Abstract: As an irregular MPI (Message Passing Interface) collective communication, Gatherv provides great flexibility for the description of parallel communication behavior, but its irregularity brings high implementation difficulties. Existing methods have some problems, such as outstanding communication hotspots, high memory overhead, low memory access efficiency, etc., which are difficult to satisfy the performance requirements of todays large-scale parallel applications. A Gatherv optimization method for large scale concurrency is proposed. Starting from the optimization level, buffer management and other key issues, the binomial tree model commonly used in the implementation of regular collective communication is applied to the implementation of Gatherv. Besides, a message chain scheduling is proposed to further reduce the overhead and improve the optimization effect. Test data shows that the proposed method can effectively solve the performance problems of the existing methods, and achieve efficient scalability of Gatherv performance under the condition of large-scale concurrency.

Key words: message passing interface (MPI), irregular collectives, Gatherv, Binomial-Tree, message chain scheduling