Likely invariants model properties that hold in operating conditions of a computing system. Dynamic mining of invariants aims at extracting logic formulas representing such properties from the system execution traces, and it is widely used for verification of intellectual property (IP) blocks. Although the extracted formulas represent likely invariants that hold in the considered traces, there is no guarantee that they are true in general for the system under verification. As a consequence, to increase the probability that the mined invariants are true in general, dynamic mining has to be performed to large sets of representative execution traces. This makes the execution-based mining process of actual IP blocks very time-consuming due to the trace lengths and to the large sets of monitored signals. This article presents Mangrove, an efficient implementation of a dynamic invariant mining algorithm for GPU architectures. Mangrove exploits inference rules, which are applied at run time to filter invariants from the execution traces and, thus, to sensibly reduce the problem complexity. Mangrove allows users to define invariant templates and, from these templates, it automatically generates kernels for parallel and efficient mining on GPU architectures. The article presents the tool, the analysis of its performance, and its comparison with the best sequential and parallel implementations at the state of the art.

中文翻译：

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Mangrove：基于推理的 GPU 架构动态不变挖掘

可能的不变量对在计算系统的操作条件下保持的属性进行建模。不变量的动态挖掘旨在从系统执行轨迹中提取代表这些属性的逻辑公式，它被广泛用于知识产权（IP）块的验证。尽管提取的公式代表了在所考虑的轨迹中可能存在的不变量，但不能保证它们对于被验证的系统通常是正确的。因此，为了增加挖掘出的不变量通常为真的概率，必须对大量有代表性的执行轨迹进行动态挖掘。由于跟踪长度和大量监控信号，这使得基于执行的实际 IP 块挖掘过程非常耗时。这篇文章介绍了红树林，GPU 架构的动态不变挖掘算法的有效实现。Mangrove 利用推理规则，这些规则在运行时应用于从执行跟踪中过滤不变量，从而显着降低问题的复杂性。Mangrove 允许用户定义不变模板，并从这些模板中自动生成内核，以便在 GPU 架构上进行并行和高效挖掘。本文介绍了该工具、其性能分析，以及它与最先进的最佳顺序和并行实现的比较。Mangrove 允许用户定义不变模板，并从这些模板中自动生成内核，以便在 GPU 架构上进行并行和高效挖掘。本文介绍了该工具、其性能分析，以及它与最先进的最佳顺序和并行实现的比较。Mangrove 允许用户定义不变模板，并从这些模板中自动生成内核，以便在 GPU 架构上进行并行和高效挖掘。本文介绍了该工具、其性能分析，以及它与最先进的最佳顺序和并行实现的比较。