Design and Optimization of Parallel Algorithm for Kalman Filter on SW26010 Many-Core Processors

Abstract

Kalman filter algorithm, an effective data processing algorithm, has been widely used in space monitoring, wireless communications, tracking systems, financial industry, big data and so on. On Sunway TaihuLight platform, we present an optimized Kalman filter parallel algorithm which is according to new architecture of the SW26010 many-core processors (260 cores) and new programming mode (master and slave heterogeneous collaboration mode). Furthermore, we propose a pipelined parallel mode for Kalman filter algorithm based on seven-level pipeline of SW26010 processor. The vector optimization strategy and double buffering mechanisms are provided to improve parallel efficiency of Kalman filter parallel algorithm on SW26010 processors. The vector optimization strategy can improve data concurrency in parallel computing. In addition, the communication time can be hidden by double buffering mechanisms of SW26010 processors. The experimental results show that the performance and scalability of the parallel Kalman filter algorithm based on SW26010 are greatly improved compared with the CPU algorithm for five data sets, and is also improved compared to the algorithm on GPU.