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xpu-profile-unitrace

価格未確認★ 21 GitHub スター登録情報の更新日 · 2026年9月14日agent-skill

概要

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.

説明全文を読む

ソース文書であり、このサイトへの操作指示ではありません。コマンド実行前に権限を確認してください。

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

ソースを見る ↗

ファイルのメタデータ

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.

元のテキストを表示

---
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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ライセンス: Apache-2.0
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ライセンス: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
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AI レビュー承認がありません
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
Review status: AI review approval is missing

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メタデータと審査情報は参考です。人気、ソースの発見、実行成功は別の事実です。

ソースリポジトリ: intel/gpu-ai-skills
ライセンス: Apache-2.0
バージョン: Unknown
最終 GitHub プッシュ: 2026年9月11日
登録情報の更新日: 2026年9月14日
手順のパス: plugins/intel-gpu-ai-skills/skills/xpu-profile-unitrace/SKILL.md @ 0b4fafd09c5e

登録されたバージョンです。ソースのリリース情報を確認してください。

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52/100

要レビュー

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57/100

Do not auto-install

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68/100

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Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
AI レビュー承認がありません
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
Review status: AI review approval is missing

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詳細情報

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      "stars": "21 GitHub stars",
      "repoActivity": "21 stars, 6 forks",
      "lastPushed": "1mo since push",
      "license": "Apache-2.0",
      "repository": "https://github.com/intel/gpu-ai-skills/tree/main/plugins/intel-gpu-ai-skills/skills/xpu-profile-unitrace",
      "install": "npx skills add intel/gpu-ai-skills --skill xpu-profile-unitrace",
      "installSafety": "standard package or runtime install path",
      "permissionSurface": "secrets or environment access, shell or command execution",
      "documentation": "Strong README/SKILL.md context",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
      "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"
  }
}

クリエイター向け

掲載元

Registry により登録

申請可能

この掲載は公開ソースから登録されており、メンテナー申請が承認されるまで公式として表示されません。

作成者: intel
ソース: intel/gpu-ai-skills
インデックス作成者: OpenAgentSkill コミュニティインデックス

帰属は公開リポジトリまたは作成者プロフィールにリンクされています。作成者は掲載を申請して所有権シグナルを更新できます。

このスキルを申請

所有者の申請

このスキル掲載を申請

この Registry により登録掲載は intel に帰属していますが、まだ公式として表示されていません。申請すると、確認済み所有者シグナルが追加され、今後の公開、インストール、監査更新の信頼性が高まります。

共有キット

クリエイター被リンクキット

README にエビデンスバッジを追加

開発者がリポジトリを評価する場所で、正規掲載、現在の信頼・監査シグナル、実際の Agent-Proven エビデンスを表示します。

[![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)

バッジをプレビュー監査を開くクリエイターキット

コミュニティシグナル

このスキルが Agent ワークフローに役立つかを共有してください。集約されたフィードバックがランキングを改善します。

概要

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

ソースを確認

価格と実行コスト

小さなタスクから始める

出典と利用上の注意

Agent 接続

関連スキル

クリエイター向け

Registry により登録

このスキル掲載を申請

README にエビデンスバッジを追加

コミュニティシグナル

What unitrace adds over `torch.profiler`