A Compiler-First Planetary Compute Engine: Automatic differentiable and performance(…)
summary
Claims a compiler-stack solution that makes production Earth models end-to-end auto-differentiable and hardware-portable. Uses Enzyme.jl on LLVM and MLIR/StableHLO plus Reactant.jl to differentiate Oceananigans.jl, ISSM, SpeedyWeather.jl and lift kernels to tensor ops. Demonstrates a 4.2x speedup from differentiating after optimization, single-node run time from 272s to 11.5s after tensor optimizations, scaling to ~9,000 GPUs and ~8,000 TPUs with communication and checkpointing passes that produce practical speed and memory wins.
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chapters (25)
- 0:00Planetary compute engine intro
- 0:41Earth systems modeling challenges
- 3:29Model uncertainties and differentiability
- 5:35Climate code maintenance burden
- 6:38Compiler-first automation approach
- 7:19Why automatic differentiation matters
- 9:02Derivatives in black hole imaging
- 9:22Limitations of AD approaches
- 11:48Compiler IR advantages
- 12:09Faster gradients via compiler optimizations
- 13:11Enzyme: Compiler-level AD
- 15:18Post-optimization differentiation impact
- 16:00Sensitivity analysis with shallow water
- 17:01Checkpointing for memory reduction
- 18:03AI chips for climate modeling
- 19:06Climate code structure and acceleration
- 20:52Compiling climate code to AI hardware
- 21:55Detecting structure in LLVM code
- 23:19Tensorizing kernels for AI hardware
- 24:23Climate sims on GPUs and TPUs
- 25:23Weak scaling ocean and atmosphere
- 26:05TPU cluster utilization and memory
- 26:45Distributed execution optimizations
- 28:08Compiler solutions for earth models
- 28:53Q&A: Reactant vs Jax
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