Subgrid-Scale Turbulence Closure and 44.01× Speedup on Blackwell SM120: Matrix-Free Fused Triton Kernels for High-Resolution Global Climate Modeling
RadhikaChain Sovereign Infrastructure & Research Lab
Zenodo, 02-11-2026
@misc{padilla_arriaga_2026_23871045,
author={Padilla Arriaga, Axel Domingo},
title={Subgrid-Scale Turbulence Closure and 44.01× Speedup on Blackwell SM120: Matrix-Free Fused Triton Kernels for High-Resolution Global Climate Modeling},
month={nov},
year={2026},
publisher={Zenodo},
doi={10.5281/zenodo.23871045},
url={https://doi.org/10.5281/zenodo.23871045}
}
High-resolution atmospheric modeling frameworks encounter severe memory bottlenecks when evaluating non-linear subgrid turbulence closures across global meshes (such as ERA5 with 103.8 million 3D points). Standard deep learning frameworks (PyTorch Eager) dispatch multiple disjoint kernels, causing effective throughput to drop below 50 GB/s. We present a register-fused, matrix-free subgrid turbulence closure operator in Triton coupling Smagorinsky eddy dissipation, advective shocks, and Coriolis dispersion. On an NVIDIA RTX PRO 6000 Blackwell Server Edition GPU (94.97 GB VRAM, 188 SMs, sm_120), the kernel achieves 0.8597 ms latency versus 37.84 ms for PyTorch Eager—delivering a measured 44.01x end-to-end speedup and sustaining 1,449.14 GB/s of high-bandwidth memory (HBM) throughput. Low-level compiler SASS telemetry confirmed optimal occupancy: 40 registers per thread, zero spills to local memory, and 100% coalesced global memory access.
November 8, 2026 by hgpu
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