본문으로 건너뛰기
cccskills
무료GitHub에 공개

doca-dpa-hl-tracer

Capture and decode execution traces from BlueField DPA workloads to inspect kernel events, synchronization, and communication timing. Choose capture modes, adjust file limits and recording overhead, and diagnose empty traces or decoding failures.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Use this skill when the user runs doca_dpa_hl_tracer to capture/decode DPA-side traces at the programming-events layer (kernel entry/exit, sync points, comm primitive calls, RDMA WR submission, completion drain) — picking TRACE vs CRIT, tuning the JSON config (file-size limits + file_size_limit_policy, thread priorities/cores), decoding against the matching DPA-side ELF, or diagnosing empty/noisy captures. Trigger even when the user does not explicitly mention "DOCA DPA tracer" or "high-level tracer" — typical implicit phrasings include "DPA kernel returns wrong result but host completions look clean", "kernel-entry to first-comm latency is huge", "RDMA WR to drain gap on the DPA", "trace file truncated mid-run", "TRACE doubled my DPA latency", or "tracer wrote a file but parser shows zero events". Refuse and route elsewhere for writing DPA kernels, DPA-Comms/DPA-Verbs programming, raw per-cycle DPA profiling, host-side doca-dpa debugging, or production DPA telemetry — those belong to other skills.

설치 방법 보기

이럴 때 유용합니다

  • Investigate incorrect results from a DPA kernel
  • Inspect delays between kernel entry and communication
  • Adjust capture modes and overhead
  • Diagnose truncated files, empty traces, or decoding failures

포함된 파일(8)

  • SKILL.md17.1 KB
  • BENCHMARK.md7.5 KB
  • CAPABILITIES.md22.6 KB
  • evals/evals.json2.6 KB
  • skill-card.md4.9 KB
  • skill.oms.sig5.1 KB
  • SKILLCARD.yaml3.3 KB
  • TASKS.md26.3 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

DOCA DPA High-Level Tracer

Examples

  • "My DPA kernel is doing the wrong thing — where do I look?" — this is in scope when there is no host-side DOCA_ERROR_*. Capture then decode; do not start with cycle-only profiles.
  • "TRACE mode buried the DPA in observation overhead." — switch capture window and TRACE vs CRIT before changing the kernel.

Instructions

Follow Loading order below. Open the tasks companion for configure / run / debug. Open the capabilities companion for event taxonomy. Do not open every companion file.

Where to start: This is a tool skill for invoking doca_dpa_hl_tracer — the documented host-side CLI that captures DPA-side execution traces in higher-level terms (DPA programming events: kernel entry / exit, sync points, comm primitive calls, RDMA WR submission, completions) rather than raw cycle counts. Open the tasks companion and start at ## configure for the mode-vs-overhead decision and the JSON config layout, then ## run for the capture → decode → render pipeline. Open the capabilities companion when the question is what does this tool actually trace, which DPA programming events does it expose, what is the trace-overhead vs fidelity tradeoff, or how does it slot into a DPA debug loop alongside doca-dpa and doca-debug. If DPA is not the right surface for the user's question (e.g. the bug is host-side, the bug is in the DPACC-produced image, the user wants raw cycle counts), the path-selection rule in the capabilities companion routes the agent before any capture is attempted.

Example questions this skill answers well

The CLASSES of doca_dpa_hl_tracer questions this skill is built to answer, each with one worked example. The class is the load-bearing piece; the worked example is one instance.

