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Visualize day 1 benchmarks

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Bram 2025-12-02 11:21:37 +01:00
parent 500c885da6
commit 5c55c53f41
13 changed files with 932 additions and 642 deletions
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# Benchmarking setup
In this folder, I run some benchmarks on everyone's code.
## How to benchmark
1. Make sure you have the appropriate software installed. You can do so by
activating the Nix shell as defined in `shell.nix`, or you can view everyone's
installation requirements in the
[software requirements](#Software-requirements) section.
2. Run benchmarks by executing the respective scripts. For example, if you
wish to benchmark Bram's code, you may run `bram.sh` in your terminal. All data
will be collected in the `data/` folder.
3. Once you have collected all the data you wanted to collect, you can open the
Jupyter Notebook by typing `jupyter notebook Benchmarks.ipynb` in the terminal.
4. In the Jupyter Notebook, evaluate all cells and gain the graphs you want.
Graphs are also stored in the `img/` folder.
## Software requirements
To create the measurements, this benchmarking setup uses
[hyperfine](https://github.com/sharkdp/hyperfine). To build the visualization,
this setup uses [Jupyter Notebooks](https://jupyter.org/) - but you can install
the dependencies listed in `requirements.txt` to get all necessary packages
installed.
Bob uses [Python](https://www.python.org/).
Bram uses [Rust](https://rust-lang.org/) at version 1.86.0.
Brechtje uses [Python](https://www.python.org/).
Sander uses [GHCup](https://www.haskell.org/ghcup/) at version 9.6.7. Haskell
typically opens as a REPL from which functions can be evaluated, so some
liberties are taken here. The script is reformatted in the `sander_hs/` folder
and compiled to a binary in order to approximate performance.

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import json
DIRECTORY = []
with open("data/all.json", "r") as fp:
DIRECTORY = json.load(fp)
def generate_datafile(obj):
path = "data/" + obj["name"]
with open(path, 'r') as fp:
benchmark = json.load(fp)
for time in benchmark["results"][0]["times"]:
yield {
"author": obj["author"],
"lang": obj["lang"],
"time": time / obj["runs"],
}
def generate_day(day : int):
for part in [ 1, 2 ]:
for measurement in generate_puzzle(f"{day}-{part}"):
measurement["part"] = part
yield measurement
def generate_puzzle(puzzle : str):
for obj in DIRECTORY:
if puzzle in obj["puzzles"]:
yield from generate_datafile(obj)

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[
{
"name": "bob-d01-p1.json",
"author": "Bob",
"lang": "Python",
"puzzles": [
"1-1"
],
"runs": 1
},
{
"name": "bob-d01-p2.json",
"author": "Bob",
"lang": "Python",
"puzzles": [
"1-2"
],
"runs": 1
},
{
"name": "bram-d01-100.json",
"author": "Bram",
"lang": "Rust",
"puzzles": [
"1-1",
"1-2"
],
"runs": 100
},
{
"name": "brechtje-d01-p1.json",
"author": "Brechtje",
"lang": "Python",
"puzzles": [
"1-1"
],
"runs": 1
},
{
"name": "brechtje-d01-p2.json",
"author": "Brechtje",
"lang": "Python",
"puzzles": [
"1-2"
],
"runs": 1
},
{
"name": "sander-d01-p1.json",
"author": "Sander",
"lang": "Haskell",
"puzzles": [
"1-1"
],
"runs": 1
}
]

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"results": [
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214
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16
bram/benchmarks/requirements.txt Normal file
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@ -0,0 +1,16 @@
backports.tarfile==1.2.0
chardet==5.2.0
fqdn==1.5.1
html5lib==1.1
importlib-metadata==8.0.0
isoduration==20.11.0
jaraco.collections==5.1.0
jsonpointer==3.0.0
notebook==7.4.1
pip-chill==1.0.2
seaborn==0.13.2
tinycss2==1.4.0
toml==0.10.2
tomli==2.0.1
uri-template==1.3.0
webcolors==24.11.1

3
bram/benchmarks/shell.nix
View File

@ -11,8 +11,11 @@ in pkgs.mkShell {
# Jupyter notebook
(python312.withPackages
(py-pkgs: with py-pkgs; [
matplotlib
notebook
pandas
pip-chill
seaborn
])
)
];