Timing Loops

The Bencher structure provides a number of functions which implement different timing loops for measuring the performance of a function. This page discusses how these timing loops work and which one is appropriate for different situations.


The simplest timing loop is iter. This loop should be the default for most benchmarks. iter calls the benchmark N times in a tight loop and records the elapsed time for the entire loop. Because it takes only two measurements (the time before and after the loop) and does nothing else in the loop iter has effectively zero measurement overhead - meaning it can accurately measure the performance of functions as small as a single processor instruction.

However, iter has limitations as well. If the benchmark returns a value which implements Drop, it will be dropped inside the loop and the drop function's time will be included in the measurement. Additionally, some benchmarks need per-iteration setup. A benchmark for a sorting algorithm might require some unsorted data to operate on, but we don't want the generation of the unsorted data to affect the measurement. iter provides no way to do this.


iter_with_large_drop is an answer to the first problem. In this case, the values returned by the benchmark are collected into a Vec to be dropped after the measurement is complete. This introduces a small amount of measurement overhead, meaning that the measured value will be slightly higher than the true runtime of the function. This overhead is almost always negligible, but it's important to be aware that it exists. Extremely fast benchmarks (such as those in the hundreds-of-picoseconds range or smaller) or benchmarks that return very large structures may incur more overhead.

Aside from the measurement overhead, iter_with_large_drop has its own limitations. Collecting the returned values into a Vec uses heap memory, and the amount of memory used is not under the control of the user. Rather, it depends on the iteration count which in turn depends on the benchmark settings and the runtime of the benchmarked function. It is possible that a benchmark could run out of memory while collecting the values to drop.


iter_batched and iter_batched_ref are the next step up in complexity for timing loops. These timing loops take two closures rather than one. The first closure takes no arguments and returns a value of type T - this is used to generate setup data. For example, the setup function might clone a vector of unsorted data for use in benchmarking a sorting function. The second closure is the function to benchmark, and it takes a T (for iter_batched) or &mut T (for iter_batched_ref).

These two timing loops generate a batch of inputs and measure the time to execute the benchmark on all values in the batch. As with iter_with_large_drop they also collect the values returned from the benchmark into a Vec and drop it later without timing the drop. Then another batch of inputs is generated and the process is repeated until enough iterations of the benchmark have been measured. Keep in mind that this is only necessary if the benchmark modifies the input - if the input is constant then one input value can be reused and the benchmark should use iter instead.

Both timing loops accept a third parameter which controls how large a batch is. If the batch size is too large, we might run out of memory generating the inputs and collecting the outputs. If it's too small, we could introduce more measurement overhead than is necessary. For ease of use, Criterion provides three pre-defined choices of batch size, defined by the BatchSize enum - SmallInput, LargeInput and PerIteration. It is also possible (though not recommended) to set the batch size manually.

SmallInput should be the default for most benchmarks. It is tuned for benchmarks where the setup values are small (small enough that millions of values can safely be held in memory) and the output is likewise small or nonexistent. SmallInput incurs the least measurement overhead (equivalent to that of iter_with_large_drop and therefore negligible for nearly all benchmarks), but also uses the most memory.

LargeInput should be used if the input or output of the benchmark is large enough that SmallInput uses too much memory. LargeInput incurs slightly more measurement overhead than SmallInput, but the overhead is still small enough to be negligible for almost all benchmarks.

PerIteration forces the batch size to one. That is, it generates a single setup input, times the execution of the function once, discards the setup and output, then repeats. This results in a great deal of measurement overhead - several orders of magnitude more than the other options. It can be enough to affect benchmarks into the hundreds-of-nanoseconds range. Using PerIteration should be avoided wherever possible. However, it is sometimes necessary if the input or output of the benchmark is extremely large or holds a limited resource like a file handle.

Although sticking to the pre-defined settings is strongly recommended, Criterion.rs does allow users to choose their own batch size if necessary. This can be done with BatchSize::NumBatches or BatchSize::NumIterations, which specify the number of batches per sample or the number of iterations per batch respectively. These options should be used only when necessary, as they require the user to tune the settings manually to get accurate results. However, they are provided as an option in case the pre-defined options are all unsuitable. NumBatches should be preferred over NumIterations as it will typically have less measurement overhead, but NumIterations provides more control over the batch size which may be necessary in some situations.

What do I do if my function's runtime is smaller than the measurement overhead?

Criterion.rs' timing loops are carefully designed to minimize the measurement overhead as much as possible. For most benchmarks the measurement overhead can safely be ignored because the true runtime of most benchmarks will be very large relative to the overhead. However, benchmarks with a runtime that is not much larger than the overhead can be difficult to measure.

If you believe that your benchmark is small compared to the measurement overhead, the first option is to adjust the timing loop to reduce the overhead. Using iter or iter_batched with SmallInput should be the first choice, as these options incur a minimum of measurement overhead. In general, using iter_batched with larger batches produces less overhead, so replacing PerIteration with NumIterations with a suitable batch size will typically reduce the overhead. It is possible for the batch size to be too large, however, which will increase (rather than decrease) overhead.

If this is not sufficient, the only recourse is to benchmark a larger function. It's tempting to do this by manually executing the routine a fixed number of times inside the benchmark, but this is equivalent to what NumIterations already does. The only difference is that Criterion.rs can account for NumIterations and show the correct runtime for one iteration of the function rather than many. Instead, consider benchmarking at a higher level.

It's important to stress that measurement overhead only matters for very fast functions which modify their input. For slower functions (roughly speaking, anything at the nanosecond level or larger, or the microsecond level for PerIteration, assuming a reasonably modern x86_64 processor and OS or equivalent) are not meaningfully affected by measurement overhead. For functions which only read their input and do not modify or consume it, one value can be shared by all iterations using the iter loop which has effectively no overhead.

Deprecated Timing Loops

In older Criterion.rs benchmarks (pre 2.10), one might see two more timing loops, called iter_with_setup and iter_with_large_setup. iter_with_setup is equivalent to iter_batched with PerIteration. iter_with_large_setup is equivalent to iter_batched with NumBatches(1). Both produce much more measurement overhead than SmallInput. Additionally. large_setup also uses much more memory. Both should be updated to use iter_batched, preferably with SmallInput. They are kept for backwards-compatibility reasons, but no longer appear in the API documentation.