How to boost HISAT2 on HPC systems?

Optimize File I/O Operations

• Use parallel file systems like Lustre or GPFS to efficiently handle large datasets. This will reduce the I/O bottleneck when reading or writing alignment files.

 

• Utilize multi-threading (when supported by the file system) by ensuring the HISAT2 output is being written efficiently, possibly through custom scripts or commands that parse output in parallel.

Tune HISAT2 Parameters

• Increase the number of threads with the `--threads` parameter to fully utilize the available CPU resources of the HPC cluster.

 

• Adjust the `--dta-cufflinks` parameter if using Cufflinks downstream, optimizing for computational efficiency in this context.

Leverage HPC Scheduler Features

• Use the job scheduler's capabilities, like Slurm or PBS, to allocate resources effectively. Set the node and CPU allocations to match the threads you intend to use with HISAT2.

 

• Enable job dependency features, allowing HISAT2 jobs to run sequentially or in parallel depending on data dependencies, thus optimizing workflow execution.

Utilize Shared Memory Resources

• Take advantage of shared memory within nodes by running single-node, multi-threaded jobs to reduce inter-node communication overhead, if the data allows.

 

• Ensure proper memory allocation to prevent swap usage, potentially speeding up runtime by utilizing `--memory` or similar options in the job scheduler.

Profile and Monitor Performance

• Regularly use profiling tools to identify bottlenecks. Tools like `perf`, and similar HPC-specific profilers, can provide insights into where the most time and resources are being spent.

 

• Monitor resource usage in real-time with commands like `top`, `htop`, or cluster-specific tools, adjusting allocations as necessary based on observations.

Implement Parallel Execution

• For batch processing, divide input data across multiple jobs within the HPC environment, running multiple HISAT2 alignments simultaneously on different data chunks.

 

• Consider using pipelines, such as those provided by Snakemake or Nextflow, to automate and parallelize the execution of HISAT2, integrating seamlessly with HPC environments.

Fine-tune Indexing

• Pre-build indices using the `hisat2-build` command and store them in a shared, high-performance location accessible to all compute nodes, reducing initial setup time for each alignment job.

 

• Optimize index memory usage by adjusting the build parameters to suit the specific genomic data, potentially reducing runtime during alignment.