AutonomousOED

This repository contains the scripts used to reproduce the performance benchmarks and (with access to the large CSZ dataset) the greedy sensor-selection application described in paper: "Sensor Placement for Tsunami Early Warning via Large-Scale Bayesian Optimal Experimental Design."

Repository layout

• create_test_mat.py: create a synthetic 2D-chunked HDF5 matrix for scaling benchmarks.
• single_gpu_benchmark.py: single-GPU benchmark for naive vs Schur-update formulations.
• scaling_benchmark.py: multi-GPU benchmark with overlapped POSIX I/O + compute.
• run_scaling.sh: Slurm wrapper to run both strong + weak scaling.
• select_sensors.py: MPI greedy selection on an HDF5-backed CSZ (K) matrix (real dataset required).
• oed_hist.py: generate random-configuration baselines and (optionally) plot the histogram.

Installation

Create an environment and install dependencies:

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

For parallel file systems, it is recommended to disable HDF5 file locking:

export HDF5_USE_FILE_LOCKING=FALSE

Task 1: Generate dummy data (for scaling benchmarks)

python create_test_mat.py --file dummy_data.h5 --total_candidates 600 --Nt 420 --precision double

Task 2: Single-GPU benchmark (Figure 2)

python single_gpu_benchmark.py --max_budget 400 --Nt 420 --file ablation_results_420_single.csv

The output CSV contains peak memory and time-per-iteration for:

• Naive formulation (out-of-place / in-place)
• Schur-update formulation (out-of-place / strict in-place)

Task 3: Multi-GPU scaling benchmarks (Figure 3)

This script assumes Slurm and srun. Provide the HDF5 file and the maximum rank count to test.

bash run_scaling.sh --data dummy_data.h5 --max-ranks 512

Outputs are written under scaling_results/ by default (both strong and weak scaling CSVs).

Task 4: Cascadia application (Figure 5, real dataset required)

To download the dataset, visit the HuggingFace repo and follow the instructions there.

With the real HDF5 (K) matrix available on a parallel filesystem, run the greedy selection via MPI. Example:

srun -n 16 python select_sensors.py cascadia_K_matrix.h5 175 \
  --r_sq 1.0 \
  --checkpoint_file cascadia_optimal_results.txt

Task 5: Random baseline + histogram (Figure 4)

Generate random baseline scores (MPI-capable; produces a single-column checkpoint file). This run skips plotting:

python oed_hist.py \
  --h5_store_K cascadia_K_matrix.h5 \
  --total_samples 100 \
  --budget 175 \
  --checkpoint_file random_results.txt \
  --no_plot

Plot histogram comparing random baselines to the greedy optimum:

python oed_hist.py \
  --plot_only \
  --checkpoint_file random_results.txt \
  --optimal_file cascadia_optimal_results.txt \
  --budget 175

Notes for artifact packaging

• Large datasets (e.g., the CSZ (K) matrix) are intentionally not included in this repository.
• All scripts accept file paths via CLI arguments.