Annual Research Proposal

Title:
High-Performance GPU Acceleration and Algorithmic Enhancement of Wave Propagation Engines for Full Waveform Inversion and Reverse Time Migration

1. Background and Motivation

Accurate subsurface imaging is central to hydrocarbon exploration, CO₂ sequestration, geothermal development, and reservoir characterization. Advanced imaging workflows such as Full Waveform Inversion (FWI) and Reverse Time Migration (RTM) rely on repeated large-scale numerical solutions of the acoustic or elastic wave equation. These computations are memory-bandwidth intensive, stencil-dominated, and require efficient checkpointing and adjoint-state implementations.

Modern production codes depend heavily on GPU acceleration using frameworks such as CUDA to achieve acceptable turnaround times. However, performance scalability, memory efficiency, and algorithmic robustness remain limiting factors, especially for:

High-order finite-difference schemes

Elastic/anisotropic formulations

Multi-GPU scaling

Memory-intensive adjoint calculations

Coming from a reservoir simulation background (PDE-based modeling, physics-driven computation), I am well positioned to bridge physical modeling insight with performance engineering to strengthen the team’s wave propagation framework.

2. Objectives
2.1 Primary Technical Objectives

Enhance CUDA GPU proficiency to production-level performance engineering.

Optimize the wave propagation kernel (acoustic/elastic) for:

Memory bandwidth efficiency

Shared memory tiling strategies

Register optimization

Occupancy balancing

Improve RTM and FWI computational performance by:

Reducing memory footprint of wavefield storage

Implementing efficient checkpointing strategies

Improving adjoint-state gradient computation

Develop scalable multi-GPU support using domain decomposition.

Strengthen theoretical foundation in seismic imaging and inversion to align algorithmic design with physical modeling requirements.

3. Technical Scope of Work
3.1 Wave Propagation Engine Optimization

Tasks:

Profile existing kernels (Nsight Compute / Nsight Systems).

Quantify arithmetic intensity vs memory throughput.

Analyze stencil memory access patterns (7-point, 9-point, 27-point).

Implement and benchmark:

Shared memory tiling

Register blocking

Loop unrolling

Asynchronous memory copies (cp.async where applicable)

Evaluate mixed precision strategies.

Deliverables:

Performance report with roofline analysis.

≥20–40% speedup of baseline stencil kernel (target).

3.2 Reverse Time Migration Improvements

RTM requires forward modeling + backward propagation + cross-correlation imaging condition.

Focus Areas:

Memory reduction strategies:

Optimal checkpointing (Revolve-style)

Boundary wavefield saving

Imaging condition optimization

GPU memory pressure reduction for large 3D grids

I/O reduction strategies

Deliverables:

Optimized RTM workflow benchmarked on production model.

Documentation of memory-performance trade-offs.

3.3 Full Waveform Inversion Enhancement

FWI requires gradient computation via adjoint-state method and iterative optimization.

Focus Areas:

Efficient gradient accumulation kernels

Preconditioning strategies

Multi-scale frequency continuation workflow

Investigate Hessian approximations

Explore L-BFGS vs nonlinear conjugate gradient performance

Deliverables:

End-to-end FWI prototype with improved GPU scaling.

Convergence study on synthetic dataset.

3.4 Multi-GPU Scaling

Tasks:

Domain decomposition (spatial partitioning)

Halo exchange optimization

Overlap communication with computation

Investigate NVLink vs PCIe performance effects

Deliverables:

Strong and weak scaling study

Multi-GPU performance report

4. Training and Skill Development Plan
4.1 CUDA and Performance Engineering

Advanced CUDA memory hierarchy usage

Warp-level primitives

Asynchronous execution pipelines

Occupancy modeling

Profiling and roofline analysis

4.2 Geophysical Theory Deepening

Topics to strengthen:

Elastic wave equation formulation

Anisotropy (VTI/TTI)

Absorbing boundary conditions (PML/CPML)

Imaging conditions

Inversion regularization

5. Methodology

Baseline benchmarking of current code.

Isolate computational hotspots.

Apply systematic optimization:

Memory access optimization first

Then compute optimization

Validate physics accuracy after each modification.

Compare numerical dispersion and stability before/after optimization.

Document reproducible benchmarking pipeline.

6. Milestones and Timeline (12 Months)
Q1

Baseline profiling

CUDA advanced training

Implement optimized stencil kernel

Deliver kernel benchmark report

Q2

RTM memory optimization

Checkpointing implementation

Imaging condition optimization

Q3

FWI gradient optimization

Multi-scale inversion implementation

Initial multi-GPU prototype

Q4

Multi-GPU scaling experiments

Full system integration

Documentation and internal presentation

Draft journal/conference paper

7. Expected Outcomes

Significant reduction in wave propagation runtime

Reduced memory footprint for RTM/FWI

Scalable multi-GPU implementation

Improved inversion convergence performance

Strengthened integration between physics modeling and HPC engineering

8. Strategic Value to the Team

Accelerated imaging turnaround time

Reduced compute cost per survey

Stronger in-house GPU performance expertise

Improved competitiveness for advanced imaging projects

9. Long-Term Vision

Establish a high-performance, modular GPU wave propagation framework capable of:

Acoustic, elastic, and anisotropic modeling

Scalable 3D production workloads

Integration with reservoir simulation workflows

Potential coupling between seismic inversion and flow simulation

My background in reservoir simulation enables future cross-disciplinary integration between seismic imaging outputs and dynamic reservoir modeling.

If you would like, I can also:

Convert this into a shorter executive summary version (1–2 pages).

Make it more academic (for university submission).

Make it more corporate (for internal oil & gas R&D reporting).

Add measurable KPIs and budget estimates.
