Data-parallel halo finding with variable linking lengths

Abstract

Figure 1: Halos found on a set of particles from a cosmological simulation: (a) Dataset contains 16.7 million dark matter particles. (b) Halos with linking length 0.2 and halo size 11. (c) Halos after increasing the linking length to 0.4141. Each set of particles in the same color in (b) and (c) represents a single halo. ABSTRACTState-of-the-art cosmological simulations regularly contain billions of particles, providing scientists the opportunity to study the evolution of the Universe in great detail. However, the rate at which these simulations generate data severely taxes existing analysis techniques. Therefore, developing new scalable alternatives is essential for continued scientific progress. Here, we present a dataparallel, friends-of-friends halo finding algorithm that provides unprecedented flexibility in the analysis by extracting multiple linking lengths. Even for a single linking length, it is as fast as the existing techniques, and is portable to multi-threaded many-core systems as well as co-processing resources. Our system is implemented using PISTON and is coupled to an interactive analysis environment used to study halos at different linking lengths and track their evolution over time.

Helium flux effects on bubble growth and surface morphology in plasma-facing tungsten from large-scale molecular dynamics simulations

“…The bottom surface, which is frozen, is similarly clipped to remove the bottom surface from the visualization. Further details of these visualizations are discussed in [78].…”

Data-Parallel Hashing Techniques for GPU Architectures

“…An extensive body of work has embarked on the redesign of data structures for construction and general computation on the GPU [88]. Within the context of searching, these acceleration structures include sorted arrays [3], [4], [8], [51], [66], [67], [98] and linked lists [116], hash tables (see section III), spatial-partitioning trees (e.g., k-d trees [57], [115], [120], octrees [57], [119], bounding volume hierarchies (BVH) [57], [64], R-trees [71], and binary indexing trees [59], [99]), spatial-partitioning grids (e.g., uniform [36], [53], [62] and two-level [52]), skiplists [81], and queues (e.g., binary heap priority [43] and FIFO [17], [101]). Due to significant architectural differences between the CPU and GPU, search structures cannot simply be "ported" from the CPU to the GPU and maintain optimal performance.…”

Nested Tracking Graphs

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