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usage.txt
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usage.txt
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To use ELBA on perlmutter, follow these steps:
* Download prerequisites:
$> git clone https://github.com/PASSIONLab/ELBA . && cd ELBA
$> git clone https://github.com/PASSIONLab/CombBLAS
* Load compiler:
$> module load PrgEnv-gnu
* In order to compile ELBA, you have to provide the k-mer size, lower, and upper frequency
bounds as inputs. For example, if the k-mer size was 31, the lower frequency bound 15,
and upper frequency bound 35, you would do the following:
$> make K=31 L=15 U=35 -j8
* The executable is named "elba" and is found in the ELBA directory.
* To run ELBA on a FASTA dataset named "reads.fa", first index the file with
the command:
$> module load spack
$> spack load samtools
$> samtools faidx reads.fa
ELBA will crash if the input FASTA is not indexed. You may need to run the following
command prior to loading spack:
$> module load cpu
which will disable the gpu module. However, remember to reload the gpu module before
running elba binary, or an error may occur during runtime.
* ELBA must be run with a square number of processors. Suppose we want to run ELBA
with 64 MPI tasks on a single perlmutter node. The slurm command would then be
$> srun -N 1 -n 64 -c 2 --cpu_bind=cores ./elba reads.fa
* Each perlmutter node has 128 cores to which we can bind each MPI tasks. Here are
some further examples of valid slurm commands for ELBA:
# 4 MPI tasks
$> srun -N 1 -n 4 --cpu_bind=cores ./elba reads.fa
# 1024 MPI tasks
$> srun -N 8 -n 1024 -c 2 --cpu_bind=cores ./elba reads.fa
etc.
* ELBA can also take further input parameters as follows:
Usage: elba [options] <reads.fa>
Options: -x INT x-drop alignment threshold [15]
-A INT matching score [1]
-B INT mismatch penalty [1]
-G INT gap penalty [1]
-o STR output file name prefix "elba"
-h help message