nf-core/configs: vsc_kul_uhasselt
Edit

genius profile for use on the genius cluster of the VSC HPC.

nf-core/configs: KU Leuven/UHasselt Tier-2 High Performance Computing Infrastructure (VSC)

NB: You will need an account to use the HPC cluster to run the pipeline.

Install Nextflow on the cluster

conda create --name nf-core python=3.12 nf-core nextflow

Note

A nextflow module is available that can be loaded module load Nextflow but it does not support plugins. So it’s not recommended

Set up the environment variables in ~/.bashrc or ~/.bash_profile:

Note

If you have access to dedicated nodes, you can export these as a command separated list. These queues will only be used if specified task requirements are not available in the normal partitions but they are available in dedicated partitions. AMD is considered a dedicated partition.

export SLURM_ACCOUNT="<your-credential-account>"
 
# Comma-separated list of available dedicated partitions (if any)
# For example: export VSC_DEDICATED_QUEUES="dedicated_big_bigmem,dedicated_big_gpu"
export VSC_DEDICATED_QUEUES="<available-dedicated-partitions>"
 
# Needed for running Nextflow jobs
export NXF_HOME="$VSC_SCRATCH/.nextflow"
export NXF_WORK="$VSC_SCRATCH/work"
 
# Needed for running Apptainer containers
export APPTAINER_CACHEDIR="$VSC_SCRATCH/.apptainer/cache"
export APPTAINER_TMPDIR="$VSC_SCRATCH/.apptainer/tmp"
export NXF_CONDA_CACHEDIR="$VSC_SCRATCH/miniconda3/envs"
 
# Optional tower key
# export TOWER_ACCESS_TOKEN="<your_tower_access_token>"
# export NXF_VER="<version>"      # make sure it's larger then 24.10.1

Warning

The current config is setup with array jobs. Make sure nextflow version >= 24.10.1, read array jobs in nextflow you can do this in

export NXF_VER=24.10.1

Make the submission script.

NB: you should go to the cluster you want to run the pipeline on. You can check what clusters have the most free space using following command sinfo --cluster wice|genius.

$ more job.pbs
#!/bin/bash -l
#SBATCH --account=...
#SBATCH --chdir=....
#SBATCH --partition=batch_long
#SBATCH --nodes="1"
#SBATCH --ntasks-per-node="1"
 
# module load Nextflow # does not support plugins
conda activate nf-core
 
nextflow run <pipeline> -profile vsc_kul_uhasselt,<CLUSTER> <Add your other parameters>

NB: You have to specify your credential account, by setting export SLURM_ACCOUNT="<your-credential-account>" else the jobs will fail!

Here the cluster options are:

genius
genius_gpu
wice
wice_gpu
superdome

NB: The vsc_kul_uhasselt profile is based on a selected amount of SLURM partitions. The profile will select to its best ability the most appropriate partition for the job. Including modules with a label containing gpuwill be allocated to a gpu partition when the ‘normal’ genius profile is selected. Select the genius_gpu or wice_gpu profile to force the job to be allocated to a gpu partition. NB: If the module does not have accelerator set, it will determine the number of GPUs based on the requested resources.

Use the --cluster option to specify the cluster you intend to use when submitting the job:

sbatch --cluster=wice|genius job.slurm

All of the intermediate files required to run the pipeline will be stored in the work/ directory. It is recommended to delete this directory after the pipeline has finished successfully because it can get quite large, and all of the main output files will be saved in the results/ directory anyway.

Optional use nf-co2footprint

You can monitor the CO2 usage of your pipeline using the nf-co2footprint plugin using a nextflow version =>24.10.6. Monitoring the CO2 usage is fully optional and will only be activated when running the following command-line.

nextflow run <pipeline> -profile <CLUSTER> -plugins nf-co2footprint@1.0.0 --outdir your_output_folder <Add your other parameters>

Config file

See config file on GitHub

conf/vsc_kul_uhasselt

// Default to /tmp directory if $VSC_SCRATCH scratch env is not available,
// see: https://github.com/nf-core/configs?tab=readme-ov-file#adding-a-new-config
// NOTE: All helper variables and functions have been inlined into closures for
// compatibility with Nextflow config parser v2 (Nextflow >= 25.10).
 
