bash scripts/lock-gpu-clock.sh           # before profiling
bash scripts/reset-gpu-clock.sh          # after done

ridge_point = peak_tflops / peak_gbps

python -m mla_var3.kernel <kernel_package> [<version>] \
    --b=32 --s=16 --t=4096 --prof_type=<mode>

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Notice that version is optional, and allows you to specify a custom version. This retrieves the right `KernelPlan` of the specified version, and uses that, NOT the `plan` passed to the function. This is just a convenience to automatize version profiling.
Alternatively, you can directly build the plan object using the class of the version to be profiled, and ignore the version argument. 
**Important**: sometimes, after auto-tuning, there might be some errors. In this case, you can simply re-run the command, the best autotune config is already cached and everything should work.

python -m tests.benchmark.bench_mla_var6_plus       # base version
python -m tests.benchmark.bench_mla_var6_plus_v4    # specific version
python -m tests.benchmark.bench_all_mla             # compare all kernels (slow)

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**Performance** (<device>, locked clocks if applicable, bfloat16, b=X, s=X, t=X):

| Metric            | Value   | vs Previous |
| ----------------- | ------- | ----------- |
| Duration          | X.XX μs | Y% faster   |
| Achieved TFLOPs/s | X.XX    | +Z%         |
| Achieved GB/s     | X.XX    | +Z%         |
| Occupancy         | XX%     | --          |
| TC Util           | XX%     | --          |

**Bottleneck**: [Memory-bound / Compute-bound / Latency-bound]

**Issues**:
- [Remaining problems]

**Insights**:
- [Key lessons — why optimization worked or didn't]
- [Guidance for next iteration]