Nvshmem Tune Performance

NVIDIAOfficialCreated by NVIDIA NVSHMEM Team

Skill
Meta (authoring, orchestration, planning, memory)
Software development
Apache-2.0
Medium risk

From the author

Route NVSHMEM tuning to data collection, remote transport, NIC-to-PE mapping, or TMA. Do not use for unrelated CUDA, NCCL, or application tuning.

— the description by NVIDIA NVSHMEM Team

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill acts as a central router for NVSHMEM performance optimization. It does not perform tuning itself but instead analyzes user requests to determine the appropriate specialist skill for data collection, remote transport selection, NIC-to-PE mapping, or TMA enablement. It ensures that performance tuning follows a structured workflow, requiring explicit user selection when objectives are underspecified and maintaining context across multi-stage optimization processes.

What it does

Routes performance tuning requests to specialized NVSHMEM sub-skills.
Manages workflows for baseline data collection and targeted optimization.
Requires explicit user selection for underspecified performance improvement requests.
Sequences multi-stage workflows based on user intent and dependencies.
Preserves configuration constraints and context during handoffs to specialists.
Handles scenarios where required specialist skills are unavailable.

When to use it

When you need to optimize NVSHMEM performance but are unsure which specific domain to address.
When you have a clear objective such as baseline collection, transport selection, or NIC mapping.
When you need to coordinate a multi-stage performance tuning process.
When you want to ensure performance tuning follows established NVSHMEM best practices.

Advantages

Prevents misrouting of performance tuning tasks to unrelated domains.
Ensures that technical work is performed by the correct specialist.
Maintains consistency by preserving user constraints and context.
Reduces ambiguity by requiring explicit choices for general tuning requests.

Usage examples

“Collect a baseline for our two-node NVSHMEM all-to-all before tuning it.”: The skill invokes the performance data collection specialist while preserving the two-node and all-to-all constraints.
“Which NVSHMEM remote transport should we use with this InfiniBand cluster?”: The skill identifies the request as an inter-node data path concern and invokes the remote transport selection specialist.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/nvshmem-tune-performance/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip nvshmem-tune-performance.zip -d .agents/skills

Documentation by NVIDIA NVSHMEM Team

Tune NVSHMEM Performance

Purpose

Guide the user to the smallest applicable NVSHMEM performance workflow. Offer optional baseline collection and three focused optimization services while preserving each specialist's inputs, execution boundary, and safety rules.

Do not reproduce the specialists' technical guidance in this router. Invoke:

User goal	Specialist
Collect a baseline, run perftests, or package system and benchmark evidence	`$nvshmem-collect-performance-data`
Select an inter-node remote transport	`$nvshmem-select-remote-transport`
Select HCAs or optimize NIC-to-PE mapping	`$nvshmem-configure-nic-pe-mapping`
Assess, prepare, review, or debug NVSHMEM TMA use	`$nvshmem-enable-tma`

Requirements

Make the four specialist skills listed above available; this router delegates all technical work to them.
Require no API keys, cluster access, NVSHMEM installation, or benchmark tools to select a route. The selected specialist establishes any environment-specific prerequisites before acting.

Inputs

Required

Obtain the user's NVSHMEM performance objective or selected service. When the request is underspecified, require an explicit selection among the workflow alternatives in Section 2 before invoking a specialist.

Optional

Reuse any supplied performance report, raw logs, exact NVSHMEM version and installation prefix, source or kernel path, fabric and provider, job scale, PE-to-GPU binding, GPU/NIC topology, selected transport, tuning constraints, and optimization goal.
Reuse relevant results and unresolved conditions from an earlier specialist in the same workflow.
Ask only for information needed to resolve the routing target. Let the selected specialist inspect the environment or request its own required inputs.

Prefer the current explicit user request, then explicit invocation arguments, then non-conflicting context and prior specialist output. Report conflicting values instead of choosing silently.

Instructions

1. Resolve the Routing Target

Honor an explicitly named specialist or workflow. Otherwise classify the concrete objective:

Route requests for performance baselines, benchmark curves, latency or bandwidth perftests, performance sanity checks, or packaged system evidence to performance data collection.
Route requests about NVSHMEM_REMOTE_TRANSPORT, IBRC, IBDevX, IBGDA, GPUNetIO, UCX, libfabric, remote-network compatibility, or choosing an inter-node data path to remote transport selection.
Route requests about HCA selection, NIC ports, GPU-to-NIC affinity, multi-NIC use, NVSHMEM_HCA_*, or PE-to-NIC assignment to NIC-to-PE mapping.
Route requests about TMA, NVSHMEM_TMA_POLICY, CTA shared-memory registration, direct shared-memory transfers, or preparing a CUDA kernel for TMA to TMA enablement.

