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xpu-profile-unitrace

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Ringkasan

Profile Intel-XPU workloads at the SYCL / Level Zero kernel level via Intel pti-gpu's unitrace. Captures per-API-call and per-kernel timing, memory transfers, oneCCL / MPI events, and hardware counters PyTorch-level profilers cannot see. Use when a hot op is already known at the torch.profiler layer and the user needs the SYCL kernel beneath, or when profiling oneCCL collectives in multi-GPU runs. Not for PyTorch-level signal (use torch-xpu-profile / vllm-xpu-profile). Requires building unitrace from source.

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xpu-profile-unitrace

unitrace profiles XPU workloads at the SYCL / Level Zero kernel level — captures per-kernel timing, memcopy bytes, oneCCL/MPI events, and hardware counters that PyTorch profilers can't see.

Use when:

A PyTorch-level profiler identified a hot op and you need to know which SYCL kernel inside it is the cost.
You need Level Zero command-list events, oneCCL collectives, exact memcopy bytes, kernel launch geometry, or HW counters.
Multi-XPU run with per-rank oneCCL visibility needed.
Workload is raw SYCL / oneAPI (not PyTorch).

Prefer torch-xpu-profile or vllm-xpu-profile first; their output usually answers the question without going to SYCL level.

Install: check, then build if missing

command -v unitrace && unitrace --version

Most public XPU images don't ship unitrace. To build from source inside the target image:

source /opt/intel/oneapi/setvars.sh --force >/dev/null
git clone --depth 1 https://github.com/intel/pti-gpu.git /opt/pti-gpu
cd /opt/pti-gpu/tools/unitrace
mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j"$(nproc)"
export PATH="/opt/pti-gpu/tools/unitrace/build:$PATH"
unitrace --version          # confirm
unitrace --device-list      # confirm sees XPU

Prerequisites: CMake 3.22+, C++17 compiler, oneAPI Base Toolkit (present in vllm/vllm-openai-xpu:latest and any sglang-xpu image). Add -DBUILD_WITH_MPI=1 for multi-GPU collective profiling; -DCMAKE_INSTALL_PREFIX=/opt/unitrace && make install for an installable layout. Verified clean on vllm/vllm-openai-xpu:latest against unitrace 2.3.0.

If you profile often, bake this into a Dockerfile extending the runtime image so you don't rebuild every session.

Quickstart capture

cd /work       # working dir is where the trace lands
unitrace \
    --chrome-call-logging \
    --chrome-kernel-logging \
    python3 my_workload.py

Closing log lines name the exact paths:

[INFO] Log is stored in /work/python3.<PID>.json
[INFO] Timeline is stored in python3.<PID>.json

Drag python3.<PID>.json into https://ui.perfetto.dev. -o NAME sets a marker / log path but does not rename the timeline; don't rely on it as a "save as X.json" flag.

For hardware metrics:

unitrace --stall-sampling --chrome-kernel-logging -o /work/stalls.json python3 my_workload.py

unitrace --device-list shows visible XPUs; unitrace --metric-list shows available HW counters.

What unitrace adds over `torch.profiler`

Layer	torch.profiler	unitrace
PyTorch op (`aten::matmul`)	yes	yes (passes through)
SYCL kernel name + duration	no	yes (e.g. `xetla_gemm_universal_4_b_2_d_4`)
Level Zero command-list events	no	yes (queue submit / sync / fence)
oneCCL collectives	no	yes (`Allreduce`, `Allgather`, per-rank)
Memory copy direction + size	partial	yes (H2D/D2H/P2P with byte counts)
Hardware counters	no	yes (`--stall-sampling`, `--metric-query`)

Use it when the PyTorch op is "matmul" and you need to know which GEMM kernel was dispatched (XeTLA vs oneDNN vs Triton fallback).

Reading the timeline

Open the Chrome-trace JSON in Perfetto. Same conventions as torch.profiler, plus:

Kernel-name rows — group by kernel name (right-click) for total time per kernel; top 3–5 dominate.
Command-list submit gaps — wide gaps between adjacent kernels (> kernel duration) suggest host is the bottleneck.
CCL collective rows — multi-GPU per-rank timing. A long rank blocks others — straggler.
Memory copies — directional rows. Unexpected P2P copies often mean a missing device_map placement.

Stall sampling for hot kernels

Once a kernel dominates, rerun with stall sampling to see why:

unitrace --stall-sampling -k --chrome-kernel-logging \
    -o /work/stall.json python3 my_workload.py

Output groups stalls by category. High "Memory" -> bandwidth-bound; high "Pipeline" -> compute-bound. Read against the same roofline used by model-config-recommend.

