For the course of“Data Structures”,this paper introduces the importance and existing problems of the data structure course.Through the literature and the current teaching in major universities,the existing teaching ...For the course of“Data Structures”,this paper introduces the importance and existing problems of the data structure course.Through the literature and the current teaching in major universities,the existing teaching methods and their disadvantages are analyzed.The authors put forward teaching reform suggestions and designed an online course platform.展开更多
To solve the problem of grid coarse-grained reconfigurable array task mapping under multiple constraints,we propose a Loop Subgraph-Level Greedy Mapping(LSLGM)algorithm using parallelism and processing element fragmen...To solve the problem of grid coarse-grained reconfigurable array task mapping under multiple constraints,we propose a Loop Subgraph-Level Greedy Mapping(LSLGM)algorithm using parallelism and processing element fragmentation.Under the constraint of a reconfigurable array,the LSLGM algorithm schedules node from a ready queue to the current reconfigurable cell array block.After mapping a node,its successor’s indegree value will be dynamically updated.If its successor’s indegree is zero,it will be directly scheduled to the ready queue;otherwise,the predecessor must be dynamically checked.If the predecessor cannot be mapped,it will be scheduled to a blocking queue.To dynamically adjust the ready node scheduling order,the scheduling function is constructed by exploiting factors,such as node number,node level,and node dependency.Compared with the loop subgraph-level mapping algorithm,experimental results show that the total cycles of the LSLGM algorithm decreases by an average of 33.0%(PEA44)and 33.9%(PEA_(7×7)).Compared with the epimorphism map algorithm,the total cycles of the LSLGM algorithm decrease by an average of 38.1%(PEA_(4×4))and 39.0%(PEA_(7×7)).The feasibility of LSLGM is verified.展开更多
基金supported in part by grants from Qinglan Project of Jiangsu Province(2020)High-level demonstration construction project of Sino-foreign cooperation in running schools in Jiangsu Province,Jiangsu Higher Educational Technology Research Association-2019 Higher Education Informatization Research Project(2019JSETKT035)Ideological and Political Special Project of Philosophy and Social Science Research Projects in Colleges and Universities in 2019(2019SJB422).
文摘For the course of“Data Structures”,this paper introduces the importance and existing problems of the data structure course.Through the literature and the current teaching in major universities,the existing teaching methods and their disadvantages are analyzed.The authors put forward teaching reform suggestions and designed an online course platform.
基金This research was supported by the Natural Science Foundation of Anhui Province(No.1808085MF203)the Natural Science Foundation of China(Nos.61972438 and 61432017).
文摘To solve the problem of grid coarse-grained reconfigurable array task mapping under multiple constraints,we propose a Loop Subgraph-Level Greedy Mapping(LSLGM)algorithm using parallelism and processing element fragmentation.Under the constraint of a reconfigurable array,the LSLGM algorithm schedules node from a ready queue to the current reconfigurable cell array block.After mapping a node,its successor’s indegree value will be dynamically updated.If its successor’s indegree is zero,it will be directly scheduled to the ready queue;otherwise,the predecessor must be dynamically checked.If the predecessor cannot be mapped,it will be scheduled to a blocking queue.To dynamically adjust the ready node scheduling order,the scheduling function is constructed by exploiting factors,such as node number,node level,and node dependency.Compared with the loop subgraph-level mapping algorithm,experimental results show that the total cycles of the LSLGM algorithm decreases by an average of 33.0%(PEA44)and 33.9%(PEA_(7×7)).Compared with the epimorphism map algorithm,the total cycles of the LSLGM algorithm decrease by an average of 38.1%(PEA_(4×4))and 39.0%(PEA_(7×7)).The feasibility of LSLGM is verified.
