ForgeStencil: Automating Per-Case Stencil Specialization from Kernels to 100+ Real Applications

2026-09-06

AI Summary

针对GPU上不同应用和精度的Stencil计算优化问题，提出ForgeStencil方法，通过代码合成代理生成特定配置的CUDA算子，并在100多个实际应用中验证其有效性。

Abstract

On modern GPUs the fastest stencil kernel depends on the stencil's shape, precision, and host application, and a kernel tuned for one case is rarely fastest for another. Stencil DSLs, code generators, and autotuners instead pursued generality: a single human-authored method reused across cases and validated mainly on microbenchmarks, because per-case specialization was too costly to scale. ForgeStencil starts from the opposite assumption. Code-synthesis agents have reduced that cost enough to build a fresh solution for each case and deploy it end-to-end in real software. A Kernel Agent synthesizes CUDA and forges a per-configuration matrix of specialized operators that matches or exceeds the strongest publicly available state-of-the-art (SOTA) baseline for each case. An App Agent extends the principle to whole applications: it locates hotspots, rewrites application structure, and validates and integrates each change across 100+ real industrial and scientific codes. Most of the measured speedup comes from structural and host-side rewrites, with pure stencil replacement in the minority; the gain also correlates negatively with how well the baseline was already tuned, consistent with gains coming from specialization rather than generic reuse. Every result is checked by a measurement-integrity harness that turns an overstated speedup into a system-level error. The forged kernels reach a same-precision f32 geometric mean of 2.35x against the per-case SOTA baselines (fp16 gains, 1.95x, disclosed separately; A100), and the end-to-end application median is 1.41x across 100 codes, all against same-architecture GPU baselines with program-provided validation and timing. Of 116 candidates, every one that failed the correctness, measurement, or speedup criteria was recorded as rejected or downgraded instead of written up as a speedup.

Problem

• Stencil
• GPU
• Specialization
• Code Generation
• Performance

Innovation

• code-synthesis agents
• per-case specialization
• end-to-end deployment
• application structure modification