Common errors

unitrace: command not found -> build dir not on PATH. export PATH=/opt/pti-gpu/tools/unitrace/build:$PATH.
unable to load metric library -> oneAPI env not set. source /opt/intel/oneapi/setvars.sh --force.
Empty trace -> workload didn't run on XPU. Verify with unitrace --device-list and xpu-smi dump -d 0 -m 5.
Trace size in GB -> long runs accumulate. Capture a short window (5–10 iterations).
Permission denied reading HW metrics -> some metric modes need --privileged on the container.

Env vars

Variable	Purpose
`ZE_AFFINITY_MASK`	Pin to one XPU before profiling.
`LD_LIBRARY_PATH`	Must include oneAPI runtime libs (handled by `setvars.sh`).
`ZE_ENABLE_TRACING_LAYER=1`	Force L0 to load the tracing layer when unitrace can't inject it automatically.

What this skill does NOT cover

Fixing a hot SYCL kernel — out of scope.
Hardware-counter analysis on Data Center GPU Max (richer modes not covered here).

References

pti-gpu repo: https://github.com/intel/pti-gpu
unitrace README: https://github.com/intel/pti-gpu/blob/master/tools/unitrace/README.md
Intel performance tools: https://www.intel.com/content/www/us/en/docs/oneapi/optimization-guide-gpu/2024-1/pti-gpu.html
Perfetto: https://ui.perfetto.dev

Lihat sumber ↗

Metadata berkas

name: xpu-profile-unitrace
description: Profile Intel-XPU workloads at the SYCL / Level Zero kernel level via Intel pti-gpu's unitrace. Captures per-API-call and per-kernel timing, memory transfers, oneCCL / MPI events, and hardware counters PyTorch-level profilers cannot see. Use when a hot op is already known at the torch.profiler layer and the user needs the SYCL kernel beneath, or when profiling oneCCL collectives in multi-GPU runs. Not for PyTorch-level signal (use torch-xpu-profile / vllm-xpu-profile). Requires building unitrace from source.

Lihat teks asli

---
name: xpu-profile-unitrace
description: Profile Intel-XPU workloads at the SYCL / Level Zero kernel level via Intel pti-gpu's unitrace. Captures per-API-call and per-kernel timing, memory transfers, oneCCL / MPI events, and hardware counters PyTorch-level profilers cannot see. Use when a hot op is already known at the torch.profiler layer and the user needs the SYCL kernel beneath, or when profiling oneCCL collectives in multi-GPU runs. Not for PyTorch-level signal (use torch-xpu-profile / vllm-xpu-profile). Requires building unitrace from source.
---

# xpu-profile-unitrace

`unitrace` profiles XPU workloads at the SYCL / Level Zero kernel
level — captures per-kernel timing, memcopy bytes, oneCCL/MPI
events, and hardware counters that PyTorch profilers can't see.

Use when:

- A PyTorch-level profiler identified a hot op and you need to
  know which SYCL kernel inside it is the cost.
- You need Level Zero command-list events, oneCCL collectives,
  exact memcopy bytes, kernel launch geometry, or HW counters.
- Multi-XPU run with per-rank oneCCL visibility needed.
- Workload is raw SYCL / oneAPI (not PyTorch).

Prefer **torch-xpu-profile** or **vllm-xpu-profile** first; their
output usually answers the question without going to SYCL level.

## Install: check, then build if missing

```sh
command -v unitrace && unitrace --version
```

Most public XPU images don't ship unitrace. To build from source
inside the target image:

```sh
source /opt/intel/oneapi/setvars.sh --force >/dev/null
git clone --depth 1 https://github.com/intel/pti-gpu.git /opt/pti-gpu
cd /opt/pti-gpu/tools/unitrace
mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j"$(nproc)"
export PATH="/opt/pti-gpu/tools/unitrace/build:$PATH"
unitrace --version          # confirm
unitrace --device-list      # confirm sees XPU
```

Prerequisites: CMake 3.22+, C++17 compiler, oneAPI Base Toolkit
(present in `vllm/vllm-openai-xpu:latest` and any sglang-xpu image).
Add `-DBUILD_WITH_MPI=1` for multi-GPU collective profiling;
`-DCMAKE_INSTALL_PREFIX=/opt/unitrace && make install` for an
installable layout. Verified clean on `vllm/vllm-openai-xpu:latest`
against unitrace 2.3.0.

