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Travel-time computation with large transportation networks is often computationally intensive for two main reasons: 1) large computer memory is required to handle large networks; and 2) calculating shortest-distance paths over large networks is computing intensive. Therefore, previous research tends to limit their spatial extent to reduce computational intensity or resolve computational intensity with advanced cyberinfrastructure. In this context, this article describes a new Spatial Partitioning Algorithm for Scalable Travel-time Computation (SPASTC) that is designed based on spatial domain decomposition with computer memory limit explicitly considered. SPASTC preserves spatial relationships required for travel-time computation and respects a user-specified memory limit, which allows efficient and large-scale travel-time computation within the given memory limit. We demonstrate SPASTC by computing spatial accessibility to hospital beds across the conterminous United States. Our case study shows that SPASTC achieves significant efficiency and scalability making the travel-time computation tens of times faster. [ABSTRACT FROM AUTHOR]

Modeling coupled natural–human systems (CNHS) to inform comprehensive water resources management policies or describe hydrological cycles in the Anthropocene has become popular in recent years. To fulfill this need, we developed a semidistributed hydrological model for coupled natural–human systems, HydroCNHS. HydroCNHS is an open-source Python package supporting four application programming interfaces (APIs) that enable users to integrate their human decision models, which can be programmed with the agent-based modeling concept, into HydroCNHS. Specifically, we designed Dam API, RiverDiv API, Conveying API, and InSitu API to integrate, respectively, customized man-made infrastructures such as reservoirs, off-stream diversions, transbasin aqueducts, and drainage systems that abstract human behaviors (e.g., operator and farmer water use decisions). Each of the HydroCNHS APIs has a unique plug-in structure that respects within-subbasin and inter-subbasin (i.e., river) routing logic for maintaining the water balance. In addition, HydroCNHS uses a single model configuration file to organize input features for the hydrological model and case-specific human systems models. Also, HydroCNHS enables model calibration using parallel computing power. We demonstrate the functionalities of the HydroCNHS package through a case study in the Northwest United States. Given the integrity of the modeling framework, HydroCNHS can benefit water resources planning and management in various aspects, including uncertainty analysis in CNHS modeling and more complex agent design. [ABSTRACT FROM AUTHOR]

Allergic rhinitis (hay fever) resulting from seasonal pollen affects 15–30% of the population in the United States, and can exacerbate several related conditions, including asthma, atopic eczema, and allergic conjunctivitis. Timely monitoring, accurate prediction, and visualization of pollen levels are critical for public health prevention purposes, such as limiting outdoor exposure or physical activity. The low density of pollen detecting stations and complex movement of pollen represent a challenge for accurate prediction and modeling. In this paper, we reconstruct the dynamics of pollen variation across the Eastern United States for 2016 using space–time interpolation. Pollen levels were extracted according to a stratified spatial sampling design, augmented by additional samples in densely populated areas. These measurements were then used to estimate the space–time cross-correlation, inferring optimal spatial and temporal ranges to calibrate the space–time interpolation. Given the computational requirements of the interpolation algorithm, we implement a spatiotemporal domain decomposition algorithm, and use parallel computing to reduce the computational burden. We visualize our results in a 3D environment to identify the seasonal dynamics of pollen levels. Our approach is also portable to analyze other large space–time explicit datasets, such as air pollution, ash clouds, and precipitation. [ABSTRACT FROM AUTHOR]

As the need for multicore-aware programmers rises in both science and industry, Computer Science departments in universities around the USA are having to rethink their parallel computing curriculum. At Rice University, this rethinking took the shape of COMP 322, an introductory parallel programming course that is required for all Bachelors students. COMP 322 teaches students to reason about the behavior of parallel programs, educating them in both the high level abstractions of task-parallel programming as well as the nitty gritty details of working with threads in Java. In this paper, we detail the structure, principles, and experiences of COMP 322, gained from 6 years of teaching parallel programming to second-year undergraduates. We describe in detail two particularly useful tools that have been integrated into the curriculum: the HJlibparallel programming library and the Habanero Autograder for parallel programs. We present this work with the hope that it will help augment improvements to parallel computing education at other universities. [ABSTRACT FROM AUTHOR]

Fuzzy controllers are used in many applications because of their rapid design by translating heuristic knowledge, robustness against perturbations, and smoothness in the control action. However, they require parallel processing and special operators (such as fuzzification or defuzzification) which are not available in standard digital signal processors (DSPs), thus complicating their direct implementation. This paper describes an efficient design methodology that allows starting with any kind of fuzzy controller and subsequently transforming it until a system suitable for easy DSP implementation is obtained. Such methodology is greatly aided by the design environment Xfuzzy 3. The parking problem of an autonomous robot is described to illustrate the steps of this methodology. Real experiments with the autonomous robot ROMEO 4R demonstrate efficiency of the designed fuzzy controller embedded into a stand-alone card based on a fixed-point DSP from Texas Instruments. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) emissions play an important role in climate change. GHG emissionsconsist of carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4). Other than CO2,N2O and CH4 should be considered because N2O and CH4 have a global warmingpotential (GWP) of 298 and 25, respectively over a 100-year timescale. A majorsource of N2O is denitrification of fertilizer nitrates. A significant proportion ofnitrogen supplied to crops is also ultimately converted to N2O. Global emissionsare particularly high from crops such as maize which are often grown with highfertilizer inputs and cover a large global land area, including in the United States,which leads the world in maize production (including 36.3% of global exports).Estimation of N2O emissions at a regional to global scale can be done with mechanisticagroecosystem simulation models or through the use of simple deterministic, data-basedmodels. Simulation models allow for the coupled analysis of emission response toexogenous factors such as management and climate but often do not generate accurateresults due to the complexity of processes involved in the production of N2O in soils.Simple deterministic models can produce higher accuracy emission estimates but lackthe ability to examine responses to complex patterns in exogenous forcings andconditions. Here, we combine approaches to estimate maize yield and quantify its impacton N2O emissions from croplands in the United States. To estimate the impact ofmaize production on N2O emissions under various scenarios (i.e., different crop,management, fertilizer, and irrigation) from regional to national (with capacityfor global) scales, we developed an integrated large-scale modeling frameworkusing parallel computing. In the modeling framework, we coupled the biophysicalprocess-based model EPIC (Environmental Policy Integrated Climate) with theFarm Energy Analysis Tool (FEAT) which is a deterministic, data-based model. Weestimated N2O emissions from maize areas. Total N2O emissions per ha including N2Odirect, volatilization, and leaching and runoff are 1396 kg CO2e/ha/year and totalN2O emissions per maize yield are 178.8 kg CO2e/Mg grain. Validation resultsindicated that the integrated modeling framework simulated N2O emissions wellwith NSE (Nash-Sutcliff Efficiency) and R2 (Coefficient of Determination) whichare 0.923 and 0.995 respectively. We also tested the performance of the modelingframework using a parallel computing system developed in this study. We comparedsingle-core (serial processing) and 14 cores (28 threads) (parallel processing) computingtime for maize areas (32,900,000 ha) in the United States with 55,808 simulationunits. Serial processing requires 57.2 hours, while using the framework with 14cores requires only 7.4 hours, an approximately 87% reduction in computing time. [ABSTRACT FROM AUTHOR]

Focuses on an update on the New York University (NYU) Ultracomputer developed in Courant University in New York, New York. Capability of Multiple Instruction Multiple Data computer's shared-memory architecture; Development of a prototype hardware; Creation of parallel programs; Creation of a powerful computer systems.