// Perform work directory cleanup when the run has succesfully completed
// cleanup = true
 
// Reduce the job submit rate to about 50 per minute, this way the server won't be bombarded with jobs
// Limit queueSize to keep job rate under control and avoid timeouts
executor {
    submitRateLimit = '50/1min'
    queueSize = 50
    exitReadTimeout = "10min"
}
 
// Add backoff strategy to catch cluster timeouts and proper symlinks of files in scratch to the work directory
process {
    executor      = 'slurm'
    stageInMode   = "symlink"
    stageOutMode  = "rsync"
    errorStrategy = { sleep(Math.pow(2, task.attempt ?: 1) * 200 as long); return 'retry' }
    maxRetries    = 3
}
 
// Specify that singularity should be used and where the cache dir will be for the images
singularity {
    enabled     = true
    autoMounts  = true
    cacheDir    = "${System.getenv('VSC_SCRATCH') ?: '/tmp'}/.singularity"
    pullTimeout = "30 min"
}
 
params {
    config_profile_contact     = 'GitHub: @Joon-Klaps - Email: joon.klaps@kuleuven.be'
    config_profile_url         = 'https://docs.vscentrum.be/en/latest/index.html'
}
 
co2footprint {
    traceFile   = "${params.get('outdir', "$launchDir")}/pipeline_info/co2footprint_trace_${new java.util.Date().format('yyyy-MM-dd_HH-mm-ss')}.txt"
    summaryFile = "${params.get('outdir', "$launchDir")}/pipeline_info/co2footprint_summary_${new java.util.Date().format('yyyy-MM-dd_HH-mm-ss')}.txt"
    reportFile  = "${params.get('outdir', "$launchDir")}/pipeline_info/co2footprint_report_${new java.util.Date().format('yyyy-MM-dd_HH-mm-ss')}.html"
    location = 'BE'
    //pue = 1.33                                   // replace with PUE of your data center
    machineType = 'compute cluster'             // set to 'compute cluster', 'local', or 'cloud'
}
 
env {
    APPTAINER_TMPDIR="${System.getenv('VSC_SCRATCH') ?: '/tmp'}/.apptainer/tmp"
    APPTAINER_CACHEDIR="${System.getenv('VSC_SCRATCH') ?: '/tmp'}/.apptainer/cache"
}
 
// AWS maximum retries for errors (This way the pipeline doesn't fail if the download fails one time)
aws {
    maxErrorRetry = 3
}
 
/*
 * Queue Selection Logic for HPC Environments
 * ===========================================
 * Each process directive closure below contains inlined queue selection logic
 * for compatibility with Nextflow config parser v2 (no top-level def allowed).
 *
 * Constants used throughout:
 *   TIME_THRESHOLD          = 72.h   (threshold for long-running jobs)
 *   MEMORY_THRESHOLD_GENIUS = 175.GB (bigmem threshold for GENIUS)
 *   MEMORY_THRESHOLD_WICE   = 239.GB (high-memory threshold for WICE)
 *
 * Queue selection rules:
 *   GENIUS CPU:  memory >= 175GB → bigmem/bigmem_long; else → batch/batch_long
 *   GENIUS GPU:  memory >= 175GB → gpu_v100; else → gpu_p100/amd (with dedicated queue checks)
 *   WICE CPU:    memory >= 239GB → bigmem,hugemem (time capped at 72h); else → batch/batch_icelake
 *   WICE GPU:    memory >= 239GB → gpu_h100; else → gpu_a100,gpu (time capped at 72h unless dedicated)
 */
 
// Define profiles for each cluster
profiles {
    genius {
        params.config_profile_description = 'genius profile for use on the genius cluster of the VSC HPC.'
 