  • "My DPA kernel is doing the wrong thing — where do I look?" — worked example: "my host-side doca_dpa_kernel_launch_update_* completes, but the kernel's reported result is wrong; no host-side DOCA_ERROR_*". Answered by the when DPA-side high-level tracing is the right surface gate in the capabilities companion
    • the capture → decode → render flow in the tasks companion + the which DPA programming events to focus on rule in the tasks companion.
  • "My DPA kernel is slow at a granularity that doesn't show up in cycle profiles — how do I see kernel-entry to first-comm-call latency?" — worked example: "my DPA kernel runs but the time between launch and the first RDMA WR submission is bigger than I expected". Answered by the event-taxonomy table in the capabilities companion
    • the iterative loop in the tasks companion which treats trace overhead, mode (TRACE vs CRIT), and capture window as axes to tune.
  • "How do I capture a trace without burying the DPA in observation overhead?" — worked example: "TRACE mode is producing too much data and my measured DPA latency went up by 2x compared to without the tracer". Answered by the mode-vs-overhead tradeoff in the capabilities companion
    • the CRIT-first guidance in the tasks companion (start with critical-events-only; widen to TRACE only when the bug demands per-event detail).
  • "My trace file got truncated mid-run — how should I configure the file-size limits?" — worked example: "binary trace file hit 5 GB and the capture stopped". Answered by the log_file_max_size_in_bytes / bin_file_max_size_in_bytes / file_size_limit_policy triple in the capabilities companion
    • the JSON config layout in the tasks companion.
  • "Is the tracer on my install, and is it paired with the matching doca-dpa library and DPACC compiler version?" — worked example: "is the tracer ABI on my install compatible with the DPA image my DPACC just produced?". Answered by the version-overlay in the capabilities companion, which redirects to the canonical doca-version chain and adds the tracer ↔ doca-dpa library ↔ DPACC compiler match rule.
  • "The capture file looks empty / decode failed — is the install broken, no events fired, or am I tracing the wrong thing?" — worked example: "doca_dpa_hl_tracer ran, wrote a file, but the parser shows zero events". Answered by the layered error taxonomy in the capabilities companion (install / device-binding / DPA-image-instrumented / capture-window / decode-vs-elf / overhead-saturated / version / cross-cutting) + the layered walk in the tasks companion.

Audience

This skill serves external developers, platform operators, and AI agents who have already brought up a DPA-side workload through doca-dpa and now need higher-level visibility into what the DPA kernel is actually doing on the wire — DPA programming events ordering, sync gaps, comm-call latencies, RDMA-WR / completion timing — without dropping all the way down to raw cycle counters. Concretely:

  • A DPA developer who can launch their kernel cleanly from the host side but whose kernel's result is wrong or whose DPA-side performance is below expectation, and who needs a DPA-side ground truth before triaging.
  • A platform operator running a DPA-using workload (RDMA offload from accelerator, custom CC algorithm via doca-pcc) and needs to localize a regression to the DPA side without instrumenting the application.
  • An AI agent producing a DPA-side trace report as evidence for the host-side doca-dpa ladder when the host side reports clean completions but the DPA-side behaviour is wrong.

It is not for users debugging the tracer binary itself, not a substitute for the live public DOCA DPA Tools guide, not the right place for users learning how to write a DPA kernel (that audience belongs in doca-dpa plus the public DOCA DPA / DPACC / DPA-Comms / DPA-Verbs guides), and not the right place for raw per-instruction cycle profiling (different surface, different tool — route via doca-public-knowledge-map ## DOCA tools).

The tracer is shipped as a CLI binary under /opt/mellanox/doca/tools/, not a library you link against. The skill uses the same kind: tool three-file shape as the rest of the bundle so the agent's task-verb contract is uniform across libraries, services, and tools.

Language scope

doca_dpa_hl_tracer is a C++ host-side CLI. Its inputs are its JSON config file, the DPA-side ELF (the doca_dpa_app-class image produced by DPACC), and a running DPA-side workload that the host-side doca-dpa lifecycle already started. Its outputs are a binary trace file (bin_file) and a human-readable log file (log_file). The skill keeps the workflow guidance language-neutral — the DPA-side workload it traces can be C compiled by DPACC or any other DPA translation unit DPACC accepts — and routes per-language questions to the public DPA / DPACC guides via doca-public-knowledge-map.

When to load this skill

Load this skill when the user is — or the agent needs to — invoke doca_dpa_hl_tracer on a real host with DOCA installed against a BlueField with a DPA processor visible to the host, and the host-side doca-dpa lifecycle has already brought a DPA workload up at least once. Concretely:

  • Capturing a DPA-side trace to localize a DPA kernel's wrong-result or wrong-ordering behaviour when the host-side doca-dpa lifecycle reports clean completions.
  • Capturing a DPA-side trace to localize a DPA-side performance gap (kernel-entry to first-comm latency, RDMA-WR-issue to completion gap, sync-point dwell time) at a granularity above raw cycle counts.
  • Choosing between TRACE and CRIT capture modes based on the bug-vs-overhead tradeoff and the available capture window.
  • Tuning the JSON config (thread priorities, core affinities, file size limits, file-size-limit policy) so the capture itself does not perturb the workload more than the bug it is investigating.
  • Decoding a captured bin_file against the matching DPA-side ELF to render the human-readable event stream.
  • Capturing a side-effect-bounded trace as prerequisite evidence for a host-side doca-dpa ladder step.

