advent-of-code VS BenchmarkDotNet

I just did simple BFS on the lava cubes for part 1. For part 2, I just did a BFS on the bounding cube. Total runtime - 500 micro seconds for both parts on my 8 years old laptop: https://github.com/SvetlinZarev/advent-of-code/blob/main/2022/aoc-day-18/src/lib.rs

My solution is pretty simple - top down DP. On each step we can do only one of two things: * open a valve and stay in current position * do not open a vale, but move to a different positions

🦀🦀🦀 RUST 🦀🦀🦀

You can see it in action here: https://github.com/SvetlinZarev/advent-of-code/blob/main/2022/aoc-day-04/src/lib.rs

Rust

Here are the results of my benchmarks, which you can also run

You can calculate the distances between the points found by each scanner. If two scanners report points with the same distance between them, then most probably they are adjacent. Runs in 4ms on my machine: https://github.com/SvetlinZarev/advent-of-code/tree/main/2021/aoc-day-19

You can copy the release profile from here https://github.com/SvetlinZarev/advent-of-code/blob/main/2021/aoc-day-25/Cargo.toml#L8 and copy that directory to enable compilation for the machine's cpu https://github.com/SvetlinZarev/advent-of-code/tree/main/2021/aoc-day-25/.cargo

I want to share my repo for whoever is interested. It contains Rust solutions for:

Mine (rust)runs for 50ms for both parts. I've used just a regular bruteforce approach, so nothing fancy. There are several things I did that reduced the execution time:

Let’s look at the first example you see, when you open up BenchmarkDotnet’s website, or Github page.

Or use BenchmarkDotNet which, among other things to get an accurate benchmark, does JIT warmup outside of measurement.

( https://github.com/dotnet/BenchmarkDotNet ).

You can also do proper statistically correct benchmarking by using - https://github.com/dotnet/BenchmarkDotNet. This will run warmup the jit, gauge the overheads, and run your function many times to give you proper data.

take a look at: https://benchmarkdotnet.org/

Yes, weak references are slower than direct calls. How much slower? Heck if I know offhand. But it's usually pretty easy to set up something with Benchmark .NET and find out if it hurts your use case.

For this purpose we are going to use our beloved BenchmarkDotNet tool.

To quantify and compare the performance of the object mapping strategies discussed earlier, we can employ BenchmarkDotNet.

BenchmarkDotNet is a popular open-source library that, as stated in the repo's README.md, helps us to transform methods into benchmarks, track their performance, and share reproducible measurement experiments. Using BenchmarkDotNet feels similar to writing unit tests. It's very important to note that the library only works with console apps. Finally, we can visualize the results in the terminal where the benchmark ran or in user-friendly formats such as markdown, HTML and CSV. We will explore examples of there formats later in the article.

However, obviously this prevents parallelism within the lock, so this only makes sense if you do some other expensive operation in the parallel loop and the string builder is only a small part of it. Performance wise, it may be better to concatenate the results together after the parallel operation, instead of locking inside the loop. You'll have to benchmark it to know for sure.

We’re of course going to be using BenchmarkDotNet for our benchmarks, and you can find all of the code for these over at GitHub. To start, we need an entry point hook for our single Benchmark class that will be defining the permutations of scenarios that we’d like to run. This will be relatively basic as follows: