How do I install the Rocprofv3 Profiler skill?

Run npx skillmds@latest add wenyi-li/rocprofv3-profiler in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Rocprofv3 Profiler free to use?

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Who published Rocprofv3 Profiler?

wenyi-li (@wenyi-li) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Rocprofv3 Profiler by wenyi-li — Agent Skill

Name: Rocprofv3 Profiler
Rating: 4.2 (5 reviews)
Author: wenyi-li

Rocprofv3 Profiler

Profile AMD GPU kernels using rocprofv3 and analyze performance bottlenecks. Use when the user wants to profile HIP/ROCm kernels, identify GPU performance issues, analyze hardware counters, or understand why a kernel is slow on AMD GPUs (MI100, MI200, MI300 series). Provides wrapper scripts for rocprofv3 execution and automated parsing of profiler output into structured, agent-friendly JSON with bottleneck classification.

wenyi-li Updated Sep 22, 2026

File contents

rocprofv3-profiler

Profile AMD GPU applications and identify performance bottlenecks using rocprofv3.

Quick Start

1. Run Profiler

python3 scripts/rocprof_wrapper.py --mode counters -- ./your_app [args]

Modes:

counters (default): Collect key performance counters for bottleneck analysis
trace: Collect kernel execution traces (timing only)
full: Collect both counters and traces

Options:

--output-dir <dir>: Output directory (default: ./rocprof_output)
--counters <file>: Custom counter input file (optional)
--kernel <name>: Target specific kernel by name

2. Parse Results

python3 scripts/parse_profile.py <output_dir>

Returns structured JSON with:

Per-kernel metrics summary
Bottleneck classification (compute/memory/lds/latency bound)
Optimization hints
Path to raw data

Example Workflow

# Profile application
python3 scripts/rocprof_wrapper.py --mode counters -- ./matrix_multiply 1024

# Parse and analyze
python3 scripts/parse_profile.py ./rocprof_output

Sample output:

{
  "kernels": [{
    "name": "matmul_kernel",
    "metrics": {
      "duration_ns": 145230,
      "occupancy_pct": 45.2,
      "valu_busy_pct": 78.5,
      "lds_bank_conflict_rate": 0.12,
      "l2_hit_rate": 0.65
    },
    "bottleneck": {
      "type": "memory_bound",
      "confidence": "high",
      "detail": "Low L2 hit rate (65%) with high memory stall cycles"
    }
  }],
  "raw_data_path": "./rocprof_output/pmc_1/counter_collection.csv"
}

Bottleneck Classification

The parser classifies kernels into these categories:

Bottleneck	Indicators
compute_bound	High VALU/MFMA busy, low memory stalls
memory_bound	High memory latency, low cache hit rates
lds_bound	High LDS bank conflicts or LDS instruction stalls
latency_bound	Low occupancy with high instruction latency
balanced	No single dominant bottleneck

Reference Documentation

hardware_counters.md: Key AMD GPU counters and their meaning
bottleneck_heuristics.md: Detailed bottleneck classification rules

Direct rocprofv3 Usage

For advanced use cases, invoke rocprofv3 directly:

# List available counters
rocprofv3 -L

# Trace kernel execution
rocprofv3 --kernel-trace --stats -- ./app

# Collect specific counters
rocprofv3 -i counters.txt -- ./app

Counter input file format (counters.txt):

pmc: SQ_WAVES SQ_INSTS_VALU SQ_INSTS_VMEM
pmc: TCC_HIT TCC_MISS

Troubleshooting

"rocprofv3 not found": Ensure ROCm is installed and /opt/rocm/bin is in PATH.

"No GPU detected": Check rocm-smi output and HSA_VISIBLE_DEVICES environment variable.

Multi-pass collection: If too many counters requested, rocprofv3 replays the kernel. Use fewer counters per pmc line.

---name: rocprofv3-profilerdescription: Profile AMD GPU kernels using rocprofv3 and analyze performance bottlenecks. Use when the user wants to profile HIP/ROCm kernels, identify GPU performance issues, analyze hardware counters, or understand why a kernel is slow on AMD GPUs (MI100, MI200, MI300 series). Provides wrapper scripts for rocprofv3 execution and automated parsing of profiler output into structured, agent-friendly JSON with bottleneck classification.---# rocprofv3-profilerProfile AMD GPU applications and identify performance bottlenecks using rocprofv3.## Quick Start### 1. Run Profiler```bashpython3 scripts/rocprof_wrapper.py --mode counters -- ./your_app [args]```**Modes:**- `counters` (default): Collect key performance counters for bottleneck analysis- `trace`: Collect kernel execution traces (timing only)- `full`: Collect both counters and traces**Options:**- `--output-dir <dir>`: Output directory (default: `./rocprof_output`)- `--counters <file>`: Custom counter input file (optional)- `--kernel <name>`: Target specific kernel by name### 2. Parse Results```bashpython3 scripts/parse_profile.py <output_dir>```Returns structured JSON with:- Per-kernel metrics summary- Bottleneck classification (compute/memory/lds/latency bound)- Optimization hints- Path to raw data## Example Workflow```bash# Profile applicationpython3 scripts/rocprof_wrapper.py --mode counters -- ./matrix_multiply 1024# Parse and analyzepython3 scripts/parse_profile.py ./rocprof_output```**Sample output:**```json{ "kernels": [{ "name": "matmul_kernel", "metrics": { "duration_ns": 145230, "occupancy_pct": 45.2, "valu_busy_pct": 78.5, "lds_bank_conflict_rate": 0.12, "l2_hit_rate": 0.65 }, "bottleneck": { "type": "memory_bound", "confidence": "high", "detail": "Low L2 hit rate (65%) with high memory stall cycles" } }], "raw_data_path": "./rocprof_output/pmc_1/counter_collection.csv"}```## Bottleneck ClassificationThe parser classifies kernels into these categories:| Bottleneck | Indicators ||------------|------------|| **compute_bound** | High VALU/MFMA busy, low memory stalls || **memory_bound** | High memory latency, low cache hit rates || **lds_bound** | High LDS bank conflicts or LDS instruction stalls || **latency_bound** | Low occupancy with high instruction latency || **balanced** | No single dominant bottleneck |## Reference Documentation- **[hardware_counters.md](references/hardware_counters.md)**: Key AMD GPU counters and their meaning- **[bottleneck_heuristics.md](references/bottleneck_heuristics.md)**: Detailed bottleneck classification rules## Direct rocprofv3 UsageFor advanced use cases, invoke rocprofv3 directly:```bash# List available countersrocprofv3 -L# Trace kernel executionrocprofv3 --kernel-trace --stats -- ./app# Collect specific countersrocprofv3 -i counters.txt -- ./app```Counter input file format (`counters.txt`):```pmc: SQ_WAVES SQ_INSTS_VALU SQ_INSTS_VMEMpmc: TCC_HIT TCC_MISS```## Troubleshooting**"rocprofv3 not found"**: Ensure ROCm is installed and `/opt/rocm/bin` is in PATH.**"No GPU detected"**: Check `rocm-smi` output and `HSA_VISIBLE_DEVICES` environment variable.**Multi-pass collection**: If too many counters requested, rocprofv3 replays the kernel. Use fewer counters per `pmc` line.

Frequently asked questions