Do not load this skill for general DOCA orientation, DPA-side programming model questions, raw cycle profiling, or DOCA / DPACC install. For those, route to doca-public-knowledge-map, doca-dpa, or doca-setup.

What this skill provides

This is a thin loader. Substantive material lives in two companion files:

  • the capabilities companion — what doca_dpa_hl_tracer captures: the DPA programming event taxonomy (kernel entry / exit, sync points, comm primitive calls, RDMA WR submission and completion drain), the two documented capture modes (TRACE for full per-event, CRIT for critical-events only), the trace-overhead-vs-fidelity tradeoff, the config-file shape (receiver / binary-writer / file-writer / printer threads with priority + core affinity, file size limits, file_size_limit_policy), the capture-window + workload-must-be-running invariant, the ELF-must-match-image rule for decode, the version-availability overlay (tracer ↔ doca-dpa library ↔ DPACC compiler), the layered error taxonomy (install / device-binding / image-instrumented / capture-window / decode / overhead-saturated / version / cross-cutting), the observability surface (binary trace file + log file + tool's own stderr), and the safety policy (capture is bounded; tracing is not a production observability surface).
  • the tasks companion — step-by-step workflows for the in-scope task verbs: install (route to host-side DOCA install + DPA prerequisites), configure (mode + JSON config layout + capture window), build (route to install — the binary is shipped, the DPA-side application is user-built by DPACC), modify (refuse — do not patch the binary; modify the JSON config and the invocation instead), run (the capture flow with --mode, --config-file, --output-file), test (iterative loop tuning mode, window, and overhead), debug (walk the error taxonomy), use (consume the decoded trace in a doca-dpa debug session), plus a Deferred task verbs block.

The skill assumes a host where DOCA is already installed at the standard location, a BlueField with a DPA processor is present and visible to the host, the DPACC compiler is installed at a version matched to the host-side DOCA, the DPA-side application image (the ELF the tracer decodes against) is on disk and matches what the doca-dpa lifecycle loaded, and the operator has the privileges the public DOCA DPA Tools guide requires.

What this skill deliberately does not ship

This skill is agent guidance, not a samples or scripts bundle. To keep the boundary clean, it deliberately does not contain — and pull requests should not add:

  • Specific flag strings, event names, or mode tokens beyond what the public DOCA DPA Tools page and --help document. The DPA programming events surface evolves release to release; --help on the installed binary is the authoritative inventory.
  • Pre-baked example traces or expected event timings. Trace output is workload-, DPA-image-, BlueField-, and firmware-specific; a captured example pinned to one setup misleads operators elsewhere.
  • Wrappers, parsers, or rendering scripts in any language that consume the binary trace format. The format is documented; users who want to script against it should read the live guide and write the parser against their installed version.
  • A specific tuning recommendation derived from a single trace. A DPA-side perf decision (move a sync, batch a comm call, change a launch argument) is a workload question and the skill prescribes how to capture and read traces — it refuses to translate a captured gap into a kernel-rewrite recommendation without the user's own analysis.
  • A samples/ or reference/ subtree. This is a thin loader for a shipped CLI; substantive material lives on the public page, in --help, and in doca-dpa.

Loading order

  1. Read this skill first to confirm the user's question is in scope (DPA-side high-level tracing, not DPA-side programming and not raw cycle profiling).
  2. For the event taxonomy, capture modes, overhead tradeoff, JSON config layout, version overlay, error taxonomy, observability, and safety policy, see the capabilities companion.
  3. For the documented invocations and the capture → decode → render workflow — install, configure, build, modify, run, test, debug, use — see the tasks companion.

