Scalable streaming tools for analyzing N-body simulations: Finding halos and investigating excursion sets in one pass

Autor: Lin Yang, Zaoxing Liu, Mark C. Neyrinck, Vladimir Braverman, Srinivas Suresh Kumar, Gerard Lemson, Tamás Budavári, Alexander S. Szalay, Nikita Ivkin
Rok vydání: 2018
Předmět:
Zdroj: Astronomy and Computing. 23:166-179
ISSN: 2213-1337
Popis: Cosmological N -body simulations play a vital role in studying models for the evolution of the Universe. To compare to observations and make a scientific inference, statistic analysis on large simulation datasets, e.g., finding halos, obtaining multi-point correlation functions, is crucial. However, traditional in-memory methods for these tasks do not scale to the datasets that are forbiddingly large in modern simulations. Our prior paper (Liu et al., 2015) proposes memory-efficient streaming algorithms that can find the largest halos in a simulation with up to 1 0 9 particles on a small server or desktop. However, this approach fails when directly scaling to larger datasets. This paper presents a robust streaming tool that leverages state-of-the-art techniques on GPU boosting, sampling, and parallel I/O, to significantly improve performance and scalability. Our rigorous analysis of the sketch parameters improves the previous results from finding the centers of the 1 0 3 largest halos (Liu et al., 2015) to ∼ 1 0 4 − 1 0 5 , and reveals the trade-offs between memory, running time and number of halos. Our experiments show that our tool can scale to datasets with up to ∼ 1 0 12 particles while using less than an hour of running time on a single GPU Nvidia GTX 1080.
Databáze: OpenAIRE
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