增量预见局部压缩

MICRO 22 Pub Date : 1989-08-01 DOI:10.1145/75362.75415

Pantung Wijaya, V. Allan

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引用次数: 8

摘要

在时间约束下，由于调度决策不佳，局部压缩可能会失败。Su [SDWX87]使用预见性来避免一些糟糕的调度决策。然而，这种预见需要相当多的时间。本文介绍了增量预见算法。使用四种不同目标架构的实验表明，增量预测算法的预测效果良好，并节省了大约48%的多余时间。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Incremental foresighted local compaction

Under timing constraints, local compaction may fail because of poor scheduling decisions. Su [SDWX87] uses foresight to avoid some of the poor scheduling decisions. However, the foresight takes a considerable amount of time. In this paper the Incremental Foresight algorithm is introduced. Experiments using four different target architectures show that the Incremental Foresight algorithm works as well as foresight, and saves around 48 percent of the excess time.

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MICRO 22

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