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Achieving Speedups in Distributed Symbolic Reachability Analysis Through Asynchronous Computation

机译:通过异步计算实现分布式符号可达性分析的加速

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This paper presents a novel BDD-based distributed algorithm for reachability analysis which is completely asynchronous. Previous BDD-based distributed schemes are synchronous: they consist of interleaved rounds of computation and communication, in which the fastest machine (or one which is lightly loaded) must wait for the slowest one at the end of each round. We make two major contributions. First, the algorithm performs image computation and message transfer concurrently, employing non-blocking protocols in several layers of the communication and the computation infrastructures. As a result, regardless of the scale and type of the underlying platform, the maximal amount of resources can be utilized efficiently. Second, the algorithm incorporates an adaptive mechanism which splits the workload, taking into account the availability of free computational power. In this way, the computation can progress more quickly because, when more CPUs are available to join the computation, less work is assigned to each of them. Less load implies additional important benefits, such as better locality of reference, less overhead in compaction activities (such as reorder), and faster and better workload splitting. We implemented the new approach by extending a symbolic model checker from Intel. The effectiveness of the resulting scheme is demonstrated on a number of large industrial designs as well as public benchmark circuits, all known to be hard for reachability analysis. Our results show that the asynchronous algorithm enables efficient utilization of higher levels of parallelism. High speedups are reported, up to an order of magnitude, for computing reachability for models with higher memory requirements than was previously possible.
机译:本文介绍了一种新的基于BDD的分布式算法,可用于可达性分析,该算法是完全异步的。以前的基于BDD的分布式方案是同步的:它们由交错的计算和通信组成,其中最快的机器(或轻度加载的电机)必须等待每轮末端的最慢的一个。我们提出了两项​​重大贡献。首先,该算法同时执行图像计算和消息传输,在通信的几个层和计算基础架构中采用非阻塞协议。结果,无论底层平台的比例和类型如何,都可以有效地利用最大资源量。其次,算法包含一种自适应机制,其分割工作量,考虑到自由计算能力的可用性。以这种方式,计算可以更快地进步,因为当更多CPU可用于加入计算时,将少分配给每个CPU。较少的负载意味着额外的重要益处,例如参考的更好位置,压实活动中的较少开销(例如重新排序),更快,更好的工作量分裂。我们通过从英特尔扩展符号模型检查器来实现新方法。所得到的方案的有效性在许多大型工业设计以及公共基准电路上证明,所有已知是难以进行可达性分析的。我们的结果表明,异步算法可以高效地利用更高水平的并行性。报告高速度高速,最大级,用于计算具有比以前更高的内存要求更高的模型的可拆卸性。

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