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A small number of pulsars are known to emit giant pulses, single pulses much brighter than average. Among these is PSR J0534+2200, also known as the Crab pulsar, a young pulsar with high giant pulse rates. Long-term monitoring of the Crab pulsar presents an excellent opportunity to perform statistical studies of its giant pulses and the processes affecting them, potentially providing insight into the behavior of other neutron stars that emit bright single pulses. Here, we present an analysis of a set of 24,985 Crab giant pulses obtained from 88 hours of daily observations at a center frequency of 1.55 GHz by the 20-meter telescope at the Green Bank Observatory, spread over 461 days. We study the effects of refractive scintillation at higher frequencies than previous studies and compare methods of correcting for this effect. We also search for deterministic patterns seen in other single-pulse sources, possible periodicities seen in several rotating radio transients and fast radio bursts, and clustering of giant pulses like that seen in the repeating fast radio burst FRB121102., Comment: 21 pages, 14 figures, accepted to ApJ

Listing and counting triangles in graphs is a key algorithmic kernel for network analyses, including community detection, clustering coefficients, k-trusses, and triangle centrality. In this paper, we propose the novel concept of a cover-edge set that can be used to find triangles more efficiently. Leveraging the breadth-first search (BFS) method, we can quickly generate a compact cover-edge set. Novel sequential and parallel triangle counting algorithms that employ cover-edge sets are presented. The novel sequential algorithm performs competitively with the fastest previous approaches on both real and synthetic graphs, such as those from the Graph500 Benchmark and the MIT/Amazon/IEEE Graph Challenge. We implement 22 sequential algorithms for performance evaluation and comparison. At the same time, we employ OpenMP to parallelize 11 sequential algorithms, presenting an in-depth analysis of their parallel performance. Furthermore, we develop a distributed parallel algorithm that can asymptotically reduce communication on massive graphs. In our estimate from massive-scale Graph500 graphs, our distributed parallel algorithm can reduce the communication on a scale~36 graph by 1156x and on a scale~42 graph by 2368x. Comprehensive experiments are conducted on the recently launched Intel Xeon 8480+ processor and shed light on how graph attributes, such as topology, diameter, and degree distribution, can affect the performance of these algorithms.