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- W3124447350 abstract "Super-Droplet Method (SDM) is a probabilistic Monte-Carlo-type model of particle coagulation process, an alternative to the mean-field formulation of Smoluchowski. SDM as an algorithm has linear computational complexity with respect to the state vector length, the state vector length is constant throughout simulation, and most of the algorithm steps are readily parallelisable. This paper discusses the design and implementation of two number-crunching backends for SDM implemented in PySDM, a new free and open-source Python package for simulating the dynamics of atmospheric aerosol, cloud and rain particles (this https URL). The two backends share their application programming interface (API) but leverage distinct parallelism paradigms, target different hardware, and are built on top of different lower-level routine sets. First offers multi-threaded CPU computations and is based on Numba (using Numpy arrays). Second offers GPU computations and is built on top of ThrustRTC and CURandRTC (and does not use Numpy arrays). The paper puts forward a proposal of the Super-Droplet Kernels API featuring data structures and backend-level routines (computational kernels) suitable for performing the computational steps SDM consists of. Presented discussion covers: data dependencies across steps, parallelisation opportunities, CPU and GPU implementation nuances, and algorithm workflow. Example simulations suitable for validating implementations of the API are presented." @default.
- W3124447350 created "2021-02-01" @default.
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- W3124447350 date "2021-01-15" @default.
- W3124447350 modified "2023-09-27" @default.
- W3124447350 title "Super-Droplet Kernels -- backend-level routines for Monte-Carlo particle coagulation solvers: API proposal with CPU and GPU implementation in Python" @default.
- W3124447350 hasPublicationYear "2021" @default.
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