Enhancing parallelism of distributed algorithms with the actor model and a smart data movement technique

Abstract

ABSTRACT The centralised orchestration technique is often a bad idea for massive parallelism applications if we want to achieve a scalable solution. In this paper for this purpose, the choreography approach is undertaken and some adaptive methods and software tools of distributed implementation are proposed to enhance computation parallelism applied to the optimisation of a class of block-recursive algorithms. A new formal model of distribution and coordination of the tasks in a computing cluster as asynchronous reactive processes with message-passing represented with an actor model and choreography of actors is developed. Also, a new scheme of data placement in a multiprocessor cluster based on prioritisation of block-recursive operations is developed to reduce idling time, data movement time. Adaptive adjustment of the data placement in a cluster at run time to account for current cluster load is developed and an auto-tuning of the actor placement in a cluster based on previous statistics for optimisation is implemented. The experiments show that the choreography of actors allows to remove the central coordinating element, to avoid hard dependencies between cluster nodes, and to achieve a better degree of the parallel applications’ scalability. GRAPHICAL ABSTRACT