Visualizing network traffic to understand the performance of massively parallel simulations

pre-printThe performance of massively parallel applications is often heavily impacted by the cost of communication among compute nodes. However, determining how to best use the network is a formidable task, made challenging by the ever increasing size and complexity of modern supercomputers. This paper applies visualization techniques to aid parallel application developers in understanding the network activity by enabling a detailed exploration of the flow of packets through the hardware interconnect. In order to visualize this large and complex data, we employ two linked views of the hardware network. The first is a 2D view, that represents the network structure as one of several simplified planar projections. This view is designed to allow a user to easily identify trends and patterns in the network traffic. The second is a 3D view that augments the 2D view by preserving the physical network topology and providing a context that is familiar to the application developers. Using the massively parallel multi-physics code pF3D as a case study, we demonstrate that our tool provides valuable insight that we use to explain and optimize pF3D's performance on an IBM Blue Gene/P system

Doctor of Philosophy in Computing

thesisThe ever-increasing amounts of data generated by scientific simulations, coupled with system I/O constraints, are fueling a need for in-situ analysis techniques, i.e., performing the analysis concurrently with the simulation. Of particular interest are approaches that produce reduced data representations while maintaining the ability to redefine, extract, and study features in a postprocess to obtain scientific insights. One such approach is using topological constructs called segmented merge trees, which record changes in the topology of super-level sets of a scalar function. They encapsulate a wide range of threshold-based features, which can be extracted for analysis and visualization; however, current techniques for their computation are not scalable enough for in-situ analysis. This thesis presents a novel distributed algorithm that, for the first time, allows large-scale, in-situ computation of segmented merge trees. Existing merge tree computation techniques are restricted to simplicial complexes and three-dimensional (3D) rectilinear grids; instead, we present the theoretical foundations for computing merge trees on CW-complexes, which represent a broader class of meshes. Based on this theoretical foundation, we present two variants of in-situ feature extraction techniques using segmented merge trees. The first approach is a fast, low communication cost technique that generates an exact solution but has limited scalability. The second is a scalable, local approximation that, nevertheless, is guaranteed to correctly extract all features up to a predefined size. We demonstrate both variants using some of the largest combustion simulations available on leadership class supercomputers. Our approach allows feature-based analysis to be performed in-situ at significantly higher frequency than currently possible and with negligible impact on the overall simulation runtime. We provide a detailed performance and scalability analysis of this technique. Furthermore, as scientific applications target exascale, challenges related to power and energy are becoming dominating concerns. To this end, this thesis explores the various performance versus power trade-offs of the presented in-situ technique, studies its behavior when various in-situ computation strategies are employed, and extrapolates the power behavior to peta-scale systems to investigate different design choices through projections