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Meta-Algorithms for Scheduling a Chain of Coarse-Grained Tasks on an Array of Reconfigurable FPGAs

机译:用于在可重配置FPGA阵列上调度粗粒度任务链的元算法

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This paper considers the problem of scheduling a chain of n coarse-grained tasks on a linear array of k reconfigurable FPGAs with the objective of primarily minimizing reconfiguration time. A high-level meta-algorithm along with two detailed meta-algorithms (GPRM and SPRM) that support awide range of problem formulations and cost functions is presented. GPRM, the more general of the two schemes, reduces the problem to computing a shortest path in a DAG; SPRM, the less general scheme, employs dynamic programming. Both meta algorithms are linear in n and compute optimal solutions. GPRM can be exponential in k but is nevertheless practical because k is typically a small constant. The deterministic quality of this meta algorithm and the guarantee of optimal solutions for all of the formulations discussed make this approach a powerful alternative to other metatechniques such as simulated annealing and genetic algorithms.
机译:本文考虑了在k个可重配置FPGA的线性阵列上调度n个粗粒度任务链的问题,目的是最大程度地缩短重配置时间。提出了高级元算法以及两个详细的元算法(GPRM和SPRM),它们支持范围广泛的问题表述和成本函数。两种方案中比较通用的GPRM将问题简化为在DAG中计算最短路径。 SPRM是一种不太通用的方案,它采用动态编程。两种元算法都是n线性的,并且可以计算最优解。 GPRM可以是k的指数,但是仍然实用,因为k通常是一个小常数。这种元算法的确定性质量以及所讨论的所有公式的最优解的保证,使该方法成为其他元技术(如模拟退火和遗传算法)的有力替代方法。

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