Lower precision for higher accuracy: Precision and resolution exploration for shallow water equations

Targett, James Stanley; Niu, Xinyu; Russell, Francis P.; Luk, Wayne; Jeffress, Stephen; Dueben, Peter

doi:10.1109/fpt.2015.7393152

Cited by 5 publications

(3 citation statements)

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“…Savings due to increased performance or reduced power consumption could be reinvested into larger computing systems and used to allow simulations at higher spatial resolution. To this end, a number of studies have already been performed on the use of reduced floating-point precision in atmospheric applications that have used ancestors of the rpe tool that is presented in this paper Düben et al, 2015b, a;Thornes et al, 2017). Results show that numerical precision can be reduced significantly for the applications that have been considered.…”

Section: Introductionmentioning

confidence: 99%

rpe v5: an emulator for reduced floating-point precision in large numerical simulations

Dawson

Dueben

2017

Geosci. Model Dev.

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Abstract. This paper describes the rpe (reduced-precision emulator) library which has the capability to emulate the use of arbitrary reduced floating-point precision within large numerical models written in Fortran. The rpe software allows model developers to test how reduced floating-point precision affects the result of their simulations without having to make extensive code changes or port the model onto specialized hardware. The software can be used to identify parts of a program that are problematic for numerical precision and to guide changes to the program to allow a stronger reduction in precision.The development of rpe was motivated by the strong demand for more computing power. If numerical precision can be reduced for an application under consideration while still achieving results of acceptable quality, computational cost can be reduced, since a reduction in numerical precision may allow an increase in performance or a reduction in power consumption. For simulations with weather and climate models, savings due to a reduction in precision could be reinvested to allow model simulations at higher spatial resolution or complexity, or to increase the number of ensemble members to improve predictions. rpe was developed with a particular focus on the community of weather and climate modelling, but the software could be used with numerical simulations from other domains.

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Section: Introductionmentioning

confidence: 99%

rpe v5: an emulator for reduced floating-point precision in large numerical simulations

Dawson

Dueben

2017

Geosci. Model Dev.

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“…The equations can be derived from the Navier-Stokes equations by assuming the height of the fluid is negligible compared to its width, and integrating vertically [13]. We use the form of the shallow water equations shown in [8].…”

Section: Approach Overviewmentioning

confidence: 99%

“…The shallow water model is frequently used to test new techniques for use in weather and climate models [2], [3], [4], [5]. The shallow water model we investigate is a finite difference based model set up to model the wind forced double Munk gyre test case [6], [7] which has features which resemble the Subtropical and Subpolar Gyres of the North Atlantic Surface circulation [8]. This involves a 2D stencil computation with a grid on the order of hundreds by hundreds of cells integrated for an extended period of time.…”

Section: Introductionmentioning

confidence: 99%

Validating optimisations for chaotic simulations

Targett

Dueben

Luk

2017

2017 27th International Conference on Field Programmable Logic and Applications (FPL)

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Abstract-It is non-trivial to optimise computations of chaotic systems since slightly perturbed simulations diverge exponentially over time due to the well-known butterfly effect if bit-reproducible results are not achieved. Therefore, two model setups that show the same quality in the representation of a chaotic system will show uncorrelated behaviour if integrated long enough, hence it is challenging to check whether a given optimisation degrades model quality. Most models in computational fluid dynamics show chaotic behaviour. In this paper we focus on models of atmosphere and ocean that are vital for predictions of future weather and climate. Since forecast quality is usually limited by the available computational power, optimisation is highly desirable. We describe a new method for accepting or rejecting an optimised implementation of a reconfigurable design to simulate dynamics of a chaotic system. We apply this method to optimise numerical precision to a minimal level of stencil computations that can be used in an idealised ocean model, and show the performance improvements gained on an FPGA. The proposed method enables precision reduction for the FPGA so that it computes up to 9 times faster with 6 times lower energy consumption than an implementation on the same device with double precision arithmetic, while ensuring the optimised design to have acceptable numerical behaviour.

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High performance reconfigurable computing for numerical simulation and deep learning

et al. 2020

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Lower precision for higher accuracy: Precision and resolution exploration for shallow water equations

Cited by 5 publications

References 10 publications

rpe v5: an emulator for reduced floating-point precision in large numerical simulations

rpe v5: an emulator for reduced floating-point precision in large numerical simulations

Validating optimisations for chaotic simulations

High performance reconfigurable computing for numerical simulation and deep learning

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