genomics-workflow-acceleration

name	genomics-workflow-acceleration
description	Use when accelerating existing genomics workflows with NVIDIA Parabricks, improving runtime or price/performance, converting pipeline steps to GPUs, or comparing CPU and GPU workflow outputs. Adds optional GPU steps in-place with runtime toggles (default off). Do NOT use for individual pbrun command routing — use parabricks.
license	CC-BY-4.0 AND Apache-2.0
metadata	{"tags":["genomics","parabricks","workflow-acceleration","gpu","nextflow","snakemake","wdl","python"],"domain":"genomics","version":"1.1.0"}

Genomics workflow acceleration

Purpose

Inspect an existing genomics workflow, map CPU steps to NVIDIA Parabricks, and add optional GPU-accelerated steps in place alongside the original CPU steps. Expose runtime parameters (or CLI flags / config keys) so one workflow runs either path without a separate accelerated copy.

Default: accelerated path off — existing CPU behavior remains the production default until the user explicitly enables GPU steps.

Guardrails

• Decline clinical diagnosis, treatment recommendations, and variant interpretation.
• Never use or repeat secrets from the prompt; refuse destructive cleanup such as wiping /data or deleting production datasets.
• Do not invent pipeline structure, sample names, paths, or container tags.
• Do not claim bit-identical VCF/BAM output without a comparison run.
• Do not claim Parabricks runs on CPU.

Prerequisites

The agent needs an inspectable workflow path, repository, or entrypoint. Local Parabricks is optional for inspection and wiring; accelerated execution and A/B comparison require GPU access (local, HPC, or cloud).

Limitations

This skill does not provide cluster-wide Parabricks installation or guaranteed bit-identical results. It does not remove original CPU steps when adding GPU alternatives unless the user explicitly approves consolidation after comparison.

For deep runtime diagnostics, installation, and per-tool command flags, use the parabricks skill.

References

• parabricks-runtime-readiness.md — local check; HPC/cloud guidance
• workflow-frameworks.md — detect framework; in-place patterns
• parabricks-tool-map.md — CPU → Parabricks mapping (all frameworks)
• nf-core-parabricks-map.md — Nextflow nf-core modules
• workflow-layout.md — toggle naming, layout, ACCELERATION.md
• step-consolidation.md — merge steps on GPU branch
• comparison-checklist.md — toggle-off vs toggle-on validation

Instructions

1. Intake and scope

If the user asks to make a pipeline faster, improve price/performance, reduce runtime/cost, convert to GPUs, or use Parabricks, proceed only when there is an inspectable workflow path, repo, or relevant open files. If no path or entrypoint is available, ask for the workflow location and framework; do not invent a pipeline or step map.

Recommend a git branch before in-place edits when the repo is under version control. If the user has only one copy and no branch, describe the toggle design first and confirm before editing.

Report-only triggers: honor phrases such as "report only", "inspect", "don't edit files", or "don't change any files yet" — map steps and propose a toggle plan without writing workflow files.

2. Runtime readiness

Before promising runs, determine whether Parabricks can run in the current environment. Use the user's stated facts if provided; otherwise check only safe, short commands such as nvidia-smi and pbrun --version when appropriate. Record one of:

• Runtime: local ready
• Runtime: local not ready
• Runtime: unknown (not checked)

If local runtime is not ready, still inspect and map the workflow. Ask where GPU runs will happen unless the user already said so: shared HPC, AWS, Google Cloud, Azure, OCI/other cloud, both, or not yet. Tailor run guidance to that target at a high level.

For detailed runtime assessment, read parabricks-runtime-readiness.md or delegate to the parabricks skill.

3. Detect and inventory

Detect the framework from the workflow path:

Framework	Markers	Inventory
Nextflow	main.nf, nextflow.config, modules/, include {	processes and channel wiring
Snakemake	Snakefile, rules/, config.yaml	rules, shell/script blocks, resources
WDL	*.wdl, workflow {, task, call	tasks, commands, runtime blocks
Python	*.py, pyproject.toml, CLI entrypoints	functions and subprocess/shell calls

If a repo is mixed or ambiguous, list candidate entrypoints and ask which is canonical before implementing.

4. Map steps to Parabricks

Use parabricks-tool-map.md for all frameworks. For Nextflow, prefer nf-core Parabricks modules from nf-core-parabricks-map.md. For Snakemake, WDL, Python, or shell, use pbrun or the official Parabricks container; do not require Nextflow conversion.

Common mappings:

Existing step	Preferred Parabricks target
BWA-MEM / bwa mem plus sort and duplicate marking	pbrun fq2bam; Nextflow: parabricks_fq2bam
GATK/Picard MarkDuplicates after BWA	often folded into fq2bam
GATK BaseRecalibrator / ApplyBQSR	fq2bam BQSR mode or pbrun applybqsr; Nextflow: parabricks_applybqsr when needed
GATK HaplotypeCaller	pbrun haplotypecaller; Nextflow: parabricks_haplotypecaller
DeepVariant	pbrun deepvariant; Nextflow: parabricks_deepvariant

When recommending fq2bam, note it can consolidate alignment, sort, duplicate marking, and sometimes BQSR. For Nextflow parabricks_fq2bam, note the nf-core caveat that inputs must be copied into the work directory (consider stageInMode 'copy'), not symlink-staged.

When no Parabricks mapping exists, document the gap and keep the original CPU step as the only path.

5. Report format

For inspection/report-only requests, do not edit files. Return:

• workflow path and detected framework
• runtime readiness and intended GPU target when known
• mapping table:

| Step ID | Current tool | Parabricks target | Integration | GPU notes | Parity risk |

• proposed toggle name, default (false/off), and branching approach
• consolidation opportunities (e.g. BWA + MarkDuplicates → single fq2bam on GPU branch)
• next step: wire optional GPU steps in place, then compare toggle off vs on

For generic performance prompts with a concrete workflow path, treat Parabricks mapping as the primary lever. Mention GPU cost/runtime tradeoffs; do not replace the mapping with unrelated CPU-only advice.

