7,717 research outputs found
Parallel Tempering Simulation of the three-dimensional Edwards-Anderson Model with Compact Asynchronous Multispin Coding on GPU
Monte Carlo simulations of the Ising model play an important role in the
field of computational statistical physics, and they have revealed many
properties of the model over the past few decades. However, the effect of
frustration due to random disorder, in particular the possible spin glass
phase, remains a crucial but poorly understood problem. One of the obstacles in
the Monte Carlo simulation of random frustrated systems is their long
relaxation time making an efficient parallel implementation on state-of-the-art
computation platforms highly desirable. The Graphics Processing Unit (GPU) is
such a platform that provides an opportunity to significantly enhance the
computational performance and thus gain new insight into this problem. In this
paper, we present optimization and tuning approaches for the CUDA
implementation of the spin glass simulation on GPUs. We discuss the integration
of various design alternatives, such as GPU kernel construction with minimal
communication, memory tiling, and look-up tables. We present a binary data
format, Compact Asynchronous Multispin Coding (CAMSC), which provides an
additional speedup compared with the traditionally used Asynchronous
Multispin Coding (AMSC). Our overall design sustains a performance of 33.5
picoseconds per spin flip attempt for simulating the three-dimensional
Edwards-Anderson model with parallel tempering, which significantly improves
the performance over existing GPU implementations.Comment: 15 pages, 18 figure
Spatial Patterns and Sequential Sampling Plans for Estimating Densities of Stink Bugs (Hemiptera: Pentatomidae) in Soybean in the North Central Region of the United States
Stink bugs are an emerging threat to soybean (Fabales: Fabaceae) in the North Central Region of the United States. Consequently, region-specific scouting recommendations for stink bugs are needed. The aim of this study was to characterize the spatial pattern and to develop sampling plans to estimate stink bug population density in soybean fields. In 2016 and 2017, 125 fields distributed across nine states were sampled using sweep nets. Regression analyses were used to determine the effects of stink bug species [Chinavia hilaris (Say) (Hemiptera: Pentatomidae) and Euschistus spp. (Hemiptera: Pentatomidae)], life stages (nymphs and adults), and field locations (edge and interior) on spatial pattern as represented by variance–mean relationships. Results showed that stink bugs were aggregated. Sequential sampling plans were developed for each combination of species, life stage, and location and for all the data combined. Results for required sample size showed that an average of 40–42 sample units (sets of 25 sweeps) would be necessary to achieve a precision of 0.25 for stink bug densities commonly encountered across the region. However, based on the observed geographic gradient of stink bug densities, more practical sample sizes (5–10 sample units) may be sufficient in states in the southeastern part of the region, whereas impractical sample sizes (\u3e100 sample units) may be required in the northwestern part of the region. Our findings provide research-based sampling recommendations for estimating densities of these emerging pests in soybean
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