasoning tasks to sub-tasks that could be concurrently processed by different reasoning agents. The second approach is to parallelize the nondeterministic branches within the DDL tableau procedure. The parallel computation of nondeterministic branches also makes it possible that the reasoning task is executed simultaneously on several independent machines. Finally, we introduce a prototype inference engine and present its evaluation. The results indicate that the proposed approaches achieve promising performance results.