There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.

/pdf/using-deep-learning-for-image-based-plant-disease-detection-3ep7b222h5.pdf

Using Deep Learning for Image-Based Plant Disease Detection

Deep learning in agriculture: A survey

Natural ventilation is an important factor in the design of sustainable buildings; it has the potential to improve air quality, while providing thermal comfort at reduced energy costs. Computational fluid dynamics (CFD) simulations provide comprehensive information on the internal flow pattern and can be used as a design tool. The present work offers insight of natural ventilation in a fully functional building, namely, the solar facility Interlock House in Iowa. Ventilation in the house is studied during summer months with some of its furniture included. The results show quantitative agreement between numerical simulations and experiments of vertical temperature profiles for each room. The temperature profile of the room with the inlet opening shows a more pronounced temperature variation. Flow patterns show higher velocities near the walls and marked flow circulation towards the opposite side of the building. The purpose of this work is to validate the numerical model that predicts airflow distribution for different configurations.Copyright © 2015 by ASME

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1175&context=me_conf

High Fidelity CFD Modeling of Natural Ventilation in a Solar House

In most computational codes, the core computational kernel is the Sparse Matrix-Vector product (SpMV) that enables specialized linear algebra libraries like PETSc to be used, especially in the distributed memory setting. However, optimizing SpMvperformance and scalability at all levels of a modern heterogeneous architecture can be challenging as it is characterized by irregular memory access. This work presents a hybrid approach (HyMV) for evaluating SpMV for matrices arising from PDE discretization schemes such as the finite element method (FEM). The approach enables localized structured memory access that provides improved performance and scalability. Additionally, it simplifies the programmability and portability on different architectures. The developed HyMV approach enables efficient parallelization using MPI, SIMD, OpenMP, and CUDA with minimum programming effort. We present a detailed comparison of HyMV with the two traditional approaches in computational code, matrix-assembled and matrix-free approaches, for structured and unstructured meshes. Our results demonstrate that the HyMV approach achieves excellent scalability and outperforms both approaches, e.g., achieving average speedups of 11x for matrix setup, 1.7x for SpMV with structured meshes, 3.6x for SpMV with unstructured meshes, and 7.5x for GPU SpMV.

A scalable adaptive-matrix SPMV for heterogeneous architectures

Estimating contaminant distribution from finite sensor data: Perron Frobenious operator and ensemble Kalman Filtering

An investigation into the dynamics of spinodal decomposition under varying levels of noise is presented. Obtaining a clear understanding of how noise affects the short-term dynamics of this phenomenon is critical for accurate model development and subsequent efforts for morphology control. Following the well-known Cahn–Hilliard model for phase separation, we present an efficient and scalable finite element–based framework to study the dynamics of spinodal decomposition. This framework is massively parallel with good scalability up to 100 k CPUs. In addition, we formulate a higher-order time scheme and discuss thermodynamically consistent numerical treatment of the added noise term through the use of a scalable, parallel random number generator. Then, we perform a high-throughput analysis of over 6400 independent simulations and examine the interplay between noise amplitude and various numerical features critical to model development (discretization, domain size, and boundary conditions). Our numerical results suggest that a proper treatment of the stochastic term is required to study the influence of noise on the initiation of phase separation. This opens avenues for microstructure control and has important implications for materials-by-design.

Quantifying the effects of noise on early states of spinodal decomposition:: Cahn–Hilliard–Cook equation and energy-based metrics

Abstract Charge transport in molecular solids, such as semiconducting polymers, is strongly affected by packing and structural order over several length scales. Conventional approaches to modeling these phenomena range from analytical models to numerical models using quantum mechanical calculations. While analytical approaches cannot account for detailed structural effects, numerical models are expensive for exhaustive (and statistically significant) analysis. Here, we report a computationally scalable methodology using graph theory to explore the influence of molecular ordering on charge mobility. This model accurately reproduces the analytical results for transport in nematic and isotropic systems, as well as experimental results of the dependence of the charge carrier mobility on orientation correlation length for polymers. We further model how defect distribution (correlated and uncorrelated) in semiconducting polymers can modify the mobility, predicting a critical defect density above which the mobility plummets. This work enables rapid (and computationally extensible) evaluation of charge mobility semiconducting polymer devices. 

https://www.nature.com/articles/s41524-022-00714-w.pdf

Baskar Ganapathysubramanian

Papers

High Fidelity CFD Modeling of Natural Ventilation in a Solar House

A scalable adaptive-matrix SPMV for heterogeneous architectures

Estimating contaminant distribution from finite sensor data: Perron Frobenious operator and ensemble Kalman Filtering

Quantifying the effects of noise on early states of spinodal decomposition:: Cahn–Hilliard–Cook equation and energy-based metrics

A graph based approach to model charge transport in semiconducting polymers