        process {
            // 768 - 65 so 65GB for overhead, max is 720000MB
            resourceLimits = [ memory: 703.GB, cpus: 36, time: 168.h ]
            beforeScript   = {
                // determineGeniusQueue logic
                def availQueues = (System.getenv('VSC_DEDICATED_QUEUES') ?: '').split(',')
                def isHighMemory = task.memory >= 175.GB
                def isLongRunning = task.time >= 72.h
                def hasDedicatedBigmem = availQueues.contains('dedicated_big_bigmem')
                def queueName
                if (isHighMemory) {
                    queueName = isLongRunning ? (hasDedicatedBigmem ? 'dedicated_big_bigmem' : 'bigmem_long') : 'bigmem'
                } else {
                    queueName = isLongRunning ? 'batch_long' : 'batch'
                }
                'module load cluster/genius/' + queueName.split(',')[0]
            }
            queue          = {
                // determineGeniusQueue logic
                def availQueues = (System.getenv('VSC_DEDICATED_QUEUES') ?: '').split(',')
                def isHighMemory = task.memory >= 175.GB
                def isLongRunning = task.time >= 72.h
                def hasDedicatedBigmem = availQueues.contains('dedicated_big_bigmem')
                if (isHighMemory) {
                    return isLongRunning ? (hasDedicatedBigmem ? 'dedicated_big_bigmem' : 'bigmem_long') : 'bigmem'
                }
                isLongRunning ? 'batch_long' : 'batch'
            }
            clusterOptions = {
                // determineGeniusQueue logic
                def availQueues = (System.getenv('VSC_DEDICATED_QUEUES') ?: '').split(',')
                def isHighMemory = task.memory >= 175.GB
                def isLongRunning = task.time >= 72.h
                def hasDedicatedBigmem = availQueues.contains('dedicated_big_bigmem')
                def queueName
                if (isHighMemory) {
                    queueName = isLongRunning ? (hasDedicatedBigmem ? 'dedicated_big_bigmem' : 'bigmem_long') : 'bigmem'
                } else {
                    queueName = isLongRunning ? 'batch_long' : 'batch'
                }
                queueName =~ /dedicated/ ?
                    "--clusters=genius --account=lp_big_genius_cpu" :
                    "--clusters=genius --account=${System.getenv('SLURM_ACCOUNT') ?: null}"
            }
 
            withLabel: '.*gpu.*'{
                resourceLimits         = [ memory: 703.GB, cpus: 36 , time: 168.h ]
                beforeScript           = {
                    // determineGeniusGpuQueue logic
                    def availQueues = (System.getenv('VSC_DEDICATED_QUEUES') ?: '').split(',')
                    def isHighMemory = task.memory >= 175.GB
                    def isLongRunning = task.time >= 72.h
                    def hasDedicatedGpu = availQueues.contains('dedicated_rega_gpu')
                    def hasAmdGpu = availQueues.contains('amd')
                    def queueName
                    if (isHighMemory) {
                        queueName = isLongRunning ? 'gpu_v100_long' : 'gpu_v100'
                    } else if (isLongRunning) {
                        queueName = hasDedicatedGpu ? 'dedicated_rega_gpu' : (hasAmdGpu ? 'amd_long' : 'gpu_p100_long')
                    } else {
                        queueName = hasAmdGpu ? 'amd' : 'gpu_p100'
                    }
                    'module load cluster/genius/' + queueName.split(',')[0]
                }
                apptainer.runOptions   = '--containall --cleanenv --nv'
                singularity.runOptions = '--containall --cleanenv --nv'
                queue                  = {
                    // determineGeniusGpuQueue logic
                    def availQueues = (System.getenv('VSC_DEDICATED_QUEUES') ?: '').split(',')
                    def isHighMemory = task.memory >= 175.GB
                    def isLongRunning = task.time >= 72.h
                    def hasDedicatedGpu = availQueues.contains('dedicated_rega_gpu')
                    def hasAmdGpu = availQueues.contains('amd')
                    if (isHighMemory) {
                        return isLongRunning ? 'gpu_v100_long' : 'gpu_v100'
                    }
                    if (isLongRunning) {
                        if (hasDedicatedGpu) return 'dedicated_rega_gpu'
                        if (hasAmdGpu) return 'amd_long'
                        return 'gpu_p100_long'
                    }
                    hasAmdGpu ? 'amd' : 'gpu_p100'
                }
                clusterOptions         = {
                    def gpus = task.accelerator?.request ?: Math.max(1, Math.floor((task.cpus ?:1)/9) as int)
                    "--gres=gpu:${gpus} --clusters=genius --account=${System.getenv('SLURM_ACCOUNT') ?: null}"
                }
            }
        }
    }
 
    genius_gpu {
        params.config_profile_description = 'genius_gpu profile for use on the genius cluster of the VSC HPC.'
        apptainer.runOptions              = '--containall --cleanenv --nv'
        singularity.runOptions            = '--containall --cleanenv --nv'
 
