Optimizing Data Loading and Memory Allocation for Out-of-Core Simplification

The simplification of out-of-core model must be handled by reading data in batches and using local operations. Thus, both data reading and main memory allocation have much influence on simplification efficiency. With regard to this, we put forward novel measures in this paper for dynamically optimizing data reading and memory allocation. While the algorithm is simplifying the data at the beginning, it also examines the data block size and memory allocation pattern that may influence the efficiency of the simplification. This makes computers with different configurations adaptively get their own optimized data sizes and memory allocation patterns when processing different out-of-core models. As a result, the simplification process of the remaining data could be speeded up. Experimental results show that our measures can considerably improve out-of-core simplification efficiency.