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GPU performance analysis for viscoacoustic wave equations using fast stencil computation from the symbolic specification
Seismic forward modeling is a computationally and data-intensive stage in the seismic processing workflow. By profiling the kernels of seismic forward modeling algorithms, it was observed that they need to access a wide variety of memory locations, in addition to the computational cost of performing...
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Published in: | The Journal of supercomputing 2023-08, Vol.79 (12), p.12853-12868 |
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creator | Jesus, Lauê Nogueira, Peterson Speglich, João Boratto, Murilo |
description | Seismic forward modeling is a computationally and data-intensive stage in the seismic processing workflow. By profiling the kernels of seismic forward modeling algorithms, it was observed that they need to access a wide variety of memory locations, in addition to the computational cost of performing floating-point operations for the numerical solution of wave equations. In this context, the Roofline model was used to analyze six representative computing kernels in seismic modeling on GPU environment to indicate bottlenecks in the performance and suggest improvements of these wave equation propagators. Based on this, six viscoacoustic equations were implemented using the Devito tool. Experimental data have shown that optimizations in increasing data reuse and decreasing off-chip memory traffic can significantly improve performance. |
doi_str_mv | 10.1007/s11227-023-05178-3 |
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subjects | Algorithms Chips (memory devices) Compilers Computer memory Computer Science Environment models Floating point arithmetic Interpreters Kernels Mathematical models Performance enhancement Processor Architectures Programming Languages Wave equations Workflow |
title | GPU performance analysis for viscoacoustic wave equations using fast stencil computation from the symbolic specification |
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