January 2026 monthly summary focusing on enhancements to data traceability and profiling reliability for DUNE/larnd-sim. Key improvements include HDF5 dynamic versioning with embedded Git metadata and a robust NCU profiling CLI.

December 2025 monthly summary for DUNE/larnd-sim: Implemented GPU Memory-Optimized Light LUT with FP16 precision to reduce GPU memory usage and improve simulation throughput. Added configuration options to enable FP16 precision for selected arrays while preserving FP32 for others, enabling a precise performance/accuracy trade-off. Targeted configurations include 2x2, FSD, and ND-LAr to enable larger simulations within existing hardware footprints. No major bugs fixed this month; changes are isolated to LUT implementation and its configuration layer, evidenced by a focused commit set.

Concise monthly summary for 2025-11 focused on business value and technical achievements in the DUNE/larnd-sim repository. This month delivered automation enhancements for CI and Perlmutter workflow, alignment of calibration data, and improvements to traceability and robustness to support reliable simulations and faster iteration cycles.

Oct 2025 monthly summary for DUNE/larnd-sim: Focused on CI workflow hygiene and stability. Key action: removed a duplicate pm-login.yaml CI workflow to prevent divergence with the develop branch and reduce merge conflicts, enhancing PR throughput and CI reliability. This maintenance work improves build stability and accelerates development velocity by eliminating duplicative configuration and potential merge issues across branches.

September 2025 (DUNE/larnd-sim) monthly summary focusing on feature delivery, reliability, and packaging improvements that enable scalable testing and faster CI. Key features delivered: - Lar_runner: Added a compare subcommand to compare two larnd-sim output files, integrating diff_files.py and exposing lar_runner.py and diff_files.py as executables. This enables deterministic checks of identical outputs when using the same random seed. Commit: 33e975492be5ad6f6e4bf95acff5325391849179. - Lar_runner: Added a performance analysis reporting (nsys) subcommand to generate detailed stats profiles and CSV reports with relative runtimes for NVTX ranges, with optional color support for improved visualization. Commit: 2b0fa41863f4c38bf19d65cf7bb8e3cd3f75298f. - Project packaging and CI improvements: modernized packaging with pyproject.toml, updated setup.py, removed requirements.txt, added linting/type-checking tooling, fixed package discovery for non-editable installs, and introduced a Perlmutter self-hosted runner workflow. Commits include 5753402b778aa4b43cdc0d8040322a672404853e, e09879953d8b6b8efcf32abf110398d9d3224e08, 681740c36ebce5704348b64623ffb5b8cce5e70c, c153f73bc9866fe5038ed472cd6a9a73960f331c, a4513bc3b654376962b9e23ab45c685720d914f1. Major bugs fixed: - Fixed issue when performing a regular (non-editable) install by updating packaging logic to ensure reliable discovery and installation. Commit: a4513bc3b654376962b9e23ab45c685720d914f1. Overall impact and accomplishments: - Improved testability and reproducibility: The compare subcommand enables deterministic verification of outputs across runs with the same seed, reducing QA time. - Enhanced performance diagnostics: The new report subcommand provides actionable nsys-based insights and visualization-friendly outputs for faster performance tuning. - Stronger packaging discipline and CI reliability: Pyproject-based packaging, linting/type-checking tooling, and Perlmutter CI support reduce maintenance burden and improve build stability across environments and GPU-backed runners. - Broader accessibility and maintainability: Simplified installations through streamlined packaging and CI, improved package discovery, and modernized metadata support. Technologies/skills demonstrated: - Python packaging: pyproject.toml, setup.py, non-editable install handling, packaging metadata. - CLI tooling and executables: Exposing lar_runner and diff_files.py as command-line tools via setup. - Diff-based testing: File content comparison for deterministic validation. - Performance tooling: NVTX ranges, nsys profiling, colorized output for visualization. - CI/CD: GitHub Actions workflows, Perlmutter self-hosted runner setup. - Code quality: Linting and type-checking tooling integration.

Monthly performance summary for Aug 2025 focused on delivering impactful features, targeted improvements, and tooling enhancements for larnd-sim. The work emphasizes throughput, realism of simulations, and better operational workflows with a new CLI. No major bugs fixed were reported in this period; the emphasis was on feature delivery and tooling improvements that enable faster validation and deployment.

