kaityo256/pycpp-parallel-pipeline

How kaityo256/pycpp-parallel-pipeline works

This repository demonstrates an end-to-end parallel simulation workflow: Python expands a shared YAML configuration into independent job inputs, C++ runs each bond-percolation simulation, and Python aggregates the per-seed results for plotting. The documented cps scheduler distributes generated tasks, while gnuplot renders the summary. The scheduler and plotting implementation are not sampled, so those documented capabilities are included at the directory/script level; their internal relationships are limited to those stated in the README.

Main entry points: generate_inputs.py Input generator · main.cpp Simulation entry · cps CPS scheduler · analyze_results.py Result analyzer

Components 16

Job preparation

YAML configuration input.yaml
Input generator generate_inputs.py
Job input files output
Task list generate_inputs.py

Simulation

Simulation entry cpp/main.cpp
Parameter parser cpp/param
Percolation engine cpp/percolation.hpp
Per-job results output

Parallel execution

CPS scheduler cps
MPI runtime

Results and plotting

Result analyzer analyze_results.py
Summary data output
Plot definition plot.plt
Gnuplot
Result figure fig

Other components

Workflow user

Connections 17

Workflow user → Input generator: runs README.md
YAML configuration → Input generator: configures README.md
Input generator → Job input files: writes inputs generate_inputs.py
Input generator → Task list: writes tasks generate_inputs.py
Task list → CPS scheduler: schedules commands README.md
MPI runtime → CPS scheduler: runs scheduler README.md
CPS scheduler → Simulation entry: dispatches jobs README.md
Job input files → Simulation entry: provides input README.md
Simulation entry → Parameter parser: parses parameters cpp/main.cpp
Simulation entry → Percolation engine: runs task cpp/main.cpp
Percolation engine → Per-job results: writes result cpp/percolation.hpp
Per-job results → Result analyzer: provides results README.md
YAML configuration → Result analyzer: sets aggregation range analyze_results.py
Result analyzer → Summary data: writes statistics analyze_results.py
Summary data → Gnuplot: provides plot data README.md
Plot definition → Gnuplot: configures plot README.md
Gnuplot → Result figure: renders figure README.md

Drawn by GitDiagram on November 9, 2026 from the repository's file tree, README and source files. Read as Markdown ·