profile-kernel

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GPU kernel profiling workflow across supported kernel implementation languages. Provides commands for all 4 profiling modes (annotation, event, ncu, nsys), metric interpretation tables, bottleneck identification rules, and the output contract for returning compact results to the orchestrator. Use when: (1) profiling a kernel version, (2) interpreting profiling artifacts/reports, (3) comparing kernel versions, (4) identifying bottlenecks and optimization opportunities, (5) documenting performance in the development log.

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Sourcepepperu96/hyper-mla
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NPX Install

npx skill4agent add pepperu96/hyper-mla profile-kernel

Tags

Translated version includes tags in frontmatter
gpu-kernel-profilingncu-profilingnsys-profilingperformance-optimizationbottleneck-detection

SKILL.md Content

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GPU Kernel Profiling

GPU Clock Protocol

Always lock clocks before profiling. Always run one profiling at a time.
bash
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:
text
ridge_point = peak_tflops / peak_gbps

Profiling Mode Decision Tree

Mode
--prof_type
When to useOutput 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)

bash
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

python
# 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.
bash
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

MetricHealthyIf unhealthy → IssueOptimization hint
TC Util>60%Memory or latency boundCheck DRAM%, occupancy
DRAM Throughput>70%Compute or latency boundCheck TC%, occupancy
Achieved Occupancy>25%Register/smem pressureReduce tile size, occupancy hint
L2 Hit Rate>80%Poor data reuseSwizzle, larger tiles, data layout
Local Spilling0 bytesRegister overflowSmaller tiles, fewer accumulators
Waves/SM>1.0Underfilled GPUMore blocks, reduce per-block resources

Bottleneck Classification

PatternClassificationFocus
DRAM% high + TC% lowMemory-boundData reuse, TMA hints, head grouping
TC% high + DRAM% lowCompute-boundKernel efficiency, tile sizes
Both lowLatency-boundOccupancy, reduce spilling, more blocks
L2 hit < 80%Locality issueSwizzle 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.
markdown
## 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:
markdown
**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

  • Annotation mode: references/mode-annotation.md — artifacts, report structure, reading guide
  • Event mode: references/mode-event.md — artifacts, limitations, reading guide
  • NCU mode: references/mode-ncu.md — artifacts, annotated source, per-kernel SASS metrics and PTX source files, reading guide
  • NSYS mode: references/mode-nsys.md — artifacts, CSVs, visualization, reading guide