Setting khmer memory usage

If you look at the documentation for the scripts (khmer’s command-line interface) you’ll see a -M parameter that sets the maximum memory usage for any script that uses k-mer counting tables or k-mer graphs. What is this?

khmer uses a special data structure that lets it store counting tables and k-mer graphs in very low memory; the trick is that you must fix the amount of memory khmer can use before running it. (See Pell et al., 2012 and Zhang et al., 2014 for the details.) This is what the -M parameter does.

If you set it too low, khmer will warn you to set it higher at the end. See below for some good choices for various kinds of data.

Note for khmer 1.x users: As of khmer 2.0, the -M parameter sets the -N/--n_tables and -x /--max-tablesize parameters automatically. You can still set these parameters directly if you wish.

The really short version

There is no way (except for experience, rules of thumb, and intuition) to know what this parameter should be up front. So, use the maximum available memory:

-M 16G

for a machine with 16 GB of free memory, for example. The supported suffixes for setting memory usage are K, M, G, and T for kilobyte, megabyte, gigabyte, and terabyte, respectively.

The short version

This parameter specifies the maximum memory usage of the primary data structure in khmer, which is basically N big hash tables of size x. The product of the number of hash tables and the size of the hash tables specifies the total amount of memory used, which is what the -M parameter sets.

These tables are used to track k-mers. If they are too small, khmer will fail in various ways (and will complain), but there is no harm in making it too large. So, the absolute safest thing to do is to specify as much memory as is available. Most scripts will inform you of the total memory usage, and (at the end) will complain if it’s too small.

Life is a bit more complicated than this, however, because some scripts – and – keep ancillary information that will consume memory beyond this table data structure. So if you run out of memory, decrease the table size.

Also see the rules of thumb, below.

The long version

khmer’s scripts, at their heart, represents k-mers in a very memory efficient way by taking advantage of two data structures, Bloom filters and Count-Min Sketches, that are both probabilistic and constant memory. The “probabilistic” part means that there are false positives: the less memory you use, the more likely it is that khmer will think that k-mers are present when they are not, in fact, present.

Digital normalization ( and uses the Count-Min Sketch data structure.

Graph partitioning ( etc.) uses the Bloom filter data structure.

The practical ramifications of this are pretty cool. For example, your digital normalization is guaranteed not to increase in memory utilization, and graph partitioning is estimated to be 10-20x more memory efficient than any other de Bruijn graph representation. And hash tables (which is what Bloom filters and Count-Min Sketches use) are really fast and efficient. Moreover, the optimal memory size for these primary data structures is dependent on the number of k-mers, but not explicitly on the size of k itself, which is very unusual.

In exchange for this memory efficiency, however, you gain a certain type of parameter complexity. Unlike your more typical k-mer package (like the Velvet assembler, or Jellyfish or Meryl or Tallymer), you are either guaranteed not to run out of memory (for digital normalization) or much less likely to do so (for partitioning).

The biggest problem with khmer is that there is a minimum hash number and size that you need to specify for a given number of k-mers, and you cannot confidently predict what it is before actually loading in the data. This, by the way, is also true for de Bruijn graph assemblers and all the other k-mer-based software – the final memory usage depends on the total number of k-mers, which in turn depends on the true size of your underlying genomic variation (e.g. genome or transcriptome size), the number of errors, and the k-mer size you choose (the k parameter) [ see Conway & Bromage, 2011 ]. The number of reads or the size of your data set is only somewhat correlated with the total number of k-mers. Trimming protocols, sequencing depth, and polymorphism rates are all important factors that affect k-mer count.

The bad news is that we don’t have good ways to estimate total k-mer count a priori, although we can give you some rules of thumb, below. In fact, counting the total number of distinct k-mers is a somewhat annoying challenge. Frankly, we recommend just guessing instead of trying to be all scientific about it.

The good news is that you can never give khmer too much memory! k-mer counting and set membership simply gets more and more accurate as you feed it more memory. (Although there may be performance hits from memory I/O, e.g. see the NUMA architecture.) The other good news is that khmer can measure the false positive rate (FPR) and detect dangerously low memory conditions. For partitioning, we actually know what a too-high FPR is – our k-mer percolation paper lays out the math. For digital normalization, we assume that a FPR of 20% is bad. In both cases the data-loading scripts will exit with an error-code.

If you insist on optimizing memory usage, the script will compute the approximate number of k-mers in a data set fairly quickly. This number can be provided to several scripts via the -U option, which will use it to calculate the FPR before processing any input data. If the amount of requested memory yields an unacceptable FPR, the script will complain loudly, giving you the chance to cancel the program before any time is wasted. It will also report the minimum amount of memory required for an acceptable FPR, so that you can immediately re-start the script with the desired settings.

Rules of thumb

For digital normalization, we recommend:

  • -M 8G for any amount of sequencing for a single microbial genome, MDA-amplified or single colony.
  • -M 16G for up to a billion mRNAseq reads from any organism. Past that, increase it.
  • -M 32G for most eukaryotic genome samples.
  • -M 32G will also handle most “simple” metagenomic samples (HMP on down)
  • For metagenomic samples that are more complex, such as soil or marine, start as high as possible. For example, we are using -M 256G for ~300 Gbp of soil reads.

For partitioning of complex metagenome samples, we recommend starting as high as you can – something like half your system memory. So if you have 256 GB of RAM, use -M 128G which will use 128 GB of RAM for the basic graph storage, leaving other memory for the ancillary data structures.

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