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Cosmological $N$-body simulations play a vital role in studying models forthe evolution of the Universe. To compare to observations and make a scientificinference, statistic analysis on large simulation datasets, e.g., findinghalos, obtaining multi-point correlation functions, is crucial. However,traditional in-memory methods for these tasks do not scale to the datasets thatare forbiddingly large in modern simulations. Our prior paper proposesmemory-efficient streaming algorithms that can find the largest halos in asimulation with up to $10^9$ particles on a small server or desktop. However,this approach fails when directly scaling to larger datasets. This paperpresents a robust streaming tool that leverages state-of-the-art techniques onGPU boosting, sampling, and parallel I/O, to significantly improve performanceand scalability. Our rigorous analysis of the sketch parameters improves theprevious results from finding the centers of the $10^3$ largest halos to $\sim10^4-10^5$, and reveals the trade-offs between memory, running time and numberof halos. Our experiments show that our tool can scale to datasets with up to$\sim 10^{12}$ particles while using less than an hour of running time on asingle GPU Nvidia GTX 1080.

Scalable Streaming Tools for Analyzing $N$-body Simulations: Finding Halos and Investigating Excursion Sets in One Pass