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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Bibliographic Details
Published in:The Journal of supercomputing 2023-08, Vol.79 (12), p.12853-12868
Main Authors: Jesus, Lauê, Nogueira, Peterson, Speglich, João, Boratto, Murilo
Format: Article
Language:English
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
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Summary: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.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-023-05178-3