In a given organisation or society, energy dependence increases rapidly due to shipping, transportation, and even food production. This dependency generally results in high costs and pollution, which threatens to disrupt our ecosystem. At this time, the earth is rapidly approaching a point of no return that requires international cooperation. In the following decade, reliance on effective energy-computing designs will become imperative, as power and cooling begin to cause restrictions on microchip clock speeds. Therefore, personal computer organisations are fast expanding on-chip parallelism to expedite execution. Combined operations are the most fundamental part of a transmission in shared and distributed parallel applications. In this paper, the tradeoffs between energy, memory, and runtime of various algorithms that accomplish such operations are investigated. The influence of aggregate transmissions on the execution of parallel calculation, specifically, is shown. A nondirect association with execution seen in the energy required is also presented. The best streamlining between runtime, energy, and memory tradeoff is demonstrated and a better result in the energyeffective execution of calculations is displayed. Copyright © 2018 Inderscience Enterprises Ltd.