Toward GPU-Resident Climate Models: A Feasibility Study on Lossy Compression for the Spherical Harmonic Transform's Communication Bottleneck

The study demonstrates that GPU-resident lossy compression using ZFP at 16 bits per value effectively mitigates the communication bottleneck in Spherical Harmonic Transforms (SHT) for climate models, matching the speedup of float16 truncation while delivering a 1600× lower mean relative error ($2.5 \times 10^{-7}$ vs $4 \times 10^{-4}$).

Key findings include: Performance: ZFP rate-16 reduces communication time comparably to float16 but with significantly higher accuracy, establishing a superior accuracy-performance trade-off. Hardware Necessity: CPU-based compression is non-viable due to high overhead; GPU-resident processing is essential to avoid costly host-device memory transfers and achieve net performance gains. * Scalability: Compression overhead becomes negligible relative to communication savings at 36–100 nodes, confirming feasibility for operational NWP deployments like the ECMWF IFS as they migrate to GPU architectures.

This study targets a central obstacle in moving operational pseudospectral atmospheric models, such as the ECMWF Integrated Forecasting System, from today’s CPU-dominant production environment to GPU-resident implementations. The Spherical Harmonic Transform is a core component of these models: each timestep requires both forward and inverse SHTs, and both passes are tightly coupled to global communication across compute ranks. On GPU systems, that communication can become a dominant bottleneck because large transform payloads must be exchanged, redistributed, and synchronized across devices or nodes. The paper therefore frames GPU-resident climate modeling as a feasibility problem for reducing SHT communication cost while preserving the numerical fidelity required for operational forecasting.

Its key contribution is a feasibility study of lossy compression applied to the communication traffic associated with the SHT. Rather than treating compression as merely a storage or archival technique, the work examines it as a systems-level optimization for the transform pipeline: can the spectral or intermediate transform data exchanged between ranks be compressed with controlled numerical error, and will the resulting accuracy loss remain acceptable for a production-grade atmospheric model? The study evaluates the tradeoff among bandwidth reduction, numerical stability, and model fidelity, providing a practical framework for integrating numerically aware compression into a pseudospectral forecast system.

This matters because accelerating climate and weather models on GPUs is not simply a matter of porting CPU kernels to faster hardware; it also requires rethinking data movement at scale. If lossy compression can meaningfully reduce SHT communication without degrading forecast quality, it could make GPU-resident implementations more scalable, energy-efficient, and viable for high-resolution operational use. More broadly, the work offers a useful template for addressing communication bottlenecks in other global spectral, transform-based, or distributed scientific codes where data movement, not raw compute, limits performance.