Skip to content

Latest commit

 

History

History
executable file
·
32 lines (18 loc) · 1.9 KB

README.md

File metadata and controls

executable file
·
32 lines (18 loc) · 1.9 KB

Seismic Full Waveform Inversion with GPU Acceleration

Overview

This is Seismic Full Waveform Inversion (FWI) is a powerful technique used to estimate subsurface parameters by analyzing seismic measurements obtained at the surface. However, due to the large volume of data, complex model sizes, and non-linear iterative procedures involved, numerical computations for FWI are often computationally intensive and time-consuming. This project addresses these challenges by implementing parallel computation techniques with Graphical Processing Units (GPUs) via CUDA to significantly accelerate the FWI process.

Note: This project is an implementation of the research paper described in this paper.

Implementation

  • Host code is written in C++ to manage the overall project structure and coordinate computations.
  • Parallel computation codes are written in CUDA C, a language optimized for GPU processing.

Performance Comparison

The project includes a comprehensive performance evaluation:

  • Comparing CUDA C and OpenMP: The computational time and performance achieved through CUDA C and OpenMP parallel computation are compared to a serial code implementation.

  • Scaling with Model Dimensions: The project demonstrates that as model dimensions increase, the performance improvement is enhanced. It remains nearly constant after reaching a certain threshold.

  • Impressive GPU Performance Gain: In our experiments, we achieved a GPU performance boost of up to 90 times compared to the serial code, underscoring the substantial benefits of GPU acceleration.

Prerequisites

To run this project, you will need the following:

  • NVIDIA GPU with CUDA support.
  • CUDA Toolkit installed (Download CUDA Toolkit).
  • C/C++ Compiler (e.g., nvcc for CUDA code and g++ for CPU code).
  • Git (optional, for cloning the repository).