虚拟企业合作伙伴智能优化选择研究  

Skip threaded prefetching method for linked data structures

作　　者：朱会东[1] 段赵磊[1] 黄艳[1,2] 

机构地区：[1]郑州轻工业学院计算机与通信工程学院,郑州450002 [2]北京理工大学计算机学院,北京100081

出　　处：《计算机工程与应用》2012年第24期57-61,100,共6页Computer Engineering and Applications

基　　金：教育部-英特尔信息技术专项科研基金（MOE-INTEL-08-10）

摘　　要：针对复杂的虚拟企业伙伴选择问题,利用神经网络的推理功能,并结合模糊技术处理不确定因素的优势,实现最优伙伴的确定。通过分析建立了包括成本、技术能力、服务水平等指标的评价体系,采用实例验证了该方法的可行性。Applications with Linked Data Structures （LDS） usually suffer heavy last level Cache misses due to the lack of spatial locality. By analyzing the relationship between computation task and memory access task in hot loop, this paper investigates the impact of operation characteristic on the performance of threaded prefetching technique. Combining the features of CMP, this paper has implemented a staggered threaded prefetching technique for applica- tions with LDS. The experiment results prove that the ratio of computation task and memory access task actually gives rise to significant effects on staggered threaded prefetching, and staggered threaded prefetching always outper- forms conventional threaded prefetching techniques.

关 键 词：虚拟企业 合作伙伴 神经网络 评价体系 

分 类 号：TP302[自动化与计算机技术—计算机系统结构]