You need to provide more information than this because there are many moving parts.
The pipeline runs well on small test datasets…, but when I scale up to real-world data, execution slows down significantly. I am using AWS Batch for execution, and my storage is on S3.
A larger dataset will always take longer to analyze than a small one. The key question is: how much longer, and is it within an expected range? Are you running small test sets locally or on an EC2 instance? What differences are you observing between small and large datasets?
Process Parallelization – Some steps don’t seem to utilize multiple CPUs effectively. How can I optimize this?
Nextflow submits each job to AWS Batch, but it is up to the tool within a process to use CPUs effectively. Very few bioinformatics tools are coded to efficiently utilize all available CPUs. Additionally, due to the nature of the computations involved, it’s common to see diminishing returns beyond 8–16 cores.
Nextflow can efficiently utilize single-threaded tools by sharding data and submitting each shard as a separate job. This will require some reworking of your pipeline but will allow for maximum scalability.
Which tools are you running?
Cache Management – I noticed some processes rerun even when inputs haven’t changed. How can I ensure proper caching?
I need more details. Nextflow is very conservative with caching and errs on the side of caution to preserve reproducibility. This means it may rerun a process to ensure results remain consistent across subsequent runs. Many factors can invalidate the cache, from pipeline non-determinism to subtle changes in the process code that may not be immediately obvious.
Data Transfer Bottlenecks – Transferring large files between steps is slow. Would using a different storage option help?
I/O is always the slowest part of any pipeline. There are three key steps to mitigate this:
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Minimize data transfer: Avoid unnecessary file copies, ensure inputs are essential, and reduce hard drive operations. Keep each process as scoped and efficient as possible.
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Optimize transfer overhead: Store data in the same region as compute resources, ensure proper network configurations, and use machines with the fastest possible connections.
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Use the most suitable storage option:
• Blob storage (S3) is highly scalable and cost-effective since it scales to zero when not in use, with practically infinite throughput. However, it requires upload/download operations for each node.
• NFS or Lustre provides attached storage, allowing symlinking to reduce file movement, but maintaining disks 24/7 adds costs.
• Virtual filesystems (e.g., FUSE-based storage) combine the scalability of blob storage with attached storage benefits. However, most aren’t optimized for large bioinformatics files and can introduce issues—this is why we developed Fusion.
The best solution depends on your specific bottleneck.
This is a long answer, but you’ve asked a broad question. If possible, share your code and provide a specific use case—this will make it easier to pinpoint the bottleneck.