Individual skill
build-triton-ascend-small-matmul
Build a single-program Triton-Ascend matrix multiplication with fused bias using two-dimensional pointer grids and tl.dot. Use when an agent needs a compact matmul-plus-bias kernel for small fixed shapes, a minimal Ascend cube-operation example, or a correctness scaffold before introducing multi-block tiling.
skills/build-triton-ascend-small-matmul/SKILL.md
Source commit 4ab5ee7adf47755ecd42723f55f4c58e38be95a7
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- Last checked
- Nov 4, 2026
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- Repository last updated
- Aug 15, 2026
- License
- Apache-2.0
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- ai Repository topic
- ascend Repository topic
- ascend-npu Repository topic
- npu Repository topic
- triton Repository topic
- triton-ascend Repository topic
- claude-code Repository topic
- codex Repository topic
- opencode Repository topic
- engineering Derived category
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# Build Triton-Ascend Small Matmul
## Goal
Compute output[A, C] = x[A, B] @ y[B, C] + bias[A, C] in one Triton program for small compile-time shapes.
## Workflow
1. Validate x as A by B, y as B by C, and bias as A by C.
2. Create row, reduction, and column offsets with tl.arange.
3. Form the flattened pointer grids through broadcasting.
4. Load both matrices and bias, call tl.dot, and store the result.
5. Launch exactly one program only while all tiles fit the target's on-chip resources.
