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Memoised Garbage Collection for Software Model Checking

机译:记忆垃圾收集,用于软件模型检查

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Virtual machine based software model checkers like JPF and MoonWalker spend up to half of their verification time on garbage collection. This is no surprise as after nearly each transition the heap has to be cleaned from garbage. To improve this, this paper presents the Memoised Garbage Collection (MGC) algorithm, which exploits the (typical) locality of transitions to incrementally perform garbage collection. MGC tracks the depths of objects efficiently and only purges objects whose depths have become infinite, hence unreachable. MGC was experimentally evaluated via an implementation in our model checker MoonWalker and benchmarks using the parallel Java Grande Forum benchmark suite. By using MGC, a performance increase up to 78% was measured over the traditional Mark & Sweep implementation.
机译:JPF和MoonWalker等基于虚拟机的软件模型检查器将其验证时间的一半用于垃圾回收。这不足为奇,因为几乎在每次转换之后都必须清除垃圾堆。为了改善这一点,本文提出了“记忆垃圾收集”(MGC)算法,该算法利用过渡的(典型)局部性来增量执行垃圾收集。 MGC有效地跟踪对象的深度,并且仅清除深度变得无限大因而无法到达的对象。通过使用并行Java Grande论坛基准测试套件在模型检查器MoonWalker和基准测试中的实现,对MGC进行了实验评估。通过使用MGC,与传统的Mark&Sweep实施相比,性能提高了78%。

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