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profile-kernel

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Updated 10-16 07:57

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GPU Kernel Profiling

GPU Clock Protocol

Always lock clocks before profiling. Always run one profiling at a time.

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

Device Peaks

Shared profiling guidance should not hard-code a single device. Read the active device context from params.json, runtime profiling artifacts, and the configured peak-spec sources in src/mla_var3/conf/devices.json.

Use device-specific reference files in docs/devices/ only when the measurement context matters. Ridge point remains:

ridge_point = peak_tflops / peak_gbps

Profiling Mode Decision Tree

Mode --prof_type When to use Output root
Annotation annotation Default first pass. Roofline + NCU metrics + comparison tables out/profiles/annotation/
Event event Quick iteration timing (carries Python overhead) out/profiles/event/
NCU ncu Deep investigation: full NCU sections, source annotations, optimization suggestions out/profiles/ncu/
NSYS nsys Pipeline overlap, stream concurrency, launch ordering out/profiles/nsys/

Selection rule: Start with annotation. Use event for fast A/B comparison (not suitable for KernelPipeline/ConcurrentKernels). Use ncu when you need source-level analysis, Nsight Compute optimization suggestions, or CUDA insights. Use nsys for ConcurrentKernels overlap and stream concurrency analysis.

Profiling Commands

CLI entry point (all modes)

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

# Language-specific path when needed:
python -m mla_var3.kernel.<language_python_package>.mla.<kernel_package> [<version>] \
  --b=32 --s=16 --t=4096 --prof_type=<mode>

Python API

# Annotation
from mla_var3.runtime.profiling.annotation import annotation_profile_plan
prof_result = annotation_profile_plan(plan, out_dir=..., version=..., bench_runs=...)

# NCU
from mla_var3.runtime.profiling.ncu import ncu_profile_plan
prof_result = ncu_profile_plan(plan, out_dir=..., version=..., bench_runs=...)

# NSYS
from mla_var3.runtime.profiling.nsys import nsys_profile_plan
prof_result = nsys_profile_plan(plan, out_dir=..., version=..., bench_runs=...)

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.

Benchmark scripts

Benchmark scripts in tests/benchmark/ are curated experiment drivers. Some accept CLI args, others are fixed scripts to copy and edit.

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)

Output Artifacts

All modes write to out/profiles/<mode>/<kernel>/<params>/<timestamp>/ if not ouptut directory is specified.

Common artifacts (all modes)

  • params.json — Full kernel parameter set (dtype, b, s, t, h, d, k, p). Always read this to confirm the profiling configuration, especially for NCU and NSYS modes whose report.md does not embed parameters.
  • tiling/<stage>.json — Autotuned tiling config per stage
  • tiling/<stage>/autotuning.json — Copied autotuning history from .cache/kernel-autotune/..., colocated with the selected tiling so you can inspect the explored configurations without leaving the profiling output directory
  • compiled/<stage>/ — MLIR and bytecode compilation artifacts

Per-mode artifacts

Each mode produces different artifacts. See the per-mode reference for full details:

  • mode-annotation.md — report.md with per-kernel NCU metrics, roofline comparison, tiling;
  • mode-event.md — report.md with end-to-end roofline summary;
  • mode-ncu.md — report.md (compact diagnosis + NCU recommendations), report-verbose.md (full NCU sections), annotated source, per-kernel SASS metrics, PTX source files;
  • mode-nsys.md — report.md with timeline and overlap analysis;

Metric Interpretation

Metric Healthy If unhealthy → Issue Optimization hint
TC Util >60% Memory or latency bound Check DRAM%, occupancy
DRAM Throughput >70% Compute or latency bound Check TC%, occupancy
Achieved Occupancy >25% Register/smem pressure Reduce tile size, occupancy hint
L2 Hit Rate >80% Poor data reuse Swizzle, larger tiles, data layout
Local Spilling 0 bytes Register overflow Smaller tiles, fewer accumulators
Waves/SM >1.0 Underfilled GPU More blocks, reduce per-block resources

Bottleneck Classification

Pattern Classification Focus
DRAM% high + TC% low Memory-bound Data reuse, TMA hints, head grouping
TC% high + DRAM% low Compute-bound Kernel efficiency, tile sizes
Both low Latency-bound Occupancy, reduce spilling, more blocks
L2 hit < 80% Locality issue Swizzle scheduling, tile size adjustment

Profiler Output Contract

After profiling, return results to the orchestrator in this exact format. Always read params.json to populate the configuration fields accurately.

## Profile: [kernel] [version]

### Configuration
| b | s | t | dtype |
|---|---|---|-------|
| X | X | X | bfloat16 |

### Stages
| Stage | Duration (us) | TC% | DRAM% | Occ% | Bottleneck | Key Issue |
|-------|---------------|-----|-------|------|------------|-----------|

### Bottleneck: [Memory/Compute/Latency]-bound
Root cause: [2 sentences max]

### Top 3 Opportunities (ranked by estimated impact)
1. [name] — est. X% gain — trigger: [metric=value]
2. ...
3. ...

### vs Baseline (if applicable)
| Metric | Previous | Current | Change |
|--------|----------|---------|--------|

Development Log Performance Template

Update the kernel's devlog (e.g., docs/kernels/mla-var6-plus.md) with:

**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]

Detailed References