GPUs Impact on Parallel Shared Memory Systems Performance

In recent years the graphic processing units (GPUs) programmability has increased and this lead to use in several areas. GPUs can tackle enormous data parallel issues at a higher speed than the conventional CPU. Moreover, GPUs considered more affordable and energy-efficient than distributed systems. This paper gives a comprehensive review of the several published studies in the area of the parallel-shared memory based on the GPUs in the last few years. In addition, we represent the necessary computation time and speedup gains provided by different parallel-shared memory strategies. Moreover, in this article, a clear view and a detailed summary of such used algorithms/methods, hardware, and the results obtained of various parallel-shared memory using GPU approaches present in the literature. The maximum speedup as a performance is the main target of such researches where correlation algorithm and techniques were developed in experimental studies are reported.