A benchmark suite for OCaml.

On Ubuntu 18.04.4 LTS you can try the following commands:

$ sudo apt-get install curl git libgmp-dev libdw-dev python3-pip jq bubblewrap \
	pkg-config m4 unzip
$ pip3 install jupyter seaborn pandas intervaltree

# Install OPAM if not available already
$ sh <(curl -sL https://raw.githubusercontent.com/ocaml/opam/master/shell/install.sh)
$ opam init

$ opam install dune.2.6.0

$ git clone https://github.com/ocaml-bench/sandmark.git
$ cd sandmark
$ make ocaml-versions/4.12.0+stock.bench
$ make ocaml-versions/4.12.0+domains.bench
$ make ocaml-versions/4.12.0+domains+effects.bench

You can now find the results in the _results/ folder.

On GNU/Linux you need to have libgmp-dev installed for several of the benchmarks to work. You also need to have libdw-dev installed for the profiling functionality of orun to work on Linux.

You can run make depend that will check for any missing dependencies.

Sandmark uses opam, with a static local repository, to build external libraries and applications. It then builds any sandmark OCaml benchmarks and any data dependencies. Following this it runs the benchmarks as defined in the run_config.json

These stages are implemented in:

• Opam setup: the Makefile handles the creation of an opam switch that builds a custom compiler as specified in the ocaml-versions/<version>.var file. It then installs all the required packages; these packages are statically defined by their opam files in the dependencies directory.

• Runplan: the list of benchmarks which will run along with the measurement wrapper (e.g. orun or perf) is specified in run_config.json. This config file is used to generate dune files which will run the benchmarks.

• Build: dune is used to build all the sandmark OCaml benchmarks that are in the benchmarks directory.

• Execute: dune is used to execute all the benchmarks sepcified in the runplan using the benchmark wrapper defined in run_config.json and specified via the RUN_BENCH_TARGET variable passed to the makefile.

The compiler variant and its configuration options can be specified in a .json file in the ocaml-versions/ directory. It uses the JSON syntax as shown in the following example:

{
  "url" : "https://github.com/ocaml-multicore/ocaml-multicore/archive/parallel_minor_gc.tar.gz",
  "configure" : "-q",
  "runparams" : "v=0x400"
}

The various options are described below:

• url is MANDATORY and provides the web URL to download the source for the ocaml-base-compiler.

• configure is OPTIONAL, and you can use this setting to pass specific flags to the configure script.

• runparams is OPTIONAL, and its values are passed to OCAMLRUNPARAM when building the compiler. Notice this variable is not used for the running of benchmarks, just the build of the compiler

The orun wrapper is packaged in orun/, it collects runtime and OCaml garbage collector statistics producing output in a JSON format. You can use orun independently of the sandmark benchmarking suite, by installing it as an opam pin (e.g. opam install . from within orun/).

Special care is needed if you happen to run sandmark from a directory different than home.

If you get error like # bwrap: execvp dune: No such file or directory, it may be because opam's sandboxing prevent executables to be run from non-standard locations.

In order to get around this issue, you may specify OPAM_USER_PATH_RO=/directory/to/sandmark in order to whitelist this location from sandboxing.

You can execute both serial and parallel benchmarks using the run_all_serial.sh and run_all_parallel.sh scripts. Ensure that the respective .json configuration files have the appropriate settings.

If using RUN_BENCH_TARGET=run_orunchrt then the benchmarks will run using chrt -r 1.

IMPORTANT: chrt -r 1 is necessary when using taskset to run parallel programs. Otherwise, all the domains will be scheduled on the same core and you will see slowdown with increasing number of domains.

You may need to give the user permissions to execute chrt, one way to do this can be:

sudo setcap cap_sys_nice=ep /usr/bin/chrt

A config file can be specified with the environment variable RUN_CONFIG_JSON, and the default value is run_config.json. This file lists the executable to run and the wrapper which will be used to collect data (e.g. orun or perf). You can edit this file to change benchmark parameters or wrappers.

The environment within which a wrapper runs allows the user to configure variables such as OCAMLRUNPARAM or LD_PRELOAD. For example this wrapper configuration:

{
  "name": "orun-2M",
  "environment": "OCAMLRUNPARAM='s=2M'",
  "command": "orun -o %{output} -- taskset --cpu-list 5 %{command}"
}

would allow

$ RUN_BENCH_TARGET=run_orun-2M make ocaml-versions/4.12.0+stock.bench

to run the benchmarks on 4.12.0+stock with a 2M minor heap setting taskset onto CPU 5.

