Configuration

Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)

Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu as run in Table 1.

Overview

Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu as run in Table 1.

Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.

Evaluations and results

6 evaluations · 30 results. Different protocols are not a single leaderboard.

Filter evaluations

Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

Applied filters: All linked evaluations

Source checking is not independent reproduction. Release 2026-10-09-8cc1db47c7f9.

Use this model

How it works, versions and access

Strengths, limitations and unresolved questions

Evidence

Source checking verifies the cited claim or transcription. It does not establish independent reproduction.

Evidence table

Inspect claims, sources and review details

Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.

One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.

0 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-10-09-8cc1db47c7f9
Property and statement	Original source and location	Review and provenance

No evidence rows match these filters. Choose another scope or clear the search.

All evidence (JSONL) (gzip)How to read the evidence

Sources and history

Release 2026-10-09-8cc1db47c7f9 · Record review: source checked

1 source records and release history

Accelerating genomic workflows using NVIDIA Parabricks · Original source · BMC Bioinformatics 24:221, published 2023-05-31; PMC10230726 full-text XML

Download this release (gzip)

Technical metadata and extraction receipts

Stable ID: model-execution-20261009-config-oconnell2023-gcp-a2-gpu2

areas: dna-genomes
contexts: clinical_research
method types: conventional_pipeline
foundation model eligible: false
reported name: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu
hardware: description: GCP a2-highgpu: 2 NVIDIA A100, 24 vCPUs, 170 GB RAM
source locator: Table 1 VM-type column; Methods 'GCP configuration', 'AWS configuration', 'DGX configuration'
version: Parabricks v. 3.7.0-1
protocol: Parabricks Germline Pipeline, DeepVariant Germline Pipeline, mutectcaller, somaticsniper_workflow, muse or lofreq, by protocol

