xpu-profile-unitrace: Research & Knowledge for AI Agents | OpenAgentSkill

Overview

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

View source ↗

File metadata

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.

View original text

---
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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License: Apache-2.0

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GitHub adoption: 21 GitHub stars
Stars/forks activity: 21 stars, 6 forks; issue activity unavailable in current metadata
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Permission surface: secrets or environment access, shell or command execution
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Source repository: intel/gpu-ai-skills
License: Apache-2.0
Version: Unknown
Last GitHub push: Sep 11, 2026
Registry updated: Sep 14, 2026
Instruction path: plugins/intel-gpu-ai-skills/skills/xpu-profile-unitrace/SKILL.md @ 0b4fafd09c5e

Version reported in registry metadata; check source releases before relying on it.

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    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "yanliudesign-mono-color-skill",
      "name": "mono-color",
      "url": "https://www.openagentskill.com/skills/yanliudesign-mono-color-skill",
      "stars": 1919,
      "install_command": "npx skills add yanliudesign/mono-color-skill --skill mono-color",
      "trust_score": 85,
      "audit_score": 93
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "No OpenAgentSkill engagement data yet",
    "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"
  ],
  "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: 67/100 Manual review",
      "Audit: 71/100 Needs review",
      "Safety: 27/100 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"
  }
}

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Creator: intel
Source: intel/gpu-ai-skills
Indexed by: OpenAgentSkill community index

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