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gprMax/gprMax: v.3.1.4 (Big Smoke)

Authors: Craig Warren; John Hartley; Øystein Bjørndal; Antonis Giannopoulos; Tagussan; Rajath Kumar; Angelisd;

gprMax/gprMax: v.3.1.4 (Big Smoke)

Abstract

This is the v.3.1.4, codenamed Big Smoke, release of gprMax. Version 3.1.4 contains general usability and performance improvements, as well as the following specific enhancements and bug fixes since v.3.1.4: Overhaul of snapshot writing: it is now possible to use snapshots in the GPU-based solver mode; the performance of snapshot writing when using the CPU-based solver has been improved (pull request #161 ) Speed up for writing geometry files (pull request #138 ) HDF5 output file attributes dx,dy,dz to dx_dy_dz and nx,ny,nz to nx_ny_nz Added checks for correct source polarisation in 2D modes, & explicit setting of PEC boundaries for invariant direction in 2D modes. Remove setting of magnetic properties for plates (faces). When cells edges or faces are specified only electric materials properties are used. Moved (and updated) memory estimate function, now attached to Grid class. v.3.1 continues our whisky-based naming, and is also a reference to the cities of Edinburgh (Scotland) and San Francisco (USA). Why? Because the development of v.3.1 was funded, through a research project, by Google. The most significant feature of this release is the ability for simulations to utilise general-purpose computing using graphics processing units (GPGPU). We have used NVIDIA's Compute-Unified Device Architecture (CUDA). Our testing on both consumer and data centre NVIDIA GPU cards has shown dramatic performance increases over our parallelised CPU (OpenMP) implementation. You can read about how to use the GPU functionality and find all the features of gprMax described in detail in the User Guide (http://docs.gprmax.com) Please report any bugs with code via Issues on GitHub. For general help and questions about using gprMax visit our Google Group forum (http://www.gprmax.com/forum.shtml)

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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