Invoke one specialist for a single concrete request. State which specialist is being invoked and why.

2. Resolve an Underspecified Performance Request

When the request states only a general intent to improve or tune NVSHMEM performance, do not select a specialist or run anything. Present these workflow alternatives and require an explicit selection:

Baseline characterization workflow: collect initial performance and system evidence before selecting an optimization domain.
Targeted optimization workflow: proceed without preliminary collection and select one of:
- inter-node remote-transport selection;
- topology-aware NIC-to-PE mapping;
- TMA suitability assessment or enablement for eligible peer-GPU/NVLink kernel paths.

Permit performance-data collection as the terminal scope. Briefly distinguish the three optimization domains without recommending one in the absence of supporting evidence.

3. Sequence Explicit Multi-Stage Workflows

Invoke performance data collection first only when the user requests it, selects the baseline characterization workflow, or explicitly specifies a collection-first sequence. Let that skill obtain its required benchmark selection before any run.
Invoke remote transport selection before NIC-to-PE mapping when both are requested. Pass the selected transport, version, provider, topology evidence, and unresolved conditions into the mapping workflow.
Invoke TMA separately. Do not treat TMA as a remote-transport optimization or infer that inter-node benchmark evidence establishes TMA eligibility.
Invoke no unrequested specialist. Let the active specialist resolve its own prerequisites instead of filling them in.

After data collection completes, invoke an optimization specialist already selected by the user. If no optimization domain was selected, present the three targeted optimization alternatives again and require an explicit selection.

4. Preserve the Handoff

Pass the original request, explicit constraints, exact paths, commands, logs, configuration values, source scope, raw artifacts, resolved facts, and relevant prior specialist output.
Preserve the user's transport settings and tuning constraints during collection and later comparison.
Return the specialist's result without replacing its confidence, unknowns, validation status, or requested follow-up.
Keep benchmark execution, system inspection, source edits, and configuration changes inside the selected specialist's authorization and guardrails.

5. Handle an Unavailable Specialist

If a required skill is unavailable, name it, explain which requested service cannot proceed, and ask the user to make it available or choose another service. Do not imitate or reconstruct the missing skill.

Limitations

Do not run benchmarks, collectors, launchers, or target applications directly from this router.
Do not edit CUDA source, generate transport exports, or synthesize NIC mappings directly from this router.
Do not make data collection an implicit prerequisite merely because an optimization might benefit from later measurement.
Do not promise a performance improvement or declare a regression without the evidence required by the selected specialist.
Do not treat TMA as useful for remote-network traffic; route it only as a separate peer GPU/NVLink optimization workflow.
Do not route unrelated NCCL, CUDA-only, operating-system, or general application-performance questions to these NVSHMEM skills.

Examples

Route a request to collect evidence before choosing an optimization:

User: Collect a baseline for our two-node NVSHMEM all-to-all before tuning it.
Action: Invoke $nvshmem-collect-performance-data and preserve the two-node and all-to-all constraints.

Route an explicit network-configuration request directly:

User: Which NVSHMEM remote transport should we use with this InfiniBand cluster?
Action: Invoke $nvshmem-select-remote-transport because the request concerns an inter-node data path.

Troubleshooting

Error or condition	Cause	Solution
Routing target is underspecified	The request states only a general intent to improve performance.	Present the baseline-characterization and targeted-optimization workflows; require an explicit selection.
A requested specialist is unavailable	Its skill is not installed or enabled.	Name the unavailable specialist and ask the user to enable it or choose another route.
Supplied settings conflict	The request and earlier context specify different topology, transport, or constraints.	Report the conflict and ask which value to preserve before handing off.

Files

ATTRIBUTION.mdAdded by AutoGineer943 B
BENCHMARK.md8.3 KiB
LICENSELicence file added by AutoGineer11.2 KiB
SKILL.md7.7 KiB
evals/evals.json6.7 KiB
skill-card.md3.8 KiB
skill.oms.sig4.5 KiB

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an API key, Needs Node.js

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Attribution and licence

Created by

NVIDIA NVSHMEM Team

Publisher

NVIDIA (Official)

Source

NVIDIA/skills · skills/nvshmem-tune-performance · commit fce3cca

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE-APACHE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

AutoGineer is not affiliated with or endorsed by NVIDIA NVSHMEM Team or NVIDIA.

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