独立与非独立问题的答案记忆并行回溯算法
Parallel Backtracking with Answer Memoing for Independent And-Parallelism
摘要：
目标级独立与并行（IAP）是通过调度两个或多个在运行时间不会相互干扰的目标来实现的。这可以安全地完成，即使这些目标可以产生多个答案。到目前为止，最成功的IAP实现使用了重新计算答案和顺序回溯。虽然原则上简化了实现，但如果并行目标的粒度足够大并且它们产生多个答案，则重新计算可能非常低效，而顺序排序的回溯限制了并行性。而且，尽管预期的简化，经典方案的实施已被证明涉及复杂的工程，随之而来的是系统维护和扩展的困难，同时仍然经常遇到众所周知的被困目标和垃圾槽问题。这项工作提出了一种替代的并行回溯模型IAP及其实现。该模型具有并行无序（即，非时间顺序）回溯，并依赖于答案记忆来重用和组合答案。我们表明，这种方法可以带来显着的性能优势。此外，它可以带来一些简化的重要工程任务，涉及到实现回溯机制的以前的方法。
英文摘要：
Goal-level Independent and-parallelism (IAP) is exploited by scheduling for simultaneous execution two or more goals which will not interfere with each other at run time. This can be done safely even if such goals can produce multiple answers. The most successful IAP implementations to date have used recomputation of answers and sequentially ordered backtracking. While in principle simplifying the implementation, recomputation can be very inefficient if the granularity of the parallel goals is large enough and they produce several answers, while sequentially ordered backtracking limits parallelism. And, despite the expected simplification, the implementation of the classic schemes has proved to involve complex engineering, with the consequent difficulty for system maintenance and extension, while still frequently running into the well-known trapped goal and garbage slot problems. This work presents an alternative parallel backtracking model for IAP and its implementation. The model features parallel out-of-order (i.e., non-chronological) backtracking and relies on answer memoization to reuse and combine answers. We show that this approach can bring significant performance advantages. Also, it can bring some simplification to the important engineering task involved in implementing the backtracking mechanism of previous approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
