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This commit introduces a high-performance, backend-agnostic implementation of the
Emanuel convection scheme and a reusable abstraction layer for future components.
Key Changes:
- Added : A universal layer for JAX/NumPy detection,
JIT orchestration, and functional state updates using Numba overloads.
- Added : A pure functional,
JIT-optimized implementation of the Emanuel scheme achieving ~118x speedup
over the original Python version and ~75x over Fortran.
- Refactored to utilize the new V2 implementation.
- Added comprehensive verification and benchmarking suites:
- : Formal parity regression tests.
- : side-by-side comparison of active tendencies.
- : Performance measurement script.
- Added : Detailed technical report on architectural
decisions and optimization results.
The new implementation maintains strict numerical parity (1e-12) with the original
Python code while providing a clear path toward full differentiability via JAX.
Key Changes: - Introduced : A JAX-JIT optimized implementation using and . - Enhanced with JAX-specific optimizations and vectorization. - Refactored to be backend-agnostic. - Achieved perfect bit-wise parity between JAX x64, Numba, and Original implementations. - Updated benchmarking and verification suites to support JAX performance analysis. - Updated with JAX-specific performance and architectural details.
…vection Key Changes: - Implemented in with Pytree-registered containers. - Registered in top-level init for use with . - Finalized with and for GPU/XLA optimization. - Refactored in to be backend-agnostic. - Verified ~550x speedup with Numba CPU and enabled JAX Metal (GPU) path. - Added to verify gradient computation. - Updated with final performance benchmarks on Apple Silicon.
Key Changes: - Refactored into a dual-path JIT implementation (Numba/JAX). - Verified ~3x GPU speedup at 100k columns on Apple M3 Pro. - Fixed to return raw JAX arrays for differentiable tracing. - Added for parity and grad verification. - Added for multi-backend scaling analysis. - Implemented placeholder in for sympl compatibility.
- Remove parallel=True from @njit/@jit_compile across components to fix potential segfaults. - Fix DryConvectiveAdjustment to use Numba-compatible loops and ensure differentiability. - Update SlabSurface to correctly index vertical dimensions for surface extraction and fix TracerArrayConversionError in JAX. - Enhance EmanuelConvectionPythonV3 with transpose bug fixes for diverse input shapes. - Improve JaxBackend and JaxStateContainer to handle non-numeric data and ensure better sympl integration. - Update test_emanuel_optimization.py to support JAX-based state containers. - Add comprehensive benchmarks and optimization tests for DryConvectiveAdjustment, GridScaleCondensation, HeldSuarez, Instellation, and SlabSurface. - Verify 13.8x speedup in Grey GCM benchmark using Numba-optimized path.
- GridScaleCondensation: fix precipitation sign (p_int[k] - p_int[k+1] was negative; reversed to p_int[k+1] - p_int[k]) - BergerSolarInsolation: fix eccentricity formula — zeta coefficients from CAM 3.0 are in radians, not degrees; remove the erroneous * pi/180 conversion that was applied to the whole argument - GrayLongwaveRadiation: move tendency calculation out of @njit kernel so it runs in numpy (float64 throughout); regenerate cached fixtures which previously stored float32 fluxes from the original pure-Python implementation - tests/test_components.py: restore TestSlabSurface overrides that set surface_material_density = sea_water_density before the stepping tests; these were present on develop but missing from this branch Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
… plans JAX support will live on a separate `differentiable-climt` branch. This commit cleans up the numba-only branch: - Remove climt/_core/jax_backend.py and all JAX exports from __init__.py and _core/__init__.py - Remove JAX_KERNEL_FUSION.md, PROPOSAL_JAX_BACKEND.md, plans/PROPOSAL_JAX_BACKEND.md, tests/test_jax_differentiation.py - Add numba @njit kernels for: DryConvectiveAdjustment, EmanuelV2/V3, HeldSuarez, Instellation, SlabSurface (already committed for GSC, Berger, GrayLW in previous commit) - Add optimization parity tests for all seven components - Add backend.py jit_compile helper and prange fallback - Add implementation plans for numba PR and differentiable branch Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
…nents Reports Python vs Numba timing for 8192 columns × 30 levels using ._pyfunc (py_func) on each @njit kernel. Results: 32x–124x speedups across HeldSuarez, GrayLW, Frierson tau, GSC, DryConv, Berger, SlabSurface, and Instellation components. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
…stabilization - Implement Numba optimizations for EmanuelConvectionPythonV3. - Add comprehensive benchmarks for Numba vs. Backend overhead (benchmark_numba_x_backend.py). - Add UnytBackend support to Emanuel V3 for 3.5x speedup over DataArrayBackend. - Stabilize test suite by adding constant reset to pytest conftest.py. - Add Emanuel V3 parity tests and record benchmark results.
…/climt into numba-optimized-components
Add optional Numba JIT optimization for pure-Python components
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