HdfBench — Benchmarks for Utopia Data Writing#

This “model” implements benchmarking capabilities for Utopia’s DataIO library, focussing on Hdf5 output. It is implemented as a regular model in order to use all the same structure and capabilities of the Model class and provide a benchmarking platform that is close to the real-world use case.


Benchmarks can be configured completely via the frontend; recompilation is not needed. To that end, HdfBench supplies a set of setup_funcs and bench_funcs, which perform a setup or benchmarking operation, respectively, and then return the time it took for the relevant part of the function to execute. Examples for the configuration of such benchmarks are given below.


If you are writing a run config, the examples below represent the content of the parameter_space -> HdfBench mapping.

Getting started#

# List of benchmarks to carry out
  - simple

# The corresponding configurations
  # Define the names of the setup and benchmark functions; mandatory!
  setup_func: setup_nd
  write_func: write_const

  # All the following arguments are available to _both_ functions
  write_shape: [100]
  const_val: 42.

This will result in the benchmark simple being carried out:

  • It sets up a dataset of shape {num_steps + 1, 100}

  • In each step, it writes vectors of length 100, filled with the value 42..


num_steps is defined not on this level of the configuration, but on the top-most level of the run configuration.

Multiple benchmarks#

One can also define multiple configurations and – using YAML anchors – let them share the other benchmarks’ configuration:

  - simple
  - long_rows

simple: &simple
  setup_func: setup_nd
  write_func: write_const

  write_shape: [100]
  const_val: 42.

  <<: *simple
  write_shape: [1048576]  # == 1024^2

Advanced options#

There are a number of additional configuration flags that change the behaviour of the benchmarks:

Argument name

possible values (default)



boolean (true)

Whether the initial setup is followed by a write operation; time for step 0 is then the combined time for both.


positive float-likes (0.)

Sleep time at the beginning of each step (not measured)


positive float-likes (0.)

Sleep time at the beginning of each benchmark (not measured)

The sleep_* features can make a benchmark more realistic, as they give the operating system time to do its magic, which would, in a real simulation, happen during the computational parts of a simulation step.

Available setup and write functions#




Sets up an n-dimensional dataset with shape {num_steps + 1, write_shape}


In addition to setup_chunks , this allows manually setting the chunk sizes via chunks argument (needs to include time dimension!)


Writes const_val in shape write_shape to the dataset

TODO: add more.


Data output structure#

The times dataset holds the benchmarking times. Its rows correspond to the time step, the columns correspond to the configured benchmarks (in the same order).

For dynamic evaluation of benchmarks, dataset attributes should be used:

  • dims: gives names to dimensions

  • coords_benchmark: which benchmark corresponds to which column of the dataset

  • initial_write: whether the first benchmarked time (row 0) includes a write operation or only the setup time of the dataset

Evaluation scripts#

Evaluation scripts are implemented in the model_plots.HdfBench module. By default, a plot of the benchmarked execution times over the time steps is created for universe 0.

TODO: expand these.