High-Performance Computing (HPC) is the act of using many concurrently running computational units to solve complex and resource intensive calculative tasks faster than singular Computer. These Tasks include the fourier Transformation, Computational Fluid Dynamics and many more. HPC uses HPC-Clusters, an accumulation of many Computing Nodes, to independently calculate different parts of these tasks and then gathering the results. For these tasks this improves performance immensely. A critical component of many HPC frameworks is the Message Passing Interface (MPI), which provides standardized methods for inter-process communication across distributed systems. The first communcation mechanism of the MPI Standard is the point-to-point Communication, which allows for singular processes to send data to each other. On a higher level, and at the center of our thesis, are the collective communication operations, which transmit data in a group of processes. The MPI Standard and its communication models are implemented by several libraries. Our thesis focuses on OpenMPI, a widely used open-source implementation of MPI, that continuously optimizes performance and scalability, making it the de facto choice for collective communication operations in large-scale operations. A Library with Focus on Parallelism and Concurrency is the STEllAR Group’s HPX for C++. It seeks to address limitations of MPI and other traditional parallelization methods by leveraging techniques such as message-driven computation and constraint-based synchronization. While OpenMPI has introduced numerous optimizations to improve scalability, HPX’s current collective operations still rely on a single root node for message distribution, leading to performance bottlenecks. This thesis proposes a hierarchical communicator for HPX that distributes communication workload across multiple processes, reducing overhead and improving scalability. We created a benchmark to compare our hierarchical communicator to the naive HPX communicator and various OpenMPI Algorithms. Our results show a proportional performance increase across all tested collective operations on HPX, with the hierarchical communicator achieving a speedup of up to 32 for reduce and broadcast operations and 16 for gather and scatter on 256 processes. Compared to OpenMPI’s state-of-the-art implementations for gather, scatter, and broadcast our approach shows a speedup of 1.2 and 1.5 for 128 and 256 processes respectively.