In this paper, a parallel Surface Extraction from Binary Volumes with Higher-Order Smoothness (SEBVHOS) algorithm is proposed to accelerate the SEBVHOS execution. The original SEBVHOS algorithm is parallelized first, ...In this paper, a parallel Surface Extraction from Binary Volumes with Higher-Order Smoothness (SEBVHOS) algorithm is proposed to accelerate the SEBVHOS execution. The original SEBVHOS algorithm is parallelized first, and then several performance optimization techniques which are loop optimization, cache optimization, false sharing optimization, synchronization overhead op-timization, and thread affinity optimization, are used to improve the implementation's performance on multi-core systems. The performance of the parallel SEBVHOS algorithm is analyzed on a dual-core system. The experimental results show that the parallel SEBVHOS algorithm achieves an average of 1.86x speedup. More importantly, our method does not come with additional aliasing artifacts, com-paring to the original SEBVHOS algorithm.展开更多
随着大语言模型(Large Language Model,LLM)技术的快速发展,高校图书馆分类编目面临智能化升级需求。为解决传统分类编目流程中效率偏低、标准适配滞后、复杂文献处理困难等问题,文章聚焦Transformer核心算法、预训练-微调算法、检索增...随着大语言模型(Large Language Model,LLM)技术的快速发展,高校图书馆分类编目面临智能化升级需求。为解决传统分类编目流程中效率偏低、标准适配滞后、复杂文献处理困难等问题,文章聚焦Transformer核心算法、预训练-微调算法、检索增强生成算法、人机协同反馈算法等主流算法,从分类、编目著录、数据校验与入库、后续维护等方面分析各算法的优势、劣势及效率表现。通过多维度测评发现,检索增强生成(Retrieval-Augmented Generation,RAG)算法凭借规则适配性强、知识更新高效的特点,适配综合性大学、专门学院及中等规模高校图书馆需求;人机协同反馈(Human-in-the-Loop,HITL)算法以极致准确率与风险可控性,成为研究型高校及馆藏珍贵文献图书馆的最优选择。文章构建的算法适配框架与选型建议,为高校图书馆分类编目智能化转型提供理论参考与实践指导,助力提升编目质量与资源利用效率。展开更多
基金Supported by the National Natural Science Foundation of China(No.61071173)
摘要In this paper, a parallel Surface Extraction from Binary Volumes with Higher-Order Smoothness (SEBVHOS) algorithm is proposed to accelerate the SEBVHOS execution. The original SEBVHOS algorithm is parallelized first, and then several performance optimization techniques which are loop optimization, cache optimization, false sharing optimization, synchronization overhead op-timization, and thread affinity optimization, are used to improve the implementation's performance on multi-core systems. The performance of the parallel SEBVHOS algorithm is analyzed on a dual-core system. The experimental results show that the parallel SEBVHOS algorithm achieves an average of 1.86x speedup. More importantly, our method does not come with additional aliasing artifacts, com-paring to the original SEBVHOS algorithm.
摘要随着大语言模型(Large Language Model,LLM)技术的快速发展,高校图书馆分类编目面临智能化升级需求。为解决传统分类编目流程中效率偏低、标准适配滞后、复杂文献处理困难等问题,文章聚焦Transformer核心算法、预训练-微调算法、检索增强生成算法、人机协同反馈算法等主流算法,从分类、编目著录、数据校验与入库、后续维护等方面分析各算法的优势、劣势及效率表现。通过多维度测评发现,检索增强生成(Retrieval-Augmented Generation,RAG)算法凭借规则适配性强、知识更新高效的特点,适配综合性大学、专门学院及中等规模高校图书馆需求;人机协同反馈(Human-in-the-Loop,HITL)算法以极致准确率与风险可控性,成为研究型高校及馆藏珍贵文献图书馆的最优选择。文章构建的算法适配框架与选型建议,为高校图书馆分类编目智能化转型提供理论参考与实践指导,助力提升编目质量与资源利用效率。