Data Parallel and Scheduling Mechanism Based on Petri Nets

Data-parallel and task-parallel methods are the basic methods frequently used for algorithm design in parallel computing. Data-parallel method as name means is used for partition data to be processed into some small blocks considering storage and computing capacity such as memory size of a computation node, node number to take part in parallel computing and total data size, and etc. On the other hand, data dispensing strategy is an important problem carefully considered to increase the efficiency of computation. According to the characteristics of analysis of digital terrain, petri nets is introduced to describe the parallel relationships within data partitions based on data granularity model considering two kinds of computing modes, shared memory and distributed memory respectively, and corresponding scheduling algorithms are proposed for load balance. The experimental results show that our method is very usable to data partition and dispensation, in particular to distributed memory mode.