Dataflow analysis, a form of static program analysis, can be used by compilers to gather information about a program at each point in its execution. The collected information enables program optimizations such as eliminating unnecessary work and detecting bugs. This work aims to develop both a sequential dataflow analysis implementation and a parallel version, followed by an experiment to gather and analyze performance metrics. These metrics include the speed-up achieved by the parallel implementation compared to the sequential one, as well as the parallel efficiency.The research question addressed in this thesis explores the performance of a parallel implementation of a dataflow analysis algorithm in terms of execution speed compared to its sequential counterpart. Dataflow analysis can be time-consuming, making it beneficial to leverage parallelism for faster analysis of large codebases.In this study, we implement a sequential dataflow algorithm and adapt it to utilize parallelism. An experiment is conducted with the sequential implementation as the control group and the parallel version as the experimental group. Several benchmark C programs are developed to be analyzed by both implementations. These benchmark programs are distinguished by the number of instructions in each of the functions. The benchmark programs are then analyzed using different configurations of processor cores. The experiment results demonstrate achievable speed-up in the parallel implementation compared to the sequential one. The parallel version outperforms the sequential algorithm, particularly when analyzing benchmark programs with functions containing a high instruction count. While the results are not ideal, they provide insights into the potential of parallel implementations in dataflow analysis. The results can serve as a basis for comparison when developing alternative strategies for parallelizing dataflow analysis algorithms.