This repository provides solver implementations, analysis scripts, and result figures for research on workflow mapping and scheduling in heterogeneous High Performance Computing (HPC) systems.
The project builds on the Standard Task Graph (STG) benchmark suite (Kasahara et al., Waseda University) by converting STG task graphs into structured JSON workflow representations and benchmarking six scheduling solvers across multiple graph sizes and system configurations.
| Dataset | DOI | Contents |
|---|---|---|
| STG JSON Input Graphs | 10.5281/zenodo.18927122 | 540 STG workflow instances (rnc50/100/300, homo/hetero) in JSON format |
| Multi-Solver Benchmark Results | 10.5281/zenodo.20419279 | 6,480 JSON result files from 6 solvers x 3 graph sizes x 2 system modes x 180 instances |
grapheonrl-benchmark/
+-- stg_to_json_dataset/ Input graph conversion tools and system configurations
+-- stg_to_json_benchmarks/ Solver implementations (MILP, CP-SAT, HEFT)
+-- benchmark_results/ Figure generation script and all analysis figures
| +-- generate_figures.py
| +-- figures/ 21 PDF + 21 PNG figures + summary_stats.csv
+-- README.md
| Solver | Class | Implementation |
|---|---|---|
| MILP (PuLP) | Exact | stg_to_json_benchmarks/milp_solver.py |
| MILP (Gurobi) | Exact | stg_to_json_benchmarks/milp_solver_gurobi.py |
| CP-SAT | Exact | stg_to_json_benchmarks/cp_sat_solver.py |
| HEFT | Heuristic | stg_to_json_benchmarks/heft_solver.py |
| GNNRL (self) | Learned | GNN-RL model (separate training repo) |
| GNNRL (teacher) | Learned | GNN-RL model with teacher guidance (separate training repo) |
All runs are Small Scale Benchmark Tests in Edge Device (Phase I), executed on an Intel Core i5-1145G7 edge device (4 cores / 8 threads, 15 W TDP, 16 GB RAM, Ubuntu 22.04.5 LTS).
| Graph size | Tasks | System modes | Instances per cell | Total per solver |
|---|---|---|---|---|
| rnc50 | 50 | homo (3-node), hetero (8-node) | 180 | 360 |
| rnc100 | 100 | homo (3-node), hetero (8-node) | 180 | 360 |
| rnc300 | 300 | homo (3-node), hetero (8-node) | 180 | 360 |
Total: 6 solvers x 6 cells x 180 instances = 6,480 result files
Phase II cluster-based validation on real HPC infrastructure will be published as a separate companion dataset.
Run a solver (example: HEFT on one workflow):
cd stg_to_json_benchmarks
python heft_solver.py --input workflow.json --system hetero_8node.jsonRegenerate all figures from the raw Zenodo data:
cd benchmark_results
# Place main_results/ folder (extracted from benchmark_solver_results_main.zip) here
python generate_figures.pySee benchmark_results/README.md for full reproduction instructions.
If you use this repository or the associated datasets, please cite:
Input workflow graphs (STG JSON dataset):
@dataset{Sharma2026STGDataset,
author = {Sharma, Aasish Kumar and Kunkel, Julian Martin},
title = {Standard Task Graph ({STG}) Dataset With {JSON} Conversions
for Workflow Scheduling in Heterogeneous High Performance
Computing ({HPC}) Systems},
year = {2026},
publisher = {Zenodo},
doi = {10.5281/zenodo.18927122},
url = {https://doi.org/10.5281/zenodo.18927122}
}Benchmark solver results:
@dataset{Sharma2026BenchmarkResults,
author = {Sharma, Aasish Kumar and Kunkel, Julian Martin},
title = {Standard Task Graph ({STG}) Multi-Solver Benchmark Results
for Workflow Scheduling in Heterogeneous High Performance
Computing ({HPC}) Systems},
year = {2026},
publisher = {Zenodo},
doi = {10.5281/zenodo.20419279},
url = {https://doi.org/10.5281/zenodo.20419279}
}Input workflow graphs are derived from the Standard Task Graph (STG) benchmark suite:
Hiroshi Kasahara, Waseda University https://www.kasahara.cs.waseda.ac.jp/schedule/stgarc_e.html
| Author | ORCID |
|---|---|
| Aasish Kumar Sharma | 0000-0002-7514-2340 |
| Julian Martin Kunkel | 0000-0002-6915-1179 |
University of Gottingen / GWDG, Germany