Dataset Information

Two MATLAB programs for computing paleo-elevations and burial ages from paired-cosmogenic nuclides.

ABSTRACT: Methods based on cosmic-ray produced nuclides are key to improve our understanding of the Earth surface dynamic. Measuring multiple cosmogenic nuclides in the same rock sample has a great potential, but data interpretation requires rigorous and often complex mathematical treatments. In order to make progress on this topic, this paper presents two easy-to-use MATLAB^© programs permitting to derive information from pairs of cosmogenic nuclides (²⁶Al-¹⁰Be or ¹⁰Be-²¹Ne) measured in rock samples that have been exposed to cosmic rays in the past: "Paleoaltitude.m" and "Burial.m". •"Paleoaltitude.m" computes paleoelevations from a sample whose burial age is known. This new paleoaltimetry method is presented in detail in Blard et al. [1]. The present article also develops the mathematical approach.•Since the elevation of exposure may affect the accuracy of a burial age [1], the second MATLAB© script "Burial.m" is designed to compute burial ages from ²⁶Al-¹⁰Be or ¹⁰Be-²¹Ne pairs, taking into account the position of a sample (elevation and latitude) during its preburial exposure history.

SUBMITTER: Blard PH

PROVIDER: S-EPMC6607321 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Two MATLAB programs for computing paleo-elevations and burial ages from paired-cosmogenic nuclides.

Blard Pierre-Henri PH Lupker Maarten M Rousseau Moïse M Tesson Jim J

MethodsX 20190625

Methods based on cosmic-ray produced nuclides are key to improve our understanding of the Earth surface dynamic. Measuring multiple cosmogenic nuclides in the same rock sample has a great potential, but data interpretation requires rigorous and often complex mathematical treatments. In order to make progress on this topic, this paper presents two easy-to-use MATLAB© programs permitting to derive information from pairs of cosmogenic nuclides (26Al-10Be or 10< ...[more]

PMID: 31309041

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Project description:Numerical solutions of equation-based simulations require computationally intensive tasks such as evaluation of model equations, linear algebra operations and solution of systems of linear equations. The focus in this work is on parallel evaluation of model equations on shared memory systems such as general purpose processors (multi-core CPUs and manycore devices), streaming processors (Graphics Processing Units and Field Programmable Gate Arrays) and heterogeneous systems. The current approaches for evaluation of model equations are reviewed and their capabilities and shortcomings analysed. Since stream computing differs from traditional computing in that the system processes a sequential stream of elements, equations must be transformed into a data structure suitable for both types. The postfix notation expression stacks are recognised as a platform and programming language independent method to describe, store in computer memory and evaluate general systems of differential and algebraic equations of any size. Each mathematical operation and its operands are described by a specially designed data structure, and every equation is transformed into an array of these structures (a Compute Stack). Compute Stacks are evaluated by a stack machine using a Last In First Out queue. The stack machine is implemented in the DAE Tools modelling software in the C99 language using two Application Programming Interface (APIs)/frameworks for parallelism. The Open Multi-Processing (OpenMP) API is used for parallelisation on general purpose processors, and the Open Computing Language (OpenCL) framework is used for parallelisation on streaming processors and heterogeneous systems. The performance of the sequential Compute Stack approach is compared to the direct C++ implementation and to the previous approach that uses evaluation trees. The new approach is 45% slower than the C++ implementation and more than five times faster than the previous one. The OpenMP and OpenCL implementations are tested on three medium-scale models using a multi-core CPU, a discrete GPU, an integrated GPU and heterogeneous computing setups. Execution times are compared and analysed and the advantages of the OpenCL implementation running on a discrete GPU and heterogeneous systems are discussed. It is found that the evaluation of model equations using the parallel OpenCL implementation running on a discrete GPU is up to twelve times faster than the sequential version while the overall simulation speed-up gained is more than three times.

Dataset Information

Two MATLAB programs for computing paleo-elevations and burial ages from paired-cosmogenic nuclides.

Publications

Two MATLAB programs for computing paleo-elevations and burial ages from paired-cosmogenic nuclides.

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