Description
Getting Started The artifact comprises a Docker image mlkit-pldi23-parreg.tar.gz. Depending on your system, docker commands might or might not need to be prefixed with sudo. In the following we will leave off sudo, but you may have to add it yourself. You can load the Docker image into Docker with: $ docker load -i mlkit-pldi23-parreg.tar.gz
You can then run the image with: $ docker run -it mlkit-pldi23-parreg:latest
This will put you into a shell inside a directory containing our experimental infrastructure. Run make to begin benchmarking. If it stays running for a few minutes, then everything likely works. Caveat: some of the benchmarks require almost 16GiB of memory, and the above does not test that this is available to Docker. Step by Step Instructions This artifact reproduces Figures 6(a), 6(b) and Tables 1 and 2. The figures and tables in the paper were computed on an Intel Xeon Gold 6230 CPU with 20 cores and hyper-threading disabled. The precise results you obtain are likely to be somewhat different unless you use the same hardware. Hopefully the general trends will remain stable. Caveat: the approach we use for disabling cores when doing speedup measurements is not NUMA-aware, so your results on machines with a complicated NUMA topology (e.g. multiple sockets) may look strange. This affects both MLKit and MPL. Simply running make should run all benchmarks and produce files fig6a.pdf, fig6b.pdf, table1.tex, table2.tex, corresponding to the figures and tables of the paper. While the tables are formatted as LaTeX snippets, they should be (relatively) readable as plain text, although you'll have to get the column headers from the paper. It takes about five hours to run all benchmarks, depending on machine speed. You can lower the number of cores in config.sh to speed up the process. This is particularly useful if your machine has fewer than 20 cores anyway. System Requirements The experimental infrastructure assumes that mpl, mlkit, and mlton are immediately runnable from the command line and that necessary environment variables have been set for them to work. Further, gnuplot must available. The provided Docker image has all this set up already. You must have 16GiB of memory to run the benchmarks. We have noticed that Docker on macOS may have a lower default limit than this. The Docker image also contains a tarball /home/bench/v4.7.3.tar.gz that contains the MLKit 4.7.3 source code for reference. Docker Image For space reasons, the Docker image is very sparse and does not have e.g. text editors installed. The user account has passwordless sudo so you can install more things if you want. Otherwise you can use commands such as docker cp to move data out of the image for inspection on the host system. Consult your favourite search engine for information on how to use Docker if you are unfamiliar. Manifest This section describes every top-level directory and nontrivial file and its purpose. benchmarks/: The benchmark programs. You may wish to look at these. config.sh: Configuration of the benchmark scaffolding; most importantly listing the benchmarks used. You can change this if wish or need to. results/: Contains intermediate results produced by the experimental tooling. tools/: Various SML programs that constitute the experimental tooling. You should not need to look at these. scripts/: Various shell scripts that constitute the experimental tooling. You should not need to look at these, but they are very simple, so you can take a peek if something goes wrong. They all expect to be run from the directory containing the main Makefile. Dockerfile: The file used to build the Docker image. You should use the prebuilt image if possible, but if necessary you can build it yourself with make mlkit-pldi23-parreg.tar.gz (uses sudo). Note that the Dockerfile is not reproducible, so it may or may result in a working image if you try this in the distant future. Makefile: The commands executed when running make. You can extract the commands if you need to run them out of order.
You can then run the image with: $ docker run -it mlkit-pldi23-parreg:latest
This will put you into a shell inside a directory containing our experimental infrastructure. Run make to begin benchmarking. If it stays running for a few minutes, then everything likely works. Caveat: some of the benchmarks require almost 16GiB of memory, and the above does not test that this is available to Docker. Step by Step Instructions This artifact reproduces Figures 6(a), 6(b) and Tables 1 and 2. The figures and tables in the paper were computed on an Intel Xeon Gold 6230 CPU with 20 cores and hyper-threading disabled. The precise results you obtain are likely to be somewhat different unless you use the same hardware. Hopefully the general trends will remain stable. Caveat: the approach we use for disabling cores when doing speedup measurements is not NUMA-aware, so your results on machines with a complicated NUMA topology (e.g. multiple sockets) may look strange. This affects both MLKit and MPL. Simply running make should run all benchmarks and produce files fig6a.pdf, fig6b.pdf, table1.tex, table2.tex, corresponding to the figures and tables of the paper. While the tables are formatted as LaTeX snippets, they should be (relatively) readable as plain text, although you'll have to get the column headers from the paper. It takes about five hours to run all benchmarks, depending on machine speed. You can lower the number of cores in config.sh to speed up the process. This is particularly useful if your machine has fewer than 20 cores anyway. System Requirements The experimental infrastructure assumes that mpl, mlkit, and mlton are immediately runnable from the command line and that necessary environment variables have been set for them to work. Further, gnuplot must available. The provided Docker image has all this set up already. You must have 16GiB of memory to run the benchmarks. We have noticed that Docker on macOS may have a lower default limit than this. The Docker image also contains a tarball /home/bench/v4.7.3.tar.gz that contains the MLKit 4.7.3 source code for reference. Docker Image For space reasons, the Docker image is very sparse and does not have e.g. text editors installed. The user account has passwordless sudo so you can install more things if you want. Otherwise you can use commands such as docker cp to move data out of the image for inspection on the host system. Consult your favourite search engine for information on how to use Docker if you are unfamiliar. Manifest This section describes every top-level directory and nontrivial file and its purpose. benchmarks/: The benchmark programs. You may wish to look at these. config.sh: Configuration of the benchmark scaffolding; most importantly listing the benchmarks used. You can change this if wish or need to. results/: Contains intermediate results produced by the experimental tooling. tools/: Various SML programs that constitute the experimental tooling. You should not need to look at these. scripts/: Various shell scripts that constitute the experimental tooling. You should not need to look at these, but they are very simple, so you can take a peek if something goes wrong. They all expect to be run from the directory containing the main Makefile. Dockerfile: The file used to build the Docker image. You should use the prebuilt image if possible, but if necessary you can build it yourself with make mlkit-pldi23-parreg.tar.gz (uses sudo). Note that the Dockerfile is not reproducible, so it may or may result in a working image if you try this in the distant future. Makefile: The commands executed when running make. You can extract the commands if you need to run them out of order.
| Date made available | 2023 |
|---|---|
| Publisher | Zenodo |