【摘要】 由于外存模型的数据量极大,其简化计算只能采用分批读入模型数据并局部处理的方式.其中,数据读入和内存分配的操作对简化效率影响很大.提出一种动态优化方法,在对先处理的一小部分数据进行简化操作的同时,检测数据读取块大小和内存分配模式对简化操作的影响,由此可使不同配置的计算机在处理不同的外存模型时能自适应地得到相应的优化数据读取块大小和内存分配模式,加速后续大量数据的简化操作.实验结果表明,文中方法能有效地提高外存模型简化的效率.更多还原

【Abstract】 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.更多还原