triton-ascend-case-vector-elemwise-bench-atlas-a3

What problem does it solve? Triton kernels on Ascend Atlas A3 often use tl.* APIs that are significantly slower than mathematically equivalent alternatives, and developers lack benchmarked guidance on which replacements preserve precision while improving performance. ## Core Features & Use Cases - Benchmarked API replacement tables: Provides per-dtype (fp32, fp16, bf16) tables mapping slow Triton API patterns to faster equivalents with measured speedup percentages, e.g. replacing tl.exp2(x) with tl.exp(x * LN2) for a 45% gain on fp32. - Precision-aligned guidance: Every recommended replacement is validated as semantically equivalent and precision-aligned in single-operator unit tests, with explicit warnings about error accumulation in fused kernels. - 2D and cross-dtype scenarios: Covers special cases like replacing broadcast division acc / l[:, None] with reciprocal multiplication to cut vdiv operations by 42% on fp32. - Use Case: When optimizing a Triton elementwise kernel for Atlas A3, consult the tables to swap tl.where(x>0, x, 0) for tl.maximum(x, 0) and gain up to 86% on bf16. ## Quick Start Ask the agent to review your Triton vector elementwise kernel for Atlas A3 and apply the recommended API replacements from the fp32, fp16, or bf16 tables while verifying numerical accuracy stays aligned.