        process {
            // 768 - 65 so 65GB for overhead, max is 720000MB
            resourceLimits = [ memory: 703.GB, cpus: 36, time: 168.h]
            beforeScript   = {
                // determineGeniusGpuQueue logic
                def availQueues = (System.getenv('VSC_DEDICATED_QUEUES') ?: '').split(',')
                def isHighMemory = task.memory >= 175.GB
                def isLongRunning = task.time >= 72.h
                def hasDedicatedGpu = availQueues.contains('dedicated_rega_gpu')
                def hasAmdGpu = availQueues.contains('amd')
                def queueName
                if (isHighMemory) {
                    queueName = isLongRunning ? 'gpu_v100_long' : 'gpu_v100'
                } else if (isLongRunning) {
                    queueName = hasDedicatedGpu ? 'dedicated_rega_gpu' : (hasAmdGpu ? 'amd_long' : 'gpu_p100_long')
                } else {
                    queueName = hasAmdGpu ? 'amd' : 'gpu_p100'
                }
                'module load cluster/genius/' + queueName.split(',')[0]
            }
            queue          = {
                // determineGeniusGpuQueue logic
                def availQueues = (System.getenv('VSC_DEDICATED_QUEUES') ?: '').split(',')
                def isHighMemory = task.memory >= 175.GB
                def isLongRunning = task.time >= 72.h
                def hasDedicatedGpu = availQueues.contains('dedicated_rega_gpu')
                def hasAmdGpu = availQueues.contains('amd')
                if (isHighMemory) {
                    return isLongRunning ? 'gpu_v100_long' : 'gpu_v100'
                }
                if (isLongRunning) {
                    if (hasDedicatedGpu) return 'dedicated_rega_gpu'
                    if (hasAmdGpu) return 'amd_long'
                    return 'gpu_p100_long'
                }
                hasAmdGpu ? 'amd' : 'gpu_p100'
            }
            clusterOptions = {
                def gpus = task.accelerator?.request ?: Math.max(1, Math.floor((task.cpus ?:1)/9) as int)
                "--gres=gpu:${gpus} --clusters=genius --account=${System.getenv('SLURM_ACCOUNT') ?: null}"
            }
        }
    }
 
    wice {
        params.config_profile_description = 'wice profile for use on the Wice cluster of the VSC HPC.'
 
        process {
            // max is 2016000
            resourceLimits = [ memory: 1968.GB, cpus: 72, time: 168.h ]
            beforeScript   = {
                // determineWiceQueue logic
                def availQueues = (System.getenv('VSC_DEDICATED_QUEUES') ?: '').split(',')
                def isHighMemory = task.memory >= 239.GB
                def isLongRunning = task.time >= 72.h
                def hasDedicatedQueue = availQueues.contains('dedicated_big_bigmem')
                def queueName
                if (isHighMemory) {
                    if (isLongRunning && hasDedicatedQueue) {
                        queueName = 'dedicated_big_bigmem'
                    } else {
                        task.time = task.time > 72.h ? 72.h : task.time
                        queueName = 'bigmem,hugemem'
                    }
                } else {
                    queueName = isLongRunning ?
                        'batch_long,batch_icelake_long,batch_sapphirerapids_long' :
                        'batch,batch_sapphirerapids,batch_icelake'
                }
                'module load cluster/wice/' + queueName.split(',')[0]
            }
            queue          = {
                // determineWiceQueue logic
                def availQueues = (System.getenv('VSC_DEDICATED_QUEUES') ?: '').split(',')
                def isHighMemory = task.memory >= 239.GB
                def isLongRunning = task.time >= 72.h
                def hasDedicatedQueue = availQueues.contains('dedicated_big_bigmem')
                if (isHighMemory) {
                    if (isLongRunning && hasDedicatedQueue) {
                        return 'dedicated_big_bigmem'
                    }
                    task.time = task.time > 72.h ? 72.h : task.time
                    return 'bigmem,hugemem'
                }
                isLongRunning ?
                    'batch_long,batch_icelake_long,batch_sapphirerapids_long' :
                    'batch,batch_sapphirerapids,batch_icelake'
            }
            clusterOptions = {
                // determineWiceQueue logic
                def availQueues = (System.getenv('VSC_DEDICATED_QUEUES') ?: '').split(',')
                def isHighMemory = task.memory >= 239.GB
                def isLongRunning = task.time >= 72.h
                def hasDedicatedQueue = availQueues.contains('dedicated_big_bigmem')
                def queueName
                if (isHighMemory) {
                    if (isLongRunning && hasDedicatedQueue) {
                        queueName = 'dedicated_big_bigmem'
                    } else {
                        task.time = task.time > 72.h ? 72.h : task.time
                        queueName = 'bigmem,hugemem'
                    }
                } else {
                    queueName = isLongRunning ?
                        'batch_long,batch_icelake_long,batch_sapphirerapids_long' :
                        'batch,batch_sapphirerapids,batch_icelake'
                }
                queueName =~ /dedicated/ ?
                    "--clusters=wice --account=lp_big_wice_cpu" :
                    "--clusters=wice --account=${System.getenv('SLURM_ACCOUNT') ?: null}"
            }
 
