Configuration

Parabricks 3.7.0-1, 8 A100 GPUs on GCP a2-highgpu (O'Connell et al. 2023)

Parabricks 3.7.0-1, 8 A100 GPUs on GCP a2-highgpu as run in Table 1.

Overview

Parabricks 3.7.0-1, 8 A100 GPUs on GCP a2-highgpu as run in Table 1.

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

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

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

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Technical metadata and extraction receipts

Stable ID: model-execution-20261009-config-oconnell2023-gcp-a2-gpu8

areas: dna-genomes
contexts: clinical_research
method types: conventional_pipeline
foundation model eligible: false
reported name: Parabricks 3.7.0-1, 8 A100 GPUs on GCP a2-highgpu
hardware: description: GCP a2-highgpu: 8 NVIDIA A100, 96 vCPUs, 680 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

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: Parabricks 3.7.0-1, 8 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	20.9 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, 8 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 8 (GCP, GPU8, DeepVariant), column 'Cost ($)'
Configuration: Parabricks 3.7.0-1, 8 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	36.5% 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 DeepVariant on Parabricks 3.7.0-1, 8 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 8 (GCP, GPU8, DeepVariant), column '% cost-savings'
Configuration: Parabricks 3.7.0-1, 8 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	42.6 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, 8 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 8 (GCP, GPU8, DeepVariant), column 'Time (min)'
Configuration: Parabricks 3.7.0-1, 8 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	0.71 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, 8 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 8 (GCP, GPU8, DeepVariant), column 'Time (h)'
Configuration: Parabricks 3.7.0-1, 8 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	26.5 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, 8 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 8 (GCP, GPU8, DeepVariant), column 'Fold acceleration'
Configuration: Parabricks 3.7.0-1, 8 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	17.5 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, 8 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 19 (GCP, GPU8, HaplotypeCaller), column 'Cost ($)'
Configuration: Parabricks 3.7.0-1, 8 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	74.2% 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, 8 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 19 (GCP, GPU8, HaplotypeCaller), column '% cost-savings'
Configuration: Parabricks 3.7.0-1, 8 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	35.4 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, 8 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 19 (GCP, GPU8, HaplotypeCaller), column 'Time (min)'
Configuration: Parabricks 3.7.0-1, 8 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	0.59 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 HaplotypeCaller on Parabricks 3.7.0-1, 8 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 19 (GCP, GPU8, HaplotypeCaller), column 'Time (h)'
Configuration: Parabricks 3.7.0-1, 8 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	65.8 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 HaplotypeCaller on Parabricks 3.7.0-1, 8 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 19 (GCP, GPU8, HaplotypeCaller), column 'Fold acceleration'
Configuration: Parabricks 3.7.0-1, 8 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	30.1 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, 8 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 30 (GCP, GPU8, LoFreq), column 'Cost ($)'
Configuration: Parabricks 3.7.0-1, 8 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	− 271% 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, 8 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 30 (GCP, GPU8, LoFreq), column '% cost-savings'
Configuration: Parabricks 3.7.0-1, 8 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	61.4 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, 8 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 30 (GCP, GPU8, LoFreq), column 'Time (min)'
Configuration: Parabricks 3.7.0-1, 8 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	1.02 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, 8 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 30 (GCP, GPU8, LoFreq), column 'Time (h)'
Configuration: Parabricks 3.7.0-1, 8 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	4.5 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, 8 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 30 (GCP, GPU8, LoFreq), column 'Fold acceleration'
Configuration: Parabricks 3.7.0-1, 8 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 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, 8 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 41 (GCP, GPU8, Muse), column 'Cost ($)'
Configuration: Parabricks 3.7.0-1, 8 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	22.9% 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, 8 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 41 (GCP, GPU8, Muse), column '% cost-savings'
Configuration: Parabricks 3.7.0-1, 8 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	28.5 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, 8 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 41 (GCP, GPU8, Muse), column 'Time (min)'
Configuration: Parabricks 3.7.0-1, 8 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	0.48 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, 8 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 41 (GCP, GPU8, Muse), column 'Time (h)'
Configuration: Parabricks 3.7.0-1, 8 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	21.8 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 Muse on Parabricks 3.7.0-1, 8 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 41 (GCP, GPU8, Muse), column 'Fold acceleration'
Configuration: Parabricks 3.7.0-1, 8 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	15.2 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, 8 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 52 (GCP, GPU8, Mutect2), column 'Cost ($)'
Configuration: Parabricks 3.7.0-1, 8 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	− 7.06% 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, 8 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 52 (GCP, GPU8, Mutect2), column '% cost-savings'
Configuration: Parabricks 3.7.0-1, 8 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	31 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, 8 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 52 (GCP, GPU8, Mutect2), column 'Time (min)'
Configuration: Parabricks 3.7.0-1, 8 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	0.52 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, 8 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 52 (GCP, GPU8, Mutect2), column 'Time (h)'
Configuration: Parabricks 3.7.0-1, 8 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	15.7 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, 8 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 52 (GCP, GPU8, Mutect2), column 'Fold acceleration'