Communication traces collected from MPI applications are an important source of information for performance optimization as they can help analysts determine communication patterns and identify inefficiencies. However, their collection, especially at scale, is time consuming, since it usually requires running the complete target application on a large number of nodes. In this work, we present PredCom, a tool-chain to generate a predictive communication proxy based on information gathered from a few small scale runs, which allows us to extract approximate communication traces with an accuracy high enough for most analysis goals. For this, we combine LLVM passes on the original source code (to capture static program structure) with parameter prediction (to capture dynamic and scaling behavior). This approach drastically reduces the time needed for collecting the communication traces, even for traces on large numbers of MPI processes. We demonstrate that PredCom generates communication traces of various applications up to 1612x faster with an accuracy loss of 0.11 on average compared to the original large-scale traces, and we show that the generated traces can be used to optimize process placement.

中文翻译：

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PredCom：一种收集近似通信踪迹的预测方法

从 MPI 应用程序收集的通信跟踪是性能优化的重要信息来源，因为它们可以帮助分析师确定通信模式并识别低效率。然而，它们的收集，尤其是大规模收集，是非常耗时的，因为它通常需要在大量节点上运行完整的目标应用程序。在这项工作中，我们提出了 PredCom，这是一个工具链，可根据从几次小规模运行中收集的信息生成预测通信代理，这使我们能够以足够高的准确度提取近似通信轨迹，以实现大多数分析目标。为此，我们将原始源代码上的 LLVM 传递（以捕获静态程序结构）与参数预测（以捕获动态和缩放行为）相结合。这种方法大大减少了收集通信跟踪所需的时间，即使是对大量 MPI 进程的跟踪也是如此。我们证明了与原始大规模跟踪相比，PredCom 生成各种应用程序的通信跟踪的速度提高了 1612 倍，平均精度损失为 0.11，并且我们表明生成的跟踪可用于优化流程布局。