The benchmarks also have associated tags which classify the benchmarks. The current tags are:

• macro_bench - A macro benchmark.
• run_in_ci - This benchmark is run in the CI.
• lt_1s - running time is less than 1s on the turing machine.
• 1s_10s - running time is between 1s and 10s on the turning machine.
• 10s_100s - running time is between 10s and 100s on the turing machine.
• gt_100s - running time is greater than 100s on the turing machine.

The benchmarking machine turing is an Intel Xeon Gold 5120 CPU with 64GB of RAM housed at IITM.

The run_config.json file may be filtered based on the tag. For example,

$ TAG='"macro_bench"' make run_config_filtered.json

filters the run_config.json file to only contain the benchmarks tagged as macro_bench.

The build bench target determines the type of benchmark being built. It can be specified with the environment variable BUILD_BENCH_TARGET, and the default value is buildbench which runs the serial benchmarks. For executing the parallel benchmarks use multibench_parallel. You can also setup a custom bench and add only the benchmarks you care about.

We can obtain throughput and latency results for the benchmarks. For obtaining latency results, we can adjust the environment variable RUN_BENCH_TARGET. The scripts for latencies are present in the pausetimes/ directory. The pausetimes_trunk Bash script obtains the latencies for stock OCaml and the pausetimes_multicore Bash script for Multicore OCaml.

After a run is complete, the results will be available in the _results directory.

Jupyter notebooks are available in the notebooks directory to parse and visualise the results, for both serial and parallel benchmarks. To run the Jupyter notebooks for your results, copy your results to notebooks/ sequential folder for sequential benchmarks and notebooks/parallel folder for parallel benchmarks. It is sufficient to copy only the consolidated bench files, which are present as _results/<comp-version>/<comp-version>.bench. You can run the notebooks with

$ jupyter notebook

You can add new benchmarks as follows:

• Add dependencies to packages: If there are any package dependencies your benchmark has that are not already included in Sandmark, add its opam file to dependencies/packages/<package-name>/<package-version>/opam. If the package depends on other packages, repeat this step for all of those packages. Add the package to PACKAGES variable in the Makefile.

• Add benchmark files: Find a relevant folder in benchmarks/ and add your code to it. Feel free to create a new folder if you don't find any existing ones relevant. Every folder in benchmarks/ has its own dune file; if you are creating a new directory for your benchmark, also create a dune file in that directory and add a stanza for your benchmark. If you are adding your benchmark to an existing directory, add a dune stanza for your benchmark in the directory's dune file.

  Also add you code and input files if any to an alias, buildbench for sequential benchmarks and multibench_parallel for parallel benchmarks. For instance, if you are adding a parallel benchmark benchmark.ml and its input file input.txt to a directory, in that directory's dune file add

  (alias (name multibench_parallel) (deps benchmark.ml input.txt))
  

• Add commands to run your applications: Add an entry for your benchmark run to the appropriate config file; run_config.json for sequential benchmarks and multicore_parallel_run_config.json for parallel benchmarks.

JupyterHub is a multi-user server for hosting Jupyter notebooks. The Littlest JupyterHub (TLJH) installation is capable of hosting 0-100 users.

The following steps can be used for installation on Ubuntu 18.04.4 LTS:

$ sudo apt install python3 python3-dev git curl
$ curl https://raw.githubusercontent.com/jupyterhub/the-littlest-jupyterhub/master/bootstrap/bootstrap.py | \
  sudo -E python3 - --admin adminuser

If you would like to run the the service on a specific port, say "8082", you need to update the same in /opt/tljh/state/traefix.toml file.

You can verify that the services are running from:

$ sudo systemctl status traefik
$ sudo systemctl status jupyterhub

By default, the hub login opens at hostname:15001/hub/login, which is used by the admin user to create user accounts. The users will be able to login using hostname:8082/user/username/tree.

You can also setup HTTPS using Let's Encrypt with JuptyerHub using the following steps:

$ sudo tljh-config set https.enabled true
$ sudo tljh-config set https.letsencrypt.email e-mail
$ sudo tljh-config add-item https.letsencrypt.domains example.domain
$ sudo tljh-config show
$ sudo tljh-config reload proxy

Reference: https://tljh.jupyter.org/en/latest/install/custom-server.html

ctypes 14.0.0 doesn't support multicore yet. A workaround is to update dependencies/packages/ctypes/ctypes.0.14.0/opam to use https://github.com/yallop/ocaml-ctypes/archive/14d0e913e82f8de2ecf739970561066b2dce70b7.tar.gz as the source url.

This is only needed for multicore versions before this commit

The ocaml-update-c command in multicore needs to run with GNU sed. sed will default to a BSD sed on OS X. One way to make things work on OS X is to install GNU sed with homebrew and then update the PATH you run sandmark with to pick up the GNU version.