How do I install the Rocprof Compute skill?

Run npx skillmds@latest add wenyi-li/rocprof-compute 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.

Is Rocprof Compute safe to use?

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Which AI agents work with Rocprof Compute?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Rocprof Compute free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Rocprof Compute?

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

Rocprof Compute by wenyi-li — Agent Skill

Name: Rocprof Compute
Rating: 4.2 (5 reviews)
Author: wenyi-li

File contents

rocprof-compute Profiling

Purpose

Capture AMD GPU kernel metrics, roofline data, and traces with rocprof-compute.
Analyze collected workloads to identify bottlenecks (compute vs memory, cache/TCP/TCC/SQ utilization).

When to Use

Need kernel-level performance diagnostics on AMD GPUs (MI200/MI300 family).
Comparing different kernel implementations or launch configs.
Triaging stalls/low occupancy indicated by runtime benchmarks.

How to Use

Activate project venv and install rocprof-compute Python deps (once per environment):
- source .venv/bin/activate
- python -m pip install -r /opt/rocm-7.0.0/libexec/rocprofiler-compute/requirements.txt
Profile a workload:
- source .venv/bin/activate && rocprof-compute profile -n <name> --path <out_dir> --join-type kernel -b SQ -b TCP -b TCC -- <cmd> <args>
- Example (paged attention ragged test):
  rocprof-compute profile -n kernelgen --path rocprof_compute_profile --no-roof --join-type kernel -b SQ -b TCP -b TCC -- .venv/bin/python -O op_tests/test_pa_ragged.py -p Shomy -q none -c 128
- Prefer --join-type kernel for comparing same kernel across grids; switch to grid if grid-sensitive.
- Add/adjust -b blocks to target specific hardware units; use --list-metrics <arch> if unsure.
- Use --no-roof to skip roofline if only counters are needed; remove it to gather roofline data.
Analyze collected data:
- rocprof-compute analyze --path <out_dir> -b <metric_ids> or --list-stats / --list-metrics <arch> to discover ids.
- Example: rocprof-compute analyze --path rocprof_compute_profile -b 2
- For interactive review, use --gui (default port 8050 or --random-port) or --tui.
Typical workflow checklist:
- Pick a short, reproducible workload and seed; pin --name + --path per experiment.
- Collect counters (SQ/TCP/TCC) and optionally roofline in one run; avoid mixing many kernels in a single profile when isolating a hotspot.
- After analyze, inspect top stats, occupancy, LDS/HBM bandwidth, and hotspot kernels; rerun with filtered --kernel or --dispatch if needed.

References

Load references/rocprof_compute_profile_help.txt for full rocprof-compute profile --help.
Load references/rocprof_compute_analyze_help.txt for full rocprof-compute analyze --help.

---name: rocprof-computedescription: This skill should be used when profiling AMD GPU kernels with rocprof-compute to collect metrics, roofline data, and analyze bottlenecks for HIP kernels.---# rocprof-compute Profiling## Purpose- Capture AMD GPU kernel metrics, roofline data, and traces with rocprof-compute.- Analyze collected workloads to identify bottlenecks (compute vs memory, cache/TCP/TCC/SQ utilization).## When to Use- Need kernel-level performance diagnostics on AMD GPUs (MI200/MI300 family).- Comparing different kernel implementations or launch configs.- Triaging stalls/low occupancy indicated by runtime benchmarks.## How to Use- Activate project venv and install rocprof-compute Python deps (once per environment): - `source .venv/bin/activate` - `python -m pip install -r /opt/rocm-7.0.0/libexec/rocprofiler-compute/requirements.txt`- Profile a workload: - `source .venv/bin/activate && rocprof-compute profile -n <name> --path <out_dir> --join-type kernel -b SQ -b TCP -b TCC -- <cmd> <args>` - Example (paged attention ragged test): `rocprof-compute profile -n kernelgen --path rocprof_compute_profile --no-roof --join-type kernel -b SQ -b TCP -b TCC -- .venv/bin/python -O op_tests/test_pa_ragged.py -p Shomy -q none -c 128` - Prefer `--join-type kernel` for comparing same kernel across grids; switch to `grid` if grid-sensitive. - Add/adjust `-b` blocks to target specific hardware units; use `--list-metrics <arch>` if unsure. - Use `--no-roof` to skip roofline if only counters are needed; remove it to gather roofline data.- Analyze collected data: - `rocprof-compute analyze --path <out_dir> -b <metric_ids>` or `--list-stats` / `--list-metrics <arch>` to discover ids. - Example: `rocprof-compute analyze --path rocprof_compute_profile -b 2` - For interactive review, use `--gui` (default port 8050 or `--random-port`) or `--tui`.- Typical workflow checklist: - Pick a short, reproducible workload and seed; pin `--name` + `--path` per experiment. - Collect counters (SQ/TCP/TCC) and optionally roofline in one run; avoid mixing many kernels in a single profile when isolating a hotspot. - After analyze, inspect top stats, occupancy, LDS/HBM bandwidth, and hotspot kernels; rerun with filtered `--kernel` or `--dispatch` if needed.## References- Load `references/rocprof_compute_profile_help.txt` for full `rocprof-compute profile --help`.- Load `references/rocprof_compute_analyze_help.txt` for full `rocprof-compute analyze --help`.

Frequently asked questions

Rocprof Compute