Performance evaluation of parallel re-computing algorithm in different data distribution modes

Abstract

With the rapid increase of spatial data resolution, the huge volume of datasets makes geo-computation more time-consuming especially when operating some complex algorithms, i.e. viewshed analysis and drainage network extraction in digital terrain analysis. Parallel computing is regarded as an efficient solution by utilizing more computing resources. Among them, the stable and credible services play an irreplaceable role in parallel computing, especially when an error occurs in the large-scale scientific computing. In this paper, a master/slave approach to implement the parallel re-computing is proposed based on redundancy mechanism. Once some errors in application layer are detected, the original data block with computation errors is further partitioned into several sub-blocks which are re-computed by the surviving processes concurrently to improve the efficiency of failure recovery. The multi-thread strategy in the main process is responsible for the distribution of data blocks, detecting errors and starting re-computing procedure concurrently. Performance evaluation is conducted in different data distributed modes by theory analysis. The experimental results show that the performance of fault-tolerant parallel computing is different by way of adopting different data distribution modes.