On the Efficacy of PyTorch for High-Performance Computing: A Case Study in Computational Physics

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This artifact accompanies the paper: “On the Efficacy of PyTorch for High-Performance Computing: A Case Study in Computational Physics” published in Proceedings of the 23rd ACM International Conference on Computing Frontiers (CF ’26), May 19–21, 2026, Catania, Italy. ACM, New York,NY, USA, 10 pages. https://doi.org/10.1145/3801487.3801838 It provides a complete, reproducible evaluation framework for comparing PyTorch against conventional HPC implementations (C++, OpenMP, and SYCL) using a computational physics workload based on the Local Orthogonal Inverse Transform Sampler (LOITS). The artifact is designed with a two-tier philosophy: Interpretability-first: Users can immediately regenerate all figures from precomputed results without requiring recompilation or access to specialized hardware Reproducibility (optional rerun): Full experimental pipelines can be re-executed to collect fresh performance data across CPU and GPU backends (CUDA, HIP, and XPU), subject to hardware availability. ------------------------------------------Key Features - Multi-implementation comparison - PyTorch (CPU, CUDA, HIP, XPU) - Native C++ (baseline) - OpenMP (CPU parallelism) - SYCL (DPC++, AdaptiveCpp across CPU/GPU backends) - Heterogeneous evaluation - Multicore CPUs - NVIDIA, AMD, and Intel GPUs - Unified benchmarking harness - Consistent workload (LOITS sampler) - Standardized CSV outputs - Comparable performance metrics (runtime, scaling, GFLOP/s) - Reproducible plotting pipeline - Strong scaling - Weak scaling - Fixed-resource scaling and breakdowns ------------------------------------------Artifact Workflow The artifact uses a simple `make`-based interface: Quick validation (recommended) make- Regenerates all paper figures from `results/`- Automatically bootstraps from `archived-results/` if needed Full rerun (optional)make rerun-strongmake rerun-weakmake rerun-frs- Rebuilds implementations- Executes experiments- Produces fresh results ------------------------------------------Repository Structure - cpp/ – Native C++ implementation (pybind interface)- omp/ – OpenMP implementation- sycl/ – SYCL implementations (DPC++, AdaptiveCpp, UniSYCL)- pytorch_2dloits.py – PyTorch implementation- archived-results/ – Precomputed results (used by default)- results/ – Generated or rerun results- images/ – Final figures used in the paper ------------------------------------------Reproducibility Notes - The artifact prioritizes rapid validation by shipping archived results.- Re-running experiments requires: - Appropriate toolchains (e.g., DPC++, ROCm, CUDA) - Compatible hardware for GPU backends- Performance results may vary depending on system configuration. ------------------------------------------Main Findings - PyTorch achieves 4–5× reduction in source lines of code compared to HPC C++ implementations - On CPUs, PyTorch reaches ~50–72% of optimized performance- On accelerators, PyTorch significantly outperforms SYCL: - ~5–6× on CUDA - ~15× on HIP - up to ~16× on Intel XPU ------------------------------------------Intended Use This artifact is intended for: - Researchers evaluating programming productivity vs performance- Practitioners exploring Python-based HPC workflows- Developers studying performance portability across heterogeneous systems

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