If you profile often, bake this into a Dockerfile extending the
runtime image so you don't rebuild every session.

## Quickstart capture

```sh
cd /work       # working dir is where the trace lands
unitrace \
    --chrome-call-logging \
    --chrome-kernel-logging \
    python3 my_workload.py
```

Closing log lines name the exact paths:

```
[INFO] Log is stored in /work/python3.<PID>.json
[INFO] Timeline is stored in python3.<PID>.json
```

Drag `python3.<PID>.json` into <https://ui.perfetto.dev>. `-o NAME`
sets a marker / log path but does **not** rename the timeline;
don't rely on it as a "save as X.json" flag.

For hardware metrics:

```sh
unitrace --stall-sampling --chrome-kernel-logging -o /work/stalls.json python3 my_workload.py
```

`unitrace --device-list` shows visible XPUs;
`unitrace --metric-list` shows available HW counters.

## What unitrace adds over `torch.profiler`

| Layer | torch.profiler | unitrace |
|---|---|---|
| PyTorch op (`aten::matmul`) | yes | yes (passes through) |
| SYCL kernel name + duration | no | yes (e.g. `xetla_gemm_universal_4_b_2_d_4`) |
| Level Zero command-list events | no | yes (queue submit / sync / fence) |
| oneCCL collectives | no | yes (`Allreduce`, `Allgather`, per-rank) |
| Memory copy direction + size | partial | yes (H2D/D2H/P2P with byte counts) |
| Hardware counters | no | yes (`--stall-sampling`, `--metric-query`) |

Use it when the PyTorch op is "matmul" and you need to know which
GEMM kernel was dispatched (XeTLA vs oneDNN vs Triton fallback).

## Reading the timeline

Open the Chrome-trace JSON in Perfetto. Same conventions as
torch.profiler, plus:

- **Kernel-name rows** — group by kernel name (right-click) for
  total time per kernel; top 3–5 dominate.
- **Command-list submit gaps** — wide gaps between adjacent
  kernels (> kernel duration) suggest host is the bottleneck.
- **CCL collective rows** — multi-GPU per-rank timing. A long
  rank blocks others — straggler.
- **Memory copies** — directional rows. Unexpected P2P copies
  often mean a missing `device_map` placement.

## Stall sampling for hot kernels

Once a kernel dominates, rerun with stall sampling to see *why*:

```sh
unitrace --stall-sampling -k --chrome-kernel-logging \
    -o /work/stall.json python3 my_workload.py
```

Output groups stalls by category. High "Memory" -> bandwidth-bound;
high "Pipeline" -> compute-bound. Read against the same roofline
used by **model-config-recommend**.

## Common errors

- `unitrace: command not found` -> build dir not on PATH.
  `export PATH=/opt/pti-gpu/tools/unitrace/build:$PATH`.
- `unable to load metric library` -> oneAPI env not set.
  `source /opt/intel/oneapi/setvars.sh --force`.
- Empty trace -> workload didn't run on XPU. Verify with
  `unitrace --device-list` and `xpu-smi dump -d 0 -m 5`.
- Trace size in GB -> long runs accumulate. Capture a short window
  (5–10 iterations).
- Permission denied reading HW metrics -> some metric modes need
  `--privileged` on the container.

## Env vars

| Variable | Purpose |
|---|---|
| `ZE_AFFINITY_MASK` | Pin to one XPU before profiling. |
| `LD_LIBRARY_PATH` | Must include oneAPI runtime libs (handled by `setvars.sh`). |
| `ZE_ENABLE_TRACING_LAYER=1` | Force L0 to load the tracing layer when unitrace can't inject it automatically. |

## What this skill does NOT cover

- Fixing a hot SYCL kernel — out of scope.
- Hardware-counter analysis on Data Center GPU Max (richer modes
  not covered here).

## References

- pti-gpu repo: <https://github.com/intel/pti-gpu>
- unitrace README: <https://github.com/intel/pti-gpu/blob/master/tools/unitrace/README.md>
- Intel performance tools: <https://www.intel.com/content/www/us/en/docs/oneapi/optimization-guide-gpu/2024-1/pti-gpu.html>
- Perfetto: <https://ui.perfetto.dev>

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GitHub adoption: 21 GitHub stars
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Permission surface: secrets or environment access, shell or command execution
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Repositori sumber: intel/gpu-ai-skills
Lisensi: Apache-2.0
Versi: Unknown
Push GitHub terakhir: 11 Sep 2026
Direktori diperbarui: 14 Sep 2026
Jalur instruksi: plugins/intel-gpu-ai-skills/skills/xpu-profile-unitrace/SKILL.md @ 0b4fafd09c5e

Versi dilaporkan dalam metadata direktori; periksa rilis sumber.