Related records

Suggest a correction

Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: DeepVariant execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2	19.4 compute-cost us-dollar · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source DeepVariant on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-deepvariant Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 6 (GCP, GPU2, DeepVariant), column 'Cost ($)'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: DeepVariant execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2	41% cost-saving percent · higher Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source DeepVariant on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-deepvariant Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 6 (GCP, GPU2, DeepVariant), column '% cost-savings'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: DeepVariant execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2	156 runtime minute · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source DeepVariant on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-deepvariant Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 6 (GCP, GPU2, DeepVariant), column 'Time (min)'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: DeepVariant execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2	2.6 runtime hour · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source DeepVariant on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-deepvariant Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 6 (GCP, GPU2, DeepVariant), column 'Time (h)'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: DeepVariant execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2	7.2 speedup unitless · higher Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source DeepVariant on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-deepvariant Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 6 (GCP, GPU2, DeepVariant), column 'Fold acceleration'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: HaplotypeCaller execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2	13.5 compute-cost us-dollar · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source HaplotypeCaller on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-haplotypecaller Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 17 (GCP, GPU2, HaplotypeCaller), column 'Cost ($)'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: HaplotypeCaller execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2	80.1% cost-saving percent · higher Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source HaplotypeCaller on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-haplotypecaller Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 17 (GCP, GPU2, HaplotypeCaller), column '% cost-savings'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: HaplotypeCaller execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2	119 runtime minute · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source HaplotypeCaller on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-haplotypecaller Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 17 (GCP, GPU2, HaplotypeCaller), column 'Time (min)'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: HaplotypeCaller execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2	1.98 runtime hour · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source HaplotypeCaller on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-haplotypecaller Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 17 (GCP, GPU2, HaplotypeCaller), column 'Time (h)'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: HaplotypeCaller execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2	19.6 speedup unitless · higher Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source HaplotypeCaller on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-haplotypecaller Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 17 (GCP, GPU2, HaplotypeCaller), column 'Fold acceleration'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: LoFreq execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: Synthetic tumour BAM: HG002 30x with 198 SNVs added by SomatoSim v1.0.0	19 compute-cost us-dollar · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source LoFreq on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-lofreq Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 28 (GCP, GPU2, LoFreq), column 'Cost ($)'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: LoFreq execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: Synthetic tumour BAM: HG002 30x with 198 SNVs added by SomatoSim v1.0.0	− 134.5% cost-saving percent · higher Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source LoFreq on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-lofreq Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 28 (GCP, GPU2, LoFreq), column '% cost-savings'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: LoFreq execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: Synthetic tumour BAM: HG002 30x with 198 SNVs added by SomatoSim v1.0.0	155 runtime minute · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source LoFreq on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-lofreq Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 28 (GCP, GPU2, LoFreq), column 'Time (min)'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: LoFreq execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: Synthetic tumour BAM: HG002 30x with 198 SNVs added by SomatoSim v1.0.0	2.59 runtime hour · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source LoFreq on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-lofreq Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 28 (GCP, GPU2, LoFreq), column 'Time (h)'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: LoFreq execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: Synthetic tumour BAM: HG002 30x with 198 SNVs added by SomatoSim v1.0.0	1.8 speedup unitless · higher Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source LoFreq on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-lofreq Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 28 (GCP, GPU2, LoFreq), column 'Fold acceleration'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: Muse execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: Synthetic tumour BAM: HG002 30x with 198 SNVs added by SomatoSim v1.0.0	5.4 compute-cost us-dollar · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Muse on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-muse Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 39 (GCP, GPU2, Muse), column 'Cost ($)'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: Muse execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: Synthetic tumour BAM: HG002 30x with 198 SNVs added by SomatoSim v1.0.0	70.1% cost-saving percent · higher Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Muse on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-muse Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 39 (GCP, GPU2, Muse), column '% cost-savings'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: Muse execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: Synthetic tumour BAM: HG002 30x with 198 SNVs added by SomatoSim v1.0.0	44.2 runtime minute · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Muse on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-muse Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 39 (GCP, GPU2, Muse), column 'Time (min)'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: Muse execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: Synthetic tumour BAM: HG002 30x with 198 SNVs added by SomatoSim v1.0.0	0.74 runtime hour · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Muse on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-muse Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 39 (GCP, GPU2, Muse), column 'Time (h)'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: Muse execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: Synthetic tumour BAM: HG002 30x with 198 SNVs added by SomatoSim v1.0.0	14.1 speedup unitless · higher Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Muse on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-muse Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 39 (GCP, GPU2, Muse), column 'Fold acceleration'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: Mutect2 execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: Synthetic tumour BAM: HG002 30x with 198 SNVs added by SomatoSim v1.0.0	4.03 compute-cost us-dollar · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Mutect2 on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-mutect2 Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 50 (GCP, GPU2, Mutect2), column 'Cost ($)'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: Mutect2 execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: Synthetic tumour BAM: HG002 30x with 198 SNVs added by SomatoSim v1.0.0	71.6% cost-saving percent · higher Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Mutect2 on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-mutect2 Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 50 (GCP, GPU2, Mutect2), column '% cost-savings'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: Mutect2 execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: Synthetic tumour BAM: HG002 30x with 198 SNVs added by SomatoSim v1.0.0	32.9 runtime minute · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Mutect2 on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-mutect2 Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 50 (GCP, GPU2, Mutect2), column 'Time (min)'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: Mutect2 execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: Synthetic tumour BAM: HG002 30x with 198 SNVs added by SomatoSim v1.0.0	0.55 runtime hour · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Mutect2 on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-mutect2 Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 50 (GCP, GPU2, Mutect2), column 'Time (h)'
Configuration: Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)	Protocol: Mutect2 execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: Synthetic tumour BAM: HG002 30x with 198 SNVs added by SomatoSim v1.0.0	14.8 speedup unitless · higher Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source Mutect2 on Parabricks 3.7.0-1, 2 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-mutect2 Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 50 (GCP, GPU2, Mutect2), column 'Fold acceleration'