Parallel In Situ Detection of Connected Components in Adaptive Mesh Refinement Data

摘要

Adaptive Mesh Refinement (AMR) represents a significant advance for scientific simulation codes, greatly reducing memory and compute requirements by dynamically varying simulation resolution over space and time. As simulation codes transition to AMR, existing analysis algorithms must also make this transition. One such algorithm, connected component detection, is of vital importance in many simulation and analysis contexts, with some simulation codes even relying on parallel, in situ connected component detection for correctness. Yet, current detection algorithms designed for uniform meshes are not applicable to hierarchical, non-uniform AMR, and to the best of our knowledge, AMR connected component detection has not been explored in the literature. Therefore, in this paper, we formally define the general problem of connected component detection for AMR, and present a general solution. Beyond solving the general detection problem, achieving viable in situ detection performance is even more challenging. The core issue is the conflict between the communication-intensive nature of connected component detection (in general, and especially for AMR data) and the requirement that in situ processes incur minimal performance impact on the co-located simulation. We address this challenge by presenting the first connected component detection methodology for structured AMR that is applicable in a parallel, in situ context. Our key strategy is the incorporation of an multi-phase AMR-aware communication pattern that synchronizes connectivity information across the AMR hierarchy. In addition, we distil our methodology to a generic framework within the Combo AMR infrastructure, making connected component detection services available for many existing applications. We demonstrate our method's efficacy by showing its ability to detect ice calving events in real time within the real-world BISICLES ice sheet modelling code. Results show up to a 6.8x speedup of our algorithm over the - xisting specialized BISICLES algorithm. We also show scalability results for our method up to 4,096 cores using a parallel Combo-based benchmark.