A Loop-Aware Search Strategy for Automated Performance Analysis

Automated online search is a powerful technique for performance diagnosis. Such a search can change the types of experiments it performs while the program is running, making decisions based on live performance data. Previous research has addressed search speed and scaling searches to large codes and many nodes. This paper explores using a finer granularity for the bottlenecks that we locate in an automated online search, i.e., refining the search to bottlenecks localized to loops. The ability to insert and remove instrumentation on-the-fly means an online search can utilize fine-grain program structure in ways that are infeasible using other performance diagnosis techniques. We automatically detect loops in a program�s binary control flow graph and use this information to efficiently instrument loops. We implemented our new strategy in an existing automated online performance tool, Paradyn. Results for several sequential and parallel applications show that a loop-aware search strategy can increase bottleneck precision without compromising search time or cost

Measurement of the splashback feature around SZ-selected Galaxy clusters with DES, SPT, and ACT

We present a detection of the splashback feature around galaxy clusters selected using the Sunyaev–Zel’dovich (SZ) signal. Recent measurements of the splashback feature around optically selected galaxy clusters have found that the splashback radius, rsp, is smaller than predicted by N-body simulations. A possible explanation for this discrepancy is that rsp inferred from the observed radial distribution of galaxies is affected by selection effects related to the optical cluster-finding algorithms. We test this possibility by measuring the splashback feature in clusters selected via the SZ effect in data from the South Pole Telescope SZ survey and the Atacama Cosmology Telescope Polarimeter survey. The measurement is accomplished by correlating these cluster samples with galaxies detected in the Dark Energy Survey Year 3 data. The SZ observable used to select clusters in this analysis is expected to have a tighter correlation with halo mass and to be more immune to projection effects and aperture-induced biases, potentially ameliorating causes of systematic error for optically selected clusters. We find that the measured rsp for SZ-selected clusters is consistent with the expectations from simulations, although the small number of SZ-selected clusters makes a precise comparison difficult. In agreement with previous work, when using optically selected redMaPPer clusters with similar mass and redshift distributions, rsp is ∼2σ smaller than in the simulations. These results motivate detailed investigations of selection biases in optically selected cluster catalogues and exploration of the splashback feature around larger samples of SZ-selected clusters. Additionally, we investigate trends in the galaxy profile and splashback feature as a function of galaxy colour, finding that blue galaxies have profiles close to a power law with no discernible splashback feature, which is consistent with them being on their first infall into the cluster

East Syracuse-Minoa Central School District and East Syracuse-Minoa Clerical Association United, NYSUT (2002)

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The Early Data Release of the Dark Energy Spectroscopic Instrument

International audienceThe Dark Energy Spectroscopic Instrument (DESI) completed its five-month Survey Validation in May 2021. Spectra of stellar and extragalactic targets from Survey Validation constitute the first major data sample from the DESI survey. This paper describes the public release of those spectra, the catalogs of derived properties, and the intermediate data products. In total, the public release includes good-quality spectral information from 466,447 objects targeted as part of the Milky Way Survey, 428,758 as part of the Bright Galaxy Survey, 227,318 as part of the Luminous Red Galaxy sample, 437,664 as part of the Emission Line Galaxy sample, and 76,079 as part of the Quasar sample. In addition, the release includes spectral information from 137,148 objects that expand the scope beyond the primary samples as part of a series of secondary programs. Here, we describe the spectral data, data quality, data products, Large-Scale Structure science catalogs, access to the data, and references that provide relevant background to using these spectra