Estimate program run time

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Programs used in bioinformatics vary a lot in how efficient their algorithms and implementations are. Different programs may take from minutes to years on the same data set.

As a bioinformatician, you must have some idea about how long the program will run. If the estimated time is too high, you may request more resources (more and faster computers) or even decide not to run it at all, rather than keep it running for two weeks and then kill it without getting any results.

You can estimate the run time of a program in three steps:

  1. Collect the data
  2. Build a model
  3. Extrapolate

In this article, I will demonstrate this technique on a simple example: estimate how long it takes bowtie2 to index the third human chromosome.

Collect the data

First, we download and unpack the chromosome’s nucleotide sequence:

% wget
% gunzip hs_ref_GRCh38.p7_chr3.fa.gz

In order to collect some data, we need to run the program. But if we run the program on the full data set, it may take too long to complete — the very problem we are trying to avoid.

Therefore, we run the program on small subsets of the whole data set. For instance, if we are working with many short sequences of similar lengths, we may generate files consisting of the first \(n\) sequences, as I explain here.

In our example, however, we have a few very long sequences, so we will use the following awk script (which we’ll put in the file head_fasta.awk) to extract the first limit nucleotides:

/^>/ {
  if ((l=length($0)) < limit) {
    limit -= l;
  } else {
    print(substr($0, 0, limit));

Next, we write a shell script ( that will try several numbers of nucleotides, generate a file of that size, run bowtie2-build, and record the time it took to complete.

# We rely on bash to get custom-formatted 'time' output.
# Otherwise, this script should be portable across shells.

# change the format of 'time' output to be the number of wall-clock seconds

# Print the csv header. 'n' stands for the number of nucleotides, 't' stands for
# run time in seconds.
printf "n,t\n"

# iterate over 1M, 2M, ..., 10M nucleotides
for n in `seq 1000000 1000000 10000000` # start, step, end
  # extract the first n nucleotides
  awk -v limit="$n" -f head_fasta.awk hs_ref_GRCh38.p7_chr3.fa > sample.fa
  # run bowtie2-build, noting the time
  # ignore the bowtie's output, and capture time's output from stderr
  t=$(time (bowtie2-build --noauto -f sample.fa sample >/dev/null 2>&1) 2>&1)
  # print the csv row
  printf "%d,%f\n" "$n" "$t"

There are a few things you should take into account when choosing the range of values for \(n\):

  1. Obviously, the values shouldn’t be too high, so that data gathering doesn’t take too long.
  2. The values also shouldn’t be too low; otherwise, the measurements will be noisy and dominated by the fixed costs like program start time. I usually try to come up with a range for which the first (fastest) iteration completes in about a second.
  3. The range of values shouldn’t be too narrow compared to the values themselves. This is so that any non-linear behavior can show itself. For instance, the width of range \(101, 102, \ldots, 110\) is \(110-101=9\), which is less than \(10\%\) compared to the values themselves. Instead, I usually pick the range \(n, 2n, \ldots, 10n\), whose width is nine times bigger than the smallest value.
  4. The number of values shouldn’t be too low, so that a regression model can be fit. Ten is a good starting value.

The script will produce the output in the csv format; we use tee to look at it and write it to the file:

% ./ | tee time.csv

Here’s the output I got on my laptop:


Build the model

Let’s plot our data points. We do this to see how the run time grows with \(n\). Most tools designed to work with big data sets will run in a linear or almost linear time, but it’s better to check.

To load and plot the data, we run the following R code:


time <- read_csv("time.csv")
ggplot(time, aes(n,t)) + geom_point() + geom_smooth(method="lm",se=F)

The blue line is the linear regression line. The fact that the middle points are below the regression line is a sign of a faster-than-linear growth. But the non-linearity is too subtle to be quadratic; so it’s probably \(O(n\log n)\).

To build a model with correlated predictors (\(n\) and \(n\log n\) in this case), it’s better to have more data. Let’s rerun our benchmark script,, with a smaller step size of 100000 and tee the output to time2.csv.

Now we fit the model

\[t = a + b\cdot n + c\cdot n\log n\]

and plot it along the data points. As a rule of thumb, if you include higher-order terms (like \(n\log n\) or \(n^2\)), include all lower-order terms (like \(n\)) as well. Note that lm adds the intercept term \(a\) automatically.

time <- read_csv("time2.csv")
nlm <- lm(t ~ n + I(n*log(n)), data=time)
time <- mutate(time, model=predict(nlm))

ggplot(time) + geom_point(aes(n,t)) + geom_line(aes(n,model), color="blue")

The model with an \(n\log n\) term gives a more realistic fit, so we’ll stick with it.


Let’s find out how many nucleotides are in the whole chromosome:

% awk '/^[^>]/{n+=length($0)} END{print(n)}' hs_ref_GRCh38.p7_chr3.fa

Now we simply plug this number into our R model:

predict(nlm, newdata=list(n=202786747))

So, our prediction is 332 seconds, or 5.5 minutes.

To check this prediction, I actually left bowtie2-build running for a while. It completed in 350 seconds — within 5% of our prediction!

On the other hand, if we went with the linear model, our prediction would have been 176 seconds — a severe underestimation by a factor of 2. The model with \(n \log n\) but without the linear term would have predicted 206 seconds — only somewhat better than the linear model.