            withLabel: '.*gpu.*' {
                resourceLimits         = [ memory: 703.GB, cpus: 64, time: 168.h ]
                apptainer.runOptions   = '--containall --cleanenv --nv'
                singularity.runOptions = '--containall --cleanenv --nv'
                beforeScript           = {
                    // determineWiceGpuQueue logic
                    def availQueues = (System.getenv('VSC_DEDICATED_QUEUES') ?: '').split(',')
                    def isHighMemory = task.memory >= 239.GB
                    def isLongRunning = task.time >= 72.h
                    def hasDedicatedQueue = isHighMemory ?
                        availQueues.contains('dedicated_big_gpu_h100') :
                        availQueues.contains('dedicated_big_gpu')
                    if (isLongRunning && !hasDedicatedQueue) {
                        task.time = task.time > 72.h ? 72.h : task.time
                    }
                    def queueName = isHighMemory ?
                        ((isLongRunning && hasDedicatedQueue) ? 'dedicated_big_gpu_h100' : 'gpu_h100') :
                        ((isLongRunning && hasDedicatedQueue) ? 'dedicated_big_gpu' : 'gpu_a100,gpu')
                    'module load cluster/wice/' + queueName.split(',')[0]
                }
                queue                  = {
                    // determineWiceGpuQueue logic
                    def availQueues = (System.getenv('VSC_DEDICATED_QUEUES') ?: '').split(',')
                    def isHighMemory = task.memory >= 239.GB
                    def isLongRunning = task.time >= 72.h
                    def hasDedicatedQueue = isHighMemory ?
                        availQueues.contains('dedicated_big_gpu_h100') :
                        availQueues.contains('dedicated_big_gpu')
                    if (isLongRunning && !hasDedicatedQueue) {
                        task.time = task.time > 72.h ? 72.h : task.time
                    }
                    isHighMemory ?
                        ((isLongRunning && hasDedicatedQueue) ? 'dedicated_big_gpu_h100' : 'gpu_h100') :
                        ((isLongRunning && hasDedicatedQueue) ? 'dedicated_big_gpu' : 'gpu_a100,gpu')
                }
                clusterOptions         = {
                    // determineWiceGpuQueue logic
                    def availQueues = (System.getenv('VSC_DEDICATED_QUEUES') ?: '').split(',')
                    def isHighMemory = task.memory >= 239.GB
                    def isLongRunning = task.time >= 72.h
                    def hasDedicatedQueue = isHighMemory ?
                        availQueues.contains('dedicated_big_gpu_h100') :
                        availQueues.contains('dedicated_big_gpu')
                    if (isLongRunning && !hasDedicatedQueue) {
                        task.time = task.time > 72.h ? 72.h : task.time
                    }
                    def gpus = task.accelerator?.request ?: Math.max(1, Math.floor((task.cpus ?:1)/16) as int)
                    def queueValue = isHighMemory ?
                        ((isLongRunning && hasDedicatedQueue) ? 'dedicated_big_gpu_h100' : 'gpu_h100') :
                        ((isLongRunning && hasDedicatedQueue) ? 'dedicated_big_gpu' : 'gpu_a100,gpu')
                    queueValue =~ /dedicated_big_gpu_h100/ ? "--clusters=wice --account=lp_big_wice_gpu_h100 --gres=gpu:${gpus}" :
                    queueValue =~ /dedicated_big_gpu/ ? "--clusters=wice --account=lp_big_wice_gpu --gres=gpu:${gpus}" :
                    "--clusters=wice --account=${System.getenv('SLURM_ACCOUNT') ?: null} --gres=gpu:${gpus}"
                }
            }
        }
    }
 
    wice_gpu {
        params.config_profile_description = 'wice_gpu profile for use on the Wice cluster of the VSC HPC.'
        apptainer.runOptions              = '--containall --cleanenv --nv'
        singularity.runOptions            = '--containall --cleanenv --nv'
 
