Parallel Load Balancing Strategies for Ensembles of Stochastic Biochemical Simulations

The evolution of biochemical systems where some chemical species are present with only a small number of molecules, is strongly influenced by discrete and stochastic effects that cannot be accurately captured by continuous and deterministic models. The budding yeast cell cycle provides an excellent example of the need to account for stochastic effects in biochemical reactions. To obtain statistics of the cell cycle progression, a stochastic simulation algorithm must be run thousands of times with different initial conditions and parameter values. In order to manage the computational expense involved, the large ensemble of runs needs to be executed in parallel. The CPU time for each individual task is unknown before execution, so a simple strategy of assigning an equal number of tasks per processor can lead to considerable work imbalances and loss of parallel efficiency. Moreover, deterministic analysis approaches are ill suited for assessing the effectiveness of load balancing algorithms in this context. Biological models often require stochastic simulation. Since generating an ensemble of simulation results is computationally intensive, it is important to make efficient use of computer resources. This paper presents a new probabilistic framework to analyze the performance of dynamic load balancing algorithms when applied to large ensembles of stochastic biochemical simulations. Two particular load balancing strategies (point-to-point and all-redistribution) are discussed in detail. Simulation results with a stochastic budding yeast cell cycle model confirm the theoretical analysis. While this work is motivated by cell cycle modeling, the proposed analysis framework is general and can be directly applied to any ensemble simulation of biological systems where many tasks are mapped onto each processor, and where the individual compute times vary considerably among tasks

A Framework to Analyze the Performance of Load Balancing Schemes for Ensembles of Stochastic Simulations

Ensembles of simulations are employed to estimate the statistics of possible future states of a system, and are widely used in important applications such as climate change and biological modeling. Ensembles of runs can naturally be executed in parallel. However, when the CPU times of individual simulations vary considerably, a simple strategy of assigning an equal number of tasks per processor can lead to serious work imbalances and low parallel efficiency. This paper presents a new probabilistic framework to analyze the performance of dynamic load balancing algorithms for ensembles of simulations where many tasks are mapped onto each processor, and where the individual compute times vary considerably among tasks. Four load balancing strategies are discussed: most-dividing, all-redistribution, random-polling, and neighbor-redistribution. Simulation results with a stochastic budding yeast cell cycle model is consistent with the theoretical analysis. It is especially significant that there is a provable global decrease in load imbalance for the local rebalancing algorithms due to scalability concerns for the global rebalancing algorithms. The overall simulation time is reduced by up to 25%, and the total processor idle time by 85%

A Stochastic Covariance Shrinkage Approach in Ensemble Transform Kalman Filtering

The Ensemble Kalman Filters (EnKF) employ a Monte-Carlo approach to represent covariance information, and are affected by sampling errors in operational settings where the number of model realizations is much smaller than the model state dimension. To alleviate the effects of these errors EnKF relies on model-specific heuristics such as covariance localization, which takes advantage of the spatial locality of correlations among the model variables. This work proposes an approach to alleviate sampling errors that utilizes a locally averaged-in-time dynamics of the model, described in terms of a climatological covariance of the dynamical system. We use this covariance as the target matrix in covariance shrinkage methods, and develop a stochastic covariance shrinkage approach where synthetic ensemble members are drawn to enrich both the ensemble subspace and the ensemble transformation

The atmospheric chemistry box model CAABA/MECCA-3.0

We present version 3.0 of the atmospheric chemistry box model CAABA/MECCA. In addition to a complete update of the rate coefficients to the most recent recommendations, a number of new features have been added: chemistry in multiple aerosol size bins; automatic multiple simulations reaching steady-state conditions; Monte-Carlo simulations with randomly varied rate coefficients within their experimental uncertainties; calculations along Lagrangian trajectories; mercury chemistry; more detailed isoprene chemistry; tagging of isotopically labeled species. Further changes have been implemented to make the code more user-friendly and to facilitate the analysis of the model results. Like earlier versions, CAABA/MECCA-3.0 is a community model published under the GNU General Public License

Multiscale simulations of tropospheric chemistry in the eastern Pacific and on the U.S. West Coast during spring 2002

Regional modeling analysis for the Intercontinental Transport and Chemical Transformation 2002 (ITCT 2K2) experiment over the eastern Pacific and U.S. West Coast is performed using a multiscale modeling system, including the regional tracer model Chemical Weather Forecasting System (CFORS), the Sulfur Transport and Emissions Model 2003 (STEM-2K3) regional chemical transport model, and an off-line coupling with the Model of Ozone and Related Chemical Tracers (MOZART) global chemical transport model. CO regional tracers calculated online in the CFORS model are used to identify aircraft measurement periods with Asian influences. Asian-influenced air masses measured by the National Oceanic and Atmospheric Administration (NOAA) WP-3 aircraft in this experiment are found to have lower ΔAcetone/ΔCO, ΔMethanol /ΔCO, and ΔPropane/ ΔEthyne ratios than air masses influenced by U.S. emissions, reflecting differences in regional emission signals. The Asian air masses in the eastern Pacific are found to usually be well aged (\u3e5 days), to be highly diffused, and to have low NOy levels. Chemical budget analysis is performed for two flights, and the O3 net chemical budgets are found to be negative (net destructive) in the places dominated by Asian influences or clear sites and positive in polluted American air masses. During the trans-Pacific transport, part of gaseous HNO3 was converted to nitrate particle, and this conversion was attributed to NOy decline. Without the aerosol consideration, the model tends to overestimate HNO3 background concentration along the coast region. At the measurement site of Trinidad Head, northern California, high- concentration pollutants are usually associated with calm wind scenarios, implying that the accumulation of local pollutants leads to the high concentration. Seasonal variations are also discussed from April to May for this site. A high-resolution nesting simulation with 12-km horizontal resolution is used to study the WP-3 flight over Los Angeles and surrounding areas. This nested simulation significantly improved the predictions for emitted and secondary generated species. The difference of photochemical behavior between the coarse (60-km) and nesting simulations is discussed and compared with the observation. Copyright 2004 by the American Geophysical Union

Strong constraints on aerosol-cloud interactions from volcanic eruptions.

Aerosols have a potentially large effect on climate, particularly through their interactions with clouds, but the magnitude of this effect is highly uncertain. Large volcanic eruptions produce sulfur dioxide, which in turn produces aerosols; these eruptions thus represent a natural experiment through which to quantify aerosol-cloud interactions. Here we show that the massive 2014-2015 fissure eruption in Holuhraun, Iceland, reduced the size of liquid cloud droplets-consistent with expectations-but had no discernible effect on other cloud properties. The reduction in droplet size led to cloud brightening and global-mean radiative forcing of around -0.2 watts per square metre for September to October 2014. Changes in cloud amount or cloud liquid water path, however, were undetectable, indicating that these indirect effects, and cloud systems in general, are well buffered against aerosol changes. This result will reduce uncertainties in future climate projections, because we are now able to reject results from climate models with an excessive liquid-water-path response