Scalability is a critical factor in High-Performance Computing (HPC), where optimizing resource usage has a direct impact on cost-effectiveness and time-efficiency. This report presents a strong scaling performance study of the Albany Land Ice (ALI) code across different HPC architectures, towards determining the best configuration to use when running large-scale simulation ensembles.
Climate impacts have broad economic, health, political, and national security ramifications. Societally relevant impacts are typically farther downstream, are the product of multiple interacting processes, and can arise over small regions and timeframes because their sources are short-term and localized. Short-term forcings (as can be seen in volcanic eruptions, climatic tipping points (e.g., the collapse of rainforests or the disappearance of sea ice), or in increasingly plausible climate interventions) fundamentally possess low signal-to-noise and could benefit from accounting for the multiple conditional processes through which a downstream impact arises. Under the Grand Challenge LDRD CLDERA (CLimate impacts: Discovering Etiology thRough pAthways), we have developed tools to enable downstream impact attribution from geographically and temporally localized source forcings in the climate. CLDERA developed methods that can distinguish how a localized source drives the climate system to respond with particular impacts. The how is embodied in pathways – the spatio-temporally evolving chain of physical processes that connects a source to a series of increasingly distant impacts. Novel analytic methods in pursuit of downstream impact attribution were developed and demonstrated on simulations and observations of the 1991 eruption of Mt. Pinatubo in the Philippines. As described within this report we have • developed stratospheric expertise and aerosol modeling capabilities in E3SM, • created original methods to detect and model pathways from source-to-impact, and • advanced climate attribution through novel methods, cases, and approaches. Further, CLDERA developed a tiered verification process consisting of controlled datasets to prototype, verify, and refine the original method development. CLDERA increased Sandia’s footprint in the climate analytics community and developed new climate collaborations whilst also creating a cadre of climate analysts at Sandia. The products from CLDERA have been extensive with a total of 9 journal articles published, 12 articles submitted and under review, and an additional 8 articles in preparation. We have produced 1750 simulated years and developed 9 code-bases. This report details these accomplishments and serves as a summary of the work completed during the CLDERA Grand Challenge.
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Journal of Computational and Applied Mathematics
Accurate simulation of the evolution of polar ice-sheets requires a massive amount of computational power. In order to take advantage of the newest generation of supercomputing clusters, the Albany Land Ice code has been modernized for performance portability across a variety of parallel architectures, with a focus on enabling end-to-end GPU capability. Albany uses a multigrid preconditioning approach for solving linear systems via performance portable smoothers from the Trilinos package Ifpack2. Since the Albany Land Ice code is constantly evolving and both Albany and Trilinos are in constant development, it is likely that the optimal choice of solver parameters will change over time. It is therefore critical to have an automatic performance tuning framework to ensure that the best possible performance is maintained. Toward this effect, we have developed an automatic performance tuning framework to determine the best fine- and coarse-grid smoothing algorithms and parameters. We treat the underlying performance model of the linear solve as a black box and use the python-based GPTune Bayesian optimization library to determine the optimal smoother choice and parameters. Using this approach, we have found smoothers and their corresponding parameters that result in, on average, 1.2 times faster, and up to 1.5 times faster solve-times than our manually-tuned parameters. We also show that the proposed auto-tuning approach produces reliably better parameters than naive black box optimization techniques like random search for a given function evaluation budget. By implementing our tuning framework in the Python-based workflow management tool parsl, we also ensure that we efficiently use available computing resources during the tuning process and avoid unnecessary long wait times in computing cluster job queues.
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