网格粗粒度可重构阵列的循环子图级贪心映射算法

IF 5.2 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Tsinghua Science and Technology Pub Date : 2022-09-29 DOI:10.26599/TST.2022.9010001
Naijin Chen;Fei Cheng;Chenghao Han;Jianhui Jiang;Xiaoqing Wen
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引用次数: 0

摘要

为了解决多约束条件下网格粗粒度可重构阵列任务映射问题,我们提出了一种利用并行性和处理元素碎片的循环子图级贪婪映射(LSLGM)算法。在可重构阵列的约束下,LSLGM算法将节点从就绪队列调度到当前可重构单元阵列块。映射节点后,其后续节点的不度值将动态更新。如果其继任者的不同意度为零,则将其直接调度到就绪队列;否则,必须动态检查前置器。如果前置任务无法映射,它将被安排到阻塞队列。为了动态调整就绪节点的调度顺序,通过利用节点数量、节点级别和节点依赖性等因素来构建调度函数。实验结果表明,与循环子图级映射算法相比,LSLGM算法的总周期平均减少了33.0%(PEA4×4)和33.9%(PEA7×7)。与差向映射算法相比,LSLGM算法的总周期平均减少了38.1%(PEA4×4)和39.0%(PEA7×7)。验证了LSLGM的可行性。
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Loop Subgraph-Level Greedy Mapping Algorithm for Grid Coarse-Grained Reconfigurable Array
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% (PEA 4×4 ) 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.
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CiteScore
12.10
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