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Use of general purpose graphics processing units with MODFLOW.

Joseph D Hughes1, Jeremy T White

  • 1Florida Water Science Center, U.S. Geological Survey, 4446 Pet Lane Suite 108, Lutz, FL 33559.

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PubMed
Summary
This summary is machine-generated.

General-purpose graphics processing units (GPGPUs) can significantly accelerate MODFLOW simulations. A new solver achieves speedups of 2-8x by optimizing calculations and memory transfers between the central processing unit (CPU) and GPGPU.

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Area of Science:

  • Computational hydrogeology
  • Numerical modeling
  • High-performance computing

Background:

  • MODFLOW is a widely used software for simulating groundwater flow.
  • Traditional solvers can be computationally intensive for large-scale simulations.
  • Graphics Processing Units (GPUs) offer potential for accelerating scientific computations.

Purpose of the Study:

  • To develop and evaluate a new solver for MODFLOW utilizing general-purpose graphics processing units (GPGPUs).
  • To assess the performance improvements and identify key factors for achieving speedups.
  • To compare GPGPU performance against central processing unit (CPU) based solutions.

Main Methods:

  • Development of an Unstructured Preconditioned Conjugate Gradient (UPCG) solver.
  • Implementation of various preconditioners (Jacobi, incomplete LU, etc.) and storage schemes (compressed sparse row).
  • Benchmarking simulations on a synthetic heterogeneous unconfined aquifer using both GPGPU and CPU (OpenMP) implementations.

Main Results:

  • GPGPU speedups of 2-8x were achieved compared to the standard MODFLOW preconditioned conjugate gradient (PCG) solver.
  • Key factors for speedup include optimized memory copies, efficient calculation time, high-performance GPGPU hardware, and hybrid CPU-GPGPU approaches for difficult-to-parallelize tasks.
  • GPGPU speedups surpassed parallel CPU speedups for easily parallelizable preconditioners.

Conclusions:

  • GPGPU acceleration is effective for MODFLOW simulations, particularly for large and complex models.
  • Careful optimization of data transfer and computational workload is crucial for maximizing GPGPU performance.
  • The UPCG solver offers a viable approach for significantly reducing simulation runtimes in hydrogeological studies.