[Paper] Tuning of Vectorization Parameters for Molecular Dynamics Simulations in AutoPas

Source: arXiv - 2512.03565v1

Overview

The paper investigates how to squeeze out every last bit of performance from Molecular Dynamics (MD) simulations by fine‑tuning SIMD (Single‑Instruction‑Multiple‑Data) vectorization inside the AutoPas particle‑simulation library. By experimenting with the order in which particle data are loaded into vector registers, the authors show that runtime‑adaptive choices can dramatically speed up the core force‑calculation kernel—and even reduce energy consumption.

Key Contributions

• Systematic study of vectorization orders for pairwise force calculations in MD, covering a wide range of particle densities and neighbor‑search strategies.
• Extension of AutoPas’ dynamic tuning framework to select the optimal SIMD loading pattern on‑the‑fly, rather than relying on a single static configuration.
• Comprehensive benchmark suite demonstrating up to ~30 % speed‑up (and measurable energy savings) over the previous AutoPas implementation across realistic workloads.
• Insightful analysis of how simulation‑specific parameters (e.g., particle density, cutoff radius, neighbor list algorithm) influence the best vectorization strategy.
• Open‑source integration: the new tuning logic is merged into the publicly available AutoPas codebase, enabling immediate reuse by the community.

Methodology

1. Vectorization Strategies – The authors enumerate several ways to pack particle attributes (positions, velocities, forces) into SIMD registers. The key variable is the interaction order: whether data are loaded per‑particle, per‑neighbor, or using mixed layouts.
2. Parameter Sweep – They run a matrix of experiments varying:
   • Particle density (sparse vs. dense systems)
   • Cutoff radius (affects neighbor list size)
   • Neighbor‑identification algorithm (cell‑list, Verlet list, etc.)
3. Dynamic Tuning Integration – AutoPas already features a runtime autotuner that picks optimal loop schedules and data structures. The authors augment this with a lightweight decision engine that evaluates the current simulation state and switches the SIMD loading order accordingly.
4. Benchmarking – Standard MD benchmarks (Lennard‑Jones fluid, biomolecular systems) are executed on modern x86 CPUs with AVX2/AVX‑512 support. Execution time, CPU cycles, and power draw (via RAPL counters) are recorded.
5. Statistical Validation – Results are averaged over multiple runs, and confidence intervals are reported to ensure the observed gains are not noise.

Results & Findings

• The optimal vectorization order changes as the neighbor list grows; a static choice can be up to 30 % slower.
• Dynamic tuning incurs negligible overhead (<1 % of total runtime) because the decision logic runs only when the simulation parameters cross predefined thresholds.
• Energy measurements show a consistent reduction in Joules per simulated timestep, confirming that faster execution also translates to lower power draw on modern CPUs.

Practical Implications

• For MD developers: Plug‑in the updated AutoPas library and immediately benefit from faster force calculations without rewriting kernels.
• High‑performance computing (HPC) centers: The reduced runtime and power consumption can free up node hours, allowing larger or more detailed simulations on the same hardware budget.
• Software architects: The paper demonstrates a reusable pattern—runtime‑adaptive SIMD ordering—that can be applied to other particle‑based codes (e.g., Smoothed Particle Hydrodynamics, N‑body astrophysics).
• Tooling: The extended autotuner can be combined with existing performance‑monitoring suites (e.g., Intel VTune, LIKWID) to further automate the selection of optimal compile‑time flags (AVX2 vs. AVX‑512).

Limitations & Future Work

• The study is CPU‑centric; GPU vectorization (warp‑level) behaves differently and is not covered.
• Only single‑node performance is evaluated; scaling effects across distributed‑memory clusters remain an open question.
• The decision engine relies on pre‑defined thresholds; a more sophisticated machine‑learning model could adapt to even finer‑grained runtime signals.
• Future research could explore cross‑architecture tuning (ARM SVE, RISC‑V vector extensions) and integrate the approach into other MD frameworks beyond AutoPas.

Authors

• Luis Gall
• Samuel James Newcome
• Fabio Alexander Gratl
• Markus Mühlhäußer
• Manish Kumar Mishra
• Hans-Joachim Bungartz

Paper Information

• arXiv ID: 2512.03565v1
• Categories: cs.DC, cs.CE, cs.PF
• Published: December 3, 2025
• PDF: Download PDF