Core Concepts
This paper presents novel locality-aware algorithms for sparse dynamic data exchange that achieve up to 20x speedup over existing methods, enabling more scalable parallel applications.
Abstract
The paper addresses the problem of sparse dynamic data exchange (SDDE), which is a crucial communication pattern in many parallel applications such as sparse solvers and simulations. SDDE is required to determine the communication pattern, i.e., which processes each process must send data to and receive data from, before the actual data exchange can occur.
The paper first presents a common API for SDDE algorithms within an MPI extension library, allowing applications to utilize various optimized SDDE methods. It then describes three existing SDDE algorithms: the personalized method, the non-blocking method, and an RMA-based method for constant-sized exchanges.
The key contribution of the paper is the introduction of novel locality-aware variants of the personalized and non-blocking SDDE algorithms. These locality-aware methods aggregate messages within a region, such as a socket or node, to minimize the number of inter-region messages. This reduces the impact of higher latency and lower bandwidth for inter-region communication.
The paper evaluates the performance of the various SDDE algorithms across a set of sparse matrices from the SuiteSparse collection. The results show that the locality-aware non-blocking SDDE method outperforms the other approaches, achieving up to 20x speedup at large scale. This improvement is attributed to the reduced number of inter-node messages and the avoidance of collective synchronization required by the personalized method.
The paper concludes by discussing the need for performance models to dynamically select the optimal SDDE algorithm based on the communication pattern and architecture, as well as the potential to extend the locality-aware techniques to heterogeneous systems.
Stats
The number of messages communicated by the standard and aggregated approaches are shown in the figures.