Hi all, I’m new to Nextflow development and working on a project involving pVACtools (pVACseq, pVACfuse, and pVACsplice).
I recently checked the report.html from a run and noticed that some processes show extremely high virtual memory (vmem) usage in the terabytes, which seemed unusual to me.
The tools inside these processes use TensorFlow and generate a large number of intermediate files in /tmp and the output directories (some of which are quite large).
I’m wondering:
What could be causing this massive vmem usage?
Could the high number of files in /tmp or work dirs be the reason?
Is there a recommended way to mitigate or prevent this kind of memory blow-up in Nextflow?
Any advice or experience with similar issues would be much appreciated!
What you’re seeing in the report.html is likely a misunderstanding between virtual memory (vmem) and resident set size (RSS) — and it’s very common to notice seemingly “impossible” vmem values.
What vmem actually measures
Vmem is the total amount of address space a process has requested from the operating system — including:
Code and data actually loaded in RAM
Shared libraries
Memory-mapped files (e.g. large model weights or temp files mapped into memory)
Reserved but unused memory regions
This value can easily be much larger than the physical memory in your system, because most of that address space isn’t actively stored in RAM at once.
What RSS measures
RSS is the portion of a process’s memory that is actually in physical RAM right now.
RSS is the number you usually want to pay attention to when thinking about whether a process is at risk of running out of memory on a node.
Why TensorFlow jobs often have huge vmem
TensorFlow and other frameworks frequently map large model files or datasets into memory. Even if those files live on disk (e.g., in /tmp), mapping them can make vmem look enormous.
Certain Python libraries (NumPy, pandas, etc.) also over-allocate memory arenas or reserve large address ranges for performance reasons — increasing vmem without increasing RSS.
The large number of intermediate files in /tmp or work/ can contribute indirectly if libraries memory-map them rather than reading them sequentially.
Key point:
Huge vmem values alone are not a sign of a problem unless RSS is also high enough to risk exhausting the node’s RAM.
Mitigation options
If you’re hitting real out-of-memory (OOM) errors, then look at RSS and adjust Nextflow’s memory directive for the process accordingly.
If it’s just large vmem numbers with normal RSS, you generally don’t need to “fix” it — but you can reduce temporary file bloat by:
Ensuring TMPDIR points to a directory with enough space (and ideally on local scratch)
Cleaning up large intermediate files inside the process when they’re no longer needed
Using TensorFlow dataset streaming or chunking to avoid mapping huge datasets at once
In short — vmem is more about how much address space the process could touch, not how much memory it’s actually holding in RAM. That’s why terabyte vmem values can appear without any real hardware strain.
Vmem is like the size of all the land you’ve fenced off, while RSS is the part you’ve actually built houses on — the rest is just empty space you could use someday.