Related skills

  • doca-dpa — the host-side DPA control library whose loaded application image the tracer captures. Pair them in every DPA debug session: doca-dpa brings the workload up; the tracer captures what the workload does at the DPA programming event layer. Conflating the library with the tracer is the most common DPA-debug first-touch error.
  • doca-debug — the cross-cutting debug ladder. The tracer slots in at the runtime layer as the DPA-side ground truth before any DPA-side perf or correctness conclusion is made.
  • doca-public-knowledge-map — routing to the public DOCA DPA Tools page on docs.nvidia.com and the rest of the public DOCA documentation set.
  • doca-version — canonical DOCA version-handling rules. The ## Version compatibility section in the capabilities companion is a concise overlay that redirects here for the body and adds the tracer ↔ doca-dpa library ↔ DPACC compiler matching rule.
  • doca-setup — env preparation, install verification, DPACC compiler install / verification, BlueField mode (the DPA processor must be exposed before any tracing is meaningful), and the I have no install yet path with the public NGC DOCA container.
  • doca-structured-tools-contract — the bundle's detect → prefer → fall back → report contract for structured helper tools. The command appendix in the tasks companion honors this contract.
  • doca-programming-guide — general DOCA programming patterns shared by every library / tool surface, including the cross-library DOCA_ERROR_* taxonomy this tool's host-side error layer overlays on top of when host-side doca-dpa calls fail in tandem.

The DPA-side companion libraries doca-dpa-comms (comm primitives the DPA kernel itself calls) and doca-dpa-verbs (RDMA verbs the DPA kernel itself calls) are different artifacts that the tracer's DPA programming events surface visibly names; for the DPA-side programming model itself, route through doca-public-knowledge-map to the public DOCA DPA-Comms and DPA-Verbs guides and to the shipped /opt/mellanox/doca/samples/doca_dpa/ samples. This tool traces their use; it does not redefine them.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Move pandas-style table processing to NVIDIA GPUs using cuDF. Choose minimal-change acceleration or explicit GPU code, handle joins and aggregations, compare results with pandas, and use dask-cuDF when data exceeds GPU memory.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Accelerate existing pandas processing with minimal changes
  • Move joins and aggregations to GPU code
  • Compare missing values and ordering with pandas
NVIDIA/skills3.7천2026년 10월 22일 업데이트

Customize NVIDIA Nemotron Voice Agent’s Generic Pipecat example for appointment scheduling, five-field patient intake, or a custom healthcare conversation. Build prompts and tool handlers without a separate backend, then validate conversation behavior, services, and voice interaction.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Build an appointment-scheduling voice agent
  • Collect five patient-intake fields through conversation
  • Design a custom healthcare conversation with tool calls
NVIDIA/skills3.7천2026년 10월 22일 업데이트

Calibrate live cameras by recording their RTSP video streams through VIOS and processing the clips with AutoMagicCalib. The workflow imports settings and alignment files, checks readiness, tracks calibration progress, and retrieves results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Calibrate cameras from live RTSP streams
  • Import settings and alignment files for a camera scene
  • Retrieve evaluation statistics using ground truth data
NVIDIA/skills3.7천2026년 10월 22일 업데이트

Test a running AMC service with the bundled four-camera warehouse dataset. Run camera calibration from upload to completion, then check distance and image projection errors against ground-truth data to verify the installation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Verify a newly installed AMC service
  • Check the sample's calibration errors against ground truth
  • Walk through the sample workflow in Swagger UI
NVIDIA/skills3.7천2026년 10월 22일 업데이트

Calibrate multiple cameras from recorded MP4 files through the AutoMagicCalib REST API. The skill handles project creation, uploads, input verification, progress checks, and evaluation when reference data is supplied, with optional VGGT refinement.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Calibrating cameras from synchronized MP4 recordings
  • Uploading local settings and alignment files
  • Evaluating calibration against ground-truth data
NVIDIA/skills3.7천2026년 10월 22일 업데이트

Set up AutoMagicCalib from NGC release containers using Docker Compose. The skill handles registry authentication, port and path configuration, optional VGGT model download, service startup, and readiness checks for the backend and web interface.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Launching the AutoMagicCalib backend and web interface
  • Configuring service ports and network access
  • Adding the optional VGGT model during setup
NVIDIA/skills3.7천2026년 10월 22일 업데이트

NVIDIA의 스킬 모두 보기

이 스킬의 문제 신고