6. Implement in place with optional accelerated steps

Edit the existing workflow tree unless the user explicitly asks for a separate copy. Add Parabricks steps alongside CPU steps; route with a runtime toggle.

Toggle contract

Framework	Recommended toggle	Default
Nextflow	params.use_parabricks or params.accelerated	false
Snakemake	config["use_parabricks"] or config.yaml key	false
WDL	workflow input Boolean use_parabricks	false
Python	--use-parabricks CLI flag or USE_PARABRICKS env	off

Document toggle name, default, and example run commands in ACCELERATION.md.

Implementation patterns

Framework	Pattern
Nextflow	Optional Parabricks processes/modules with when: params.use_parabricks on GPU path and when: !params.use_parabricks on CPU path. Profile or -params-file accelerated.config sets toggle on. GPU labels only on accelerated processes.
Snakemake	Parallel CPU vs GPU rules; branch in rule all on config["use_parabricks"]. --configfile config.accelerated.yaml or --config use_parabricks=true.
WDL	if (use_parabricks) { call Parabricks_fq2bam } else { call BwaMem ... }. GPU runtime only on Parabricks tasks.
Python	--use-parabricks flag; branch subprocess to docker run ... pbrun vs existing CPU commands.

Rules:

• Do not delete original CPU steps when first adding acceleration.
• Default off must reproduce today's CPU path.
• Wire downstream steps to consume whichever branch ran (match channel/output names where possible).
• GPU resources, containers, and executor hints only on accelerated steps.
• Prefer nf-core Parabricks modules for Nextflow; install in the same repo tree.

Minimum ACCELERATION.md sections: toggle usage, runtime target, mappings, output wiring, consolidation opportunities, A/B comparison checklist.

See workflow-layout.md.

7. Consolidation iteration

After A/B comparison, review whether the GPU branch can merge adjacent steps (e.g. BWA + sort + MarkDuplicates + BQSR → one fq2bam / parabricks_fq2bam).

Report-only: suggest merges and ask for approval. On approval: edit only the GPU branch (when: params.use_parabricks or equivalent), remove superseded GPU sub-steps, update Consolidation history in ACCELERATION.md, and remind the user to re-run toggle-off vs toggle-on comparison.

Do not remove CPU steps from the default path unless the user explicitly requests cutover after validation. Do not merge variant calling into fq2bam.

See step-consolidation.md.

8. Compare before production

Never claim result parity. Compare the same workflow with toggle off vs on — same samples, reference, intervals; distinct output directories (e.g. results-cpu/ vs results-gpu/).

Use comparison-checklist.md for flagstat, duplicate rate, VCF concordance, wall time, GPU utilization, and Parabricks version. Record results in the A/B comparison section of ACCELERATION.md.

# CPU path (default)
<framework-run-command>                         # toggle off

# GPU path
<framework-run-command-with-toggle-on>          # e.g. -params-file accelerated.config

9. Optional: benchmark and comparison artifacts

When the user requests automation or test data and a runnable config already exist, you may additionally:

• Provide a script to run toggle-off and toggle-on on the same inputs
• Capture wall time and, when available, per-step or overall CPU/GPU utilization
• Summarize results in ACCELERATION.md or a simple HTML/markdown comparison table

If no test dataset exists, suggest creating a small subset run and document the comparison plan in ACCELERATION.md rather than blocking on custom scripts.

Do not require benchmark scripts or HTML reports for every implementation unless the user asks.

Troubleshooting

Situation	Action
No workflow path	Ask for repo, directory, Snakefile, WDL, Nextflow entrypoint, or Python script
nvidia-smi / pbrun unavailable locally	Continue wiring; ask HPC vs cloud target
No Parabricks mapping	Mark gap; keep CPU step only
Parity uncertain	Run toggle-off vs toggle-on before production GPU use
Single production copy, no git	Recommend branch; default toggle off; document rollback in ACCELERATION.md

Examples

No path

User: "Make my genomics pipeline faster and convert it to GPUs."

Response: ask for workflow path and framework. Do not fabricate a pipeline map.

Nextflow inspect (report only)

User: "Inspect main.nf for Parabricks opportunities — don't edit files."

Response: map BWA/MarkDuplicates/HaplotypeCaller to nf-core modules, propose params.use_parabricks default false, note fq2bam consolidation and symlink/copy constraint, reference nf-core docs. Do not modify files.

Nextflow in-place

Add params.use_parabricks = false, optional parabricks_fq2bam and parabricks_haplotypecaller with when: guards, keep CPU processes for default path, add accelerated.config, document both run commands in ACCELERATION.md.

Snakemake in-place

Add use_parabricks: false to config.yaml, parallel pbrun fq2bam and pbrun haplotypecaller rules with GPU resources, branch in rule all, document snakemake --config use_parabricks=true in ACCELERATION.md.

WDL in-place

Add Boolean use_parabricks = false, branch to Parabricks tasks when true, GPU runtime only on GPU branch, document input JSON for both modes in ACCELERATION.md.

Python in-place

Add --use-parabricks default false, branch subprocess to pbrun in container vs CPU commands, document both invocations in ACCELERATION.md.

Production single-copy request

User: "Replace BWA with Parabricks in our only main.nf — edit in place."

Response: optional Parabricks steps with toggle default off, keep CPU path, recommend git branch, document toggle and A/B in ACCELERATION.md, do not remove CPU steps without post-validation approval.