        process {
            // 768 - 65 so 65GB for overhead, max is 720000MB
            resourceLimits = [ memory: 703.GB, cpus: 64, time: 168.h ]
            beforeScript   = {
                // determineWiceGpuQueue logic
                def availQueues = (System.getenv('VSC_DEDICATED_QUEUES') ?: '').split(',')
                def isHighMemory = task.memory >= 239.GB
                def isLongRunning = task.time >= 72.h
                def hasDedicatedQueue = isHighMemory ?
                    availQueues.contains('dedicated_big_gpu_h100') :
                    availQueues.contains('dedicated_big_gpu')
                if (isLongRunning && !hasDedicatedQueue) {
                    task.time = task.time > 72.h ? 72.h : task.time
                }
                def queueName = isHighMemory ?
                    ((isLongRunning && hasDedicatedQueue) ? 'dedicated_big_gpu_h100' : 'gpu_h100') :
                    ((isLongRunning && hasDedicatedQueue) ? 'dedicated_big_gpu' : 'gpu_a100,gpu')
                'module load cluster/wice/' + queueName.split(',')[0]
            }
            queue          = {
                // determineWiceGpuQueue logic
                def availQueues = (System.getenv('VSC_DEDICATED_QUEUES') ?: '').split(',')
                def isHighMemory = task.memory >= 239.GB
                def isLongRunning = task.time >= 72.h
                def hasDedicatedQueue = isHighMemory ?
                    availQueues.contains('dedicated_big_gpu_h100') :
                    availQueues.contains('dedicated_big_gpu')
                if (isLongRunning && !hasDedicatedQueue) {
                    task.time = task.time > 72.h ? 72.h : task.time
                }
                isHighMemory ?
                    ((isLongRunning && hasDedicatedQueue) ? 'dedicated_big_gpu_h100' : 'gpu_h100') :
                    ((isLongRunning && hasDedicatedQueue) ? 'dedicated_big_gpu' : 'gpu_a100,gpu')
            }
            clusterOptions = {
                // determineWiceGpuQueue logic
                def availQueues = (System.getenv('VSC_DEDICATED_QUEUES') ?: '').split(',')
                def isHighMemory = task.memory >= 239.GB
                def isLongRunning = task.time >= 72.h
                def hasDedicatedQueue = isHighMemory ?
                    availQueues.contains('dedicated_big_gpu_h100') :
                    availQueues.contains('dedicated_big_gpu')
                if (isLongRunning && !hasDedicatedQueue) {
                    task.time = task.time > 72.h ? 72.h : task.time
                }
                def gpus = task.accelerator?.request ?: Math.max(1, Math.floor((task.cpus ?:1)/16) as int)
                def queueValue = isHighMemory ?
                    ((isLongRunning && hasDedicatedQueue) ? 'dedicated_big_gpu_h100' : 'gpu_h100') :
                    ((isLongRunning && hasDedicatedQueue) ? 'dedicated_big_gpu' : 'gpu_a100,gpu')
                queueValue =~ /dedicated_big_gpu_h100/ ? "--clusters=wice --account=lp_big_wice_gpu_h100 --gres=gpu:${gpus}" :
                queueValue =~ /dedicated_big_gpu/ ? "--clusters=wice --account=lp_big_wice_gpu --gres=gpu:${gpus}" :
                "--clusters=wice --account=${System.getenv('SLURM_ACCOUNT') ?: null} --gres=gpu:${gpus}"
            }
        }
    }
 
    superdome {
        params.config_profile_description = 'superdome profile for use on the genius cluster of the VSC HPC.'
 
        process {
            clusterOptions = {"--clusters=genius --account=${System.getenv('SLURM_ACCOUNT') ?: null}"}
            beforeScript   = 'module load cluster/genius/superdome'
            // 6000 - 228 so 228GB for overhead, max is 5910888MB
            resourceLimits = [ memory: 5772.GB, cpus: 14, time: 168.h]
 
            queue = { task.time <= 72.h ? 'superdome' : 'superdome_long' }
        }
    }
}

nf-core/configs: vsc_kul_uhasselt
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nf-core/configs: KU Leuven/UHasselt Tier-2 High Performance Computing Infrastructure (VSC)

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nf-core/configs: vsc_kul_uhasselt Edit

nf-core/configs: KU Leuven/UHasselt Tier-2 High Performance Computing Infrastructure (VSC)

Config file

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nf-core/configs: vsc_kul_uhasselt
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