July 2025 — DUNE/larnd-sim monthly summary focusing on key business value and technical achievements. Key features delivered: - Simulation performance optimizations across the pipeline: zero-suppress waveform processing, pixel index mapping, SiPM response calculations, and scintillation modeling. Notable commits include: 5193055ee0ca1fb0860305884b0a0199939a1d2c; e7041a4e2ece7fe19afe4b0a94265344d56ca09e; 2339077100a7aaaa64a3214f6c53826be08035c2; 0afe2da340afb07f0b30847371918544b1bb24be; cca1202607c712897564200eb1031eb4feebadbc; eabb910f7308ced758bfc3da7a61f1f17c4f927e. - Output validation tooling: new Python script diff_files.py to validate larnd-sim output files for exact or approximate equality, aiding reproducibility and debugging. Major bugs fixed: - Stability and correctness improvements from refactoring zero_suppress_waveform_truth and isolating sipm_response from the light_det_response kernel; cleaned up sipm_response changes; improved integration by pulling scintillation_model function call out of the kernel; added pixel_index_map updates to reduce downstream mismatches. Overall impact and accomplishments: - Faster, more reliable simulations enabling larger-scale studies; improved reproducibility via automated output validation; clearer, more maintainable code paths and kernel modularity that reduce risk in future changes. Technologies/skills demonstrated: - C++/CUDA kernel refactors and performance tuning; Python scripting for validation tooling; emphasis on maintainability, reproducibility, and rigorous validation."

May 2025 Monthly Summary: larnd-sim focused on fidelity and efficiency improvements. Key outcomes include enhancements to FSD detector simulation configuration (noise data integration, timing window adjustments, and light_tick_size) to improve realism and timing precision; a numerical robustness fix for digit_samples to reduce floating-point errors and ensure accurate waveform tick counts; and the introduction of optional HDF5 dataset compression for simulate_pixels.py outputs via CLI with a safe default (compression flag default off, and excluding the 'packets' dataset). These changes collectively improve simulation fidelity, reduce data size and I/O overhead, and bolster reproducibility across runs.

March 2025: Delivered multi-repo enhancements to improve the realism, reliability, and maintainability of the DUNE simulation stack. Implemented FSD/CORSIKA workflow improvements, stabilized CORSIKA environment, standardized detector and MC/data configurations, and addressed a segmentation fault in the FSD world volume. These changes accelerate onboarding, reduce operational toil, and enable more accurate cosmic-ray simulations with coherent event IDs across larnd-sim and edep-sim workflows.

February 2025: Delivered a major milestone in the DUNE/2x2_sim pipeline by integrating FSD geometry for cosmic-ray simulations, streamlining CORSIKA run workflows, and improving data portability and dependency management. These changes reduce setup time, improve reproducibility, and enhance cross-system compatibility, directly supporting faster scientific iterations and higher-quality simulation data.

January 2025 monthly summary for DUNE/2x2_sim focused on stabilizing the data conversion path and preserving data integrity in downstream simulations. The primary deliverable was a bug fix in the CORSIKA converter. There were no new feature deliveries this month; instead, effort was directed at reducing edge-case data entries and improving maintainability of the conversion workflow.

December 2024 monthly summary for DUNE/2x2_sim focusing on configuration discipline, script reliability, and workflow alignment to enable reproducible simulations and smoother collaboration.

Month: 2024-11 — Key deliverables in DUNE/2x2_sim include end-to-end CORSIKA integration with a C++ converter to ROOT for edep-sim, containerized workflow, and repository hygiene improvements, with install/run scripts and standardized container loading. Major fixes: updated .gitignore to ignore CORSIKA configs and executables to prevent artifacts from being committed. Impact: provides a reproducible, scalable simulation pipeline from CORSIKA to ROOT/edep-sim, enabling faster experiments, easier onboarding, and safer code state. Technologies demonstrated include C++, ROOT, containerization, Makefile scripting, and git hygiene.

July 2024 - DUNE/larnd-sim: Strengthened validation, testing, and data integrity to improve reliability of simulation outputs. Delivered three validation-focused features with accompanying tests, established automated checks against reference data, and reinforced robustness against invalid inputs. This work reduces debugging time and increases confidence in results for downstream analyses and QA workflows.