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 existing specialized BISICLES algorithm. We also show scalability results for our method up to 4,096 cores using a parallel Combo-based benchmark.

PDE solvers using Adaptive Mesh Refinement on block structured grids are some of the most challenging applications to adapt to massively parallel computing environments. We describe optimizations to the Chombo AMR framework that enable it to scale efficiently to thousands of processors on the Cray XT4. The optimization process also uncovered OS-related performance variations that were not explained by conventional OS interference benchmarks. Ultimately the variability was traced back to complex interactions between the application, system software, and the memory hierarchy. Once identified, software modifications to control the variability improved performance by 20% and decreased the variation in computation time across processors by a factor of 3. These newly identified sources of variation will impact many applications and suggest new benchmarks for OS-services be developed.

Radiance is a software package developed at Lawrence Berkeley National Laboratory for lighting visualization. Lighting visualization predicts how lighting would appear if a modelled scene were to be physically realized. Unlike most lighting systems, Radiance physically models the effects of lighting, providing an image that is closer to physical reality. This is of obvious benefit to architects and lighting designers. Such visualizations are computationally expensive: rendering a single image can take hours on a standard workstation environment. Ideally, an architect would like to be able to interactively navigate through a scene to get a full impression of the true appearance of a particular model. With this goal in mind, we have (1) developed a geometric-based method (point cloud) to reuse pixels from a previous frame and (2) developed a parallel, distributed memory implementation of Radiance and the point cloud using MPI for inter-processor communication.