README.md

---
license: apache-2.0
language:
  - en
tags:
  - code
  - kernels
  - benchmark
  - arm
  - aarch64
  - sve
  - ncnn
  - conv2d
pretty_name: ARM Bench Trace
size_categories:
  - n<1K
---

# ARM Bench Trace

A benchmark trace for **ARM (aarch64 / SVE) CPU kernel** evaluation. It pairs
operator *definitions* with concrete *workloads* and reference/baseline
*solutions*, so that a candidate kernel can be checked for **bit-exact
correctness** against a reference and timed against a baseline.

The current trace covers **2D convolution (`conv2d`)** as extracted from the
[ncnn](https://github.com/Tencent/ncnn) test suite
(`tests/ncnn/candidate/convolution.cpp`).

## Repository structure

```
definitions/   # operator specs: axes (const/var), input/output shapes, PyTorch reference
  conv/        #   38 conv2d definitions, one JSON per kernel shape
solutions/     # kernels implementing a definition
  ncnn/
    _harness/                 # C/C++ harness contract (conv2d.h / conv2d.cpp)
    baseline-ncnn-arm/conv2d/ # ncnn::Convolution_arm wrappers (the timing baseline)
    reference-scalar/conv2d/  # naive scalar 6-loop conv2d (ground-truth correctness)
workloads/     # concrete problem instances (var-axis values + scalar inputs)
  conv/        #   one JSONL per definition; each line is a runnable workload
```

### Definitions (`definitions/conv/*.json`)

Each file describes one operator instantiation. Key fields:

- `name`, `op_type`, `description`, `tags`
- `axes` — each axis is either `const` (baked-in, e.g. `Kh=3`, `C_in=64`,
  `C_out=128`) or `var` (chosen per workload, e.g. `N`, `H`, `W`).
- `inputs` / `outputs` — tensor shapes (symbolic over the axes) and dtypes.
- `constraints` — derived-shape relations (e.g. output H/W from stride, pad, dilation).
- `reference` — a self-contained PyTorch (`torch.nn.functional.conv2d`) snippet
  defining the ground-truth result.

The file name encodes the const axes, e.g.
`conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c64_c128` = 3×3 kernel, stride 1, dilation 1,
64→128 channels.

### Workloads (`workloads/conv/*.jsonl`)

One JSONL file per definition; each line is a single workload instance:

```json
{"axes": {"N": 1, "H": 14, "W": 14},
 "scalar_inputs": {"pad_top": 0, "pad_left": 0, "activation_type": 0, "with_bias": 0},
 "uuid": "H14_W14_padh0_padw0_b0",
 "tags": {"from": "tests/ncnn/candidate/convolution.cpp"}}
```

`axes` fills in the `var` axes of the definition; `scalar_inputs` provides the
runtime scalar arguments.

### Solutions (`solutions/ncnn/...`)

A solution is a JSON record pointing at a definition plus inlined source files:

- `definition`, `dataset`, `author`, `description`
- `spec` — `language`, `target_hardware` (e.g. `graviton3`, `aarch64-sve`),
  `entry_point`, `dependencies`, `isa_features`, `compile_flags`, `link_flags`
- `sources` — list of `{path, content}` (the actual `kernel.cpp`)

Two solution families ship here:

- **`baseline-ncnn-arm`** — wraps `ncnn::Convolution_arm` (SVE, from
  `libncnn_arm_heavy.a`); the same `kernel.cpp` is stamped across every
  definition with const params supplied by the definition. This is the
  **performance baseline**. Times `create_pipeline + forward`.
- **`reference-scalar`** — a straight scalar 6-loop accumulator; slow but
  **ground-truth correct**, used as the correctness reference / Phase-1 smoke test.

The shared **harness** (`solutions/ncnn/_harness/conv2d.{h,cpp}`) declares the
`ncnn::convolution_kernel` contract that every solution implements, and handles
input padding before the kernel is invoked.

## Intended use

Drive an ARM CPU kernel benchmark/agent loop:

1. Load a definition + its workloads.
2. Compile a candidate solution against the harness contract.
3. Check **bit-exact correctness** vs. the PyTorch / scalar reference.
4. Time the candidate against the `baseline-ncnn-arm` solution on Graviton3.

## License

Released under the Apache-2.0 license. Kernel sources derive from / wrap
[ncnn](https://github.com/Tencent/ncnn) (BSD-3-Clause); see ncnn's license for
the underlying library terms.