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GitHub adoption: 21 GitHub stars
Stars/forks activity: 21 stars, 6 forks; issue activity unavailable in current metadata
Dependency/runtime risk: credential or environment access, external package install surface
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Review status: AI review approval is missing

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      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "research",
      "agent-skill"
    ],
    "known_risks": [
      "AI review approval is missing",
      "Low GitHub adoption signal",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 21 GitHub stars",
      "Stars/forks activity: 21 stars, 6 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: credential or environment access, external package install surface",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 68,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Low GitHub adoption signal",
      "AI review approval is missing",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 21 GitHub stars",
      "Stars/forks activity: 21 stars, 6 forks; issue activity unavailable in current metadata"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 52,
    "label": "Needs review"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "z91772524-ai-edr-bypass-re",
      "name": "edr-bypass-re",
      "url": "https://www.openagentskill.com/skills/z91772524-ai-edr-bypass-re",
      "stars": 29,
      "install_command": "npx skills add z91772524-ai/pojia-next-mac --skill edr-bypass-re",
      "trust_score": 71,
      "audit_score": 74
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "AI review approval is missing",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use xpu-profile-unitrace in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 65/100 Manual review",
      "Audit: 68/100 Needs review",
      "Safety: 24/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "intel-xpu-profile-unitrace (xpu-profile-unitrace)",
      "install_command": "npx skills add intel/gpu-ai-skills --skill xpu-profile-unitrace",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "intel-xpu-profile-unitrace",
      "task": "Use xpu-profile-unitrace in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/intel-xpu-profile-unitrace",
    "api": "https://www.openagentskill.com/api/agent/skills/intel-xpu-profile-unitrace",
    "audit": "https://www.openagentskill.com/skills/intel-xpu-profile-unitrace/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=intel-xpu-profile-unitrace&task=Use%20xpu-profile-unitrace%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20xpu-profile-unitrace%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20xpu-profile-unitrace%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/intel-xpu-profile-unitrace/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/intel-xpu-profile-unitrace"
  }
}

Untuk kreator

Sumber listing

Diindeks Registry

Dapat diklaim

Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.

Kreator: intel
Sumber: intel/gpu-ai-skills
Diindeks oleh: Indeks komunitas OpenAgentSkill

Atribusi menautkan ke repositori publik atau profil kreator. Kreator dapat mengklaim listing untuk memperbarui sinyal kepemilikan.

Klaim skill ini

Klaim pemilik

Klaim listing skill ini

Listing Diindeks Registry ini dikaitkan dengan intel, tetapi belum ditandai resmi. Klaim untuk menambahkan sinyal pemilik terverifikasi dan membuat pembaruan peluncuran, pemasangan, serta audit berikutnya lebih tepercaya.

Kit berbagi

Kit backlink kreator

Tambahkan badge bukti ke README Anda

Tampilkan listing kanonis, sinyal kepercayaan dan audit saat ini, serta bukti Agent-Proven nyata di tempat pengembang mengevaluasi repositori.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/intel-xpu-profile-unitrace?metric=listed&label=Listed)](https://www.openagentskill.com/skills/intel-xpu-profile-unitrace?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/intel-xpu-profile-unitrace?metric=trust&label=Trust)](https://www.openagentskill.com/skills/intel-xpu-profile-unitrace?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/intel-xpu-profile-unitrace?metric=audit&label=Audit)](https://www.openagentskill.com/skills/intel-xpu-profile-unitrace/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/intel-xpu-profile-unitrace?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/intel-xpu-profile-unitrace?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

Pratinjau badge Buka audit Kit kreator

Sinyal komunitas

Bagikan apakah skill ini bermanfaat untuk alur kerja Agent Anda. Masukan gabungan meningkatkan peringkat dari waktu ke waktu.

Ringkasan

xpu-profile-unitrace

Install: check, then build if missing

Quickstart capture

What unitrace adds over torch.profiler

Reading the timeline

Stall sampling for hot kernels

Common errors

Env vars

What this skill does NOT cover

References

Tinjau sumber

Harga dan biaya penggunaan

Mulai dengan tugas kecil

Sumber dan catatan penggunaan

Akses agent

Skill terkait

Untuk kreator

Diindeks Registry

Klaim listing skill ini

Tambahkan badge bukti ke README Anda

Sinyal komunitas

What unitrace adds over `torch.profiler`