The minimum spanning tree problem has influential importance in computer science, networkanalysis, and engineering. However, the sequential algorithms become unable to process the givenproblem as the volume of the data representing graph instances overgrowing. Instead, the highperformance computational resources pursue to simulate large-scale graph instances in a distributedmanner. Generally, the standard shared or distributed memory models like OpenMP and MessagePassing Interface are applied to address the parallelization. Nevertheless, as an emerging alternative,the Partitioned Global Address Space model communicates in the form of asynchronous remoteprocedure calls to access distributed-shared memory, positively affecting the performance usingoverlapping communications and locality-aware structures. The paper presents a modification of theKruskal algorithm for MST problems based on performance and energy-efficiency evaluation relyingon emerging technologies. The algorithm evaluation shows great scalability within the server up to 16vCPU and between the physical servers coupled with a connected weighted graph using differentvertices, edges, and densities.