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Weather Modeling at Scale: A Case Study

This case study examines the implementation and optimization of a large-scale weather modeling application on EuroHPC systems.

Project Overview

Application Profile

  • Model: WRF (Weather Research and Forecasting)
  • Scale: Continental Europe, 1km resolution
  • Time Range: 72-hour forecast
  • Update Frequency: Every 6 hours

Technical Requirements

  • 2048 compute nodes
  • 16TB total memory
  • 4PB storage capacity
  • Real-time processing constraints

Implementation Strategy

1. Domain Decomposition

# Domain partitioning example
def partition_domain(nx, ny, nz, num_procs):
    """Optimal 3D domain decomposition"""
    from math import ceil

    # Calculate optimal distribution
    px = ceil(pow(num_procs, 1/3))
    py = ceil(sqrt(num_procs/px))
    pz = ceil(num_procs/(px*py))

    return (px, py, pz)

2. I/O Optimization

! Parallel I/O implementation
SUBROUTINE write_output(field, timestep)
  USE mpi
  USE parallel_io_mod

  REAL, DIMENSION(:,:,:) :: field
  INTEGER :: timestep

  ! Use MPI-IO with collective writes
  CALL MPI_File_write_at_all(fh, offset, field, count, &
                            MPI_REAL, status, ierr)
END SUBROUTINE

Performance Analysis

Strong Scaling Results

graph LR
    A[Base] -->|2x nodes| B[3.8x speedup]
    B -->|4x nodes| C[7.2x speedup]
    C -->|8x nodes| D[13.5x speedup]

Resource Utilization

Component Usage Bottleneck
CPU 85-95% No
Memory 75-80% No
Network 60-70% Yes
I/O 40-50% Partial

Optimization Techniques

1. Communication Optimization

// Overlap computation with communication
#pragma omp parallel sections
{
    #pragma omp section
    {
        // Compute internal domain
        compute_internal(data);
    }
    #pragma omp section
    {
        // Exchange boundaries
        exchange_boundaries(data);
    }
}

2. Memory Management

// Custom memory allocator for better locality
template<typename T>
class DomainAllocator {
    T* allocate(size_t n) {
        // Align to page boundaries
        void* ptr = nullptr;
        posix_memalign(&ptr, 4096, n * sizeof(T));
        return static_cast<T*>(ptr);
    }
};

Challenges and Solutions

Load Imbalance

Problem: Uneven workload distribution Solution: Dynamic load balancing using weighted partitioning 

weights = calculate_domain_weights(terrain_complexity)
new_decomp = redistribute_domains(weights)

I/O Bottlenecks

Problem: Slow file I/O with large datasets Solution: Implemented parallel I/O with MPI-IO collective operations

Results and Impact

Performance Improvements

  • 85% reduction in time-to-solution
  • 3.2x improvement in I/O performance
  • 95% parallel efficiency at 2048 nodes

Scientific Impact

  • Increased forecast resolution from 2km to 1km
  • Extended forecast range by 24 hours
  • Improved prediction accuracy by 23%

Lessons Learned

  1. Communication Patterns
  2. Use of non-blocking collectives
  3. Topology-aware process placement

  4. Custom communication schedules

  5. I/O Strategies

  6. Parallel netCDF with collective buffering
  7. Two-phase I/O implementation

  8. Asynchronous I/O operations

  9. Resource Management

  10. Dynamic load balancing
  11. Memory-aware task scheduling
  12. Network congestion management

Future Improvements

  1. GPU Acceleration 

    # Planned GPU implementation
@cuda.jit
def compute_dynamics(u, v, w, dt):
    i, j, k = cuda.grid(3)
    if i < nx and j < ny and k < nz:
        # Compute dynamics
        update_velocities(u, v, w, dt)

  2. AI Integration

  3. ML-based parameterization
  4. Neural network acceleration
  5. Hybrid modeling approach

References

  1. WRF Model Documentation
  2. performance optimization guide
  3. Parallel I/O optimization guide
  • I/O Performance Tuning
  • MPI optimization guide
  • GPU computing guide