Hello, Merry Christmas and Happy Holidays! I just completed Advent of Code 2023, and wanted to jot down a few reflections before I disappear for the holidays. Like last year, I chose to implement my solutions using Rust.

Rust: easier with practice, but still a bit in the way

The good news is that I ran into far fewer problems with Rust this year. There are a few reasons for this, but a lot of it just boils down to experience. I tend to program excessively using lazy iterators, and I began to get a second sense for when I'd need to call .collect() to avoid referencing a freed temporary value. Similarly, when I would get a compiler error (and yes, this still happens a lot!), I would often immediately know how to fix the problem.

That being said, there were still occasions when the language just "got in the way". I had a few solutions where I had to throw in copious amounts of .clone() calls. One salient example was on day 22 when I wanted to memoize a subproblem. But to do so, I also had to pass a value instead of a reference to each sub-problem, which slowed things down dramatically. Anyway, with more work, the solution probably would have been to use references with lifetimes and just ensure that memoization happened during the right lifetimes. But the key phrase there is with more work. And see below -- I was already pretty frustrated.

Chat-GPT and Co-pilot

On this year's Advent of Code, I used Chat-GPT and Co-pilot quite heavily, as I do in practice when coding. I used Chat-GPT mostly to avoid looking up documentation or crates. For example, "How can I add a progress indicator to an iterator in Rust?" or "How can I bind a sub-pattern to a name in Rust?". Given that I hadn't programmed in Rust for a year, I was a bit rusty (ha!)

Co-pilot was also quite useful. It was pretty good at writing out boilerplate code. Advent of Code problems tend to include copious amounts of grid problems, and Co-pilot reduced a lot of the fatigue for helper functions. Define things for the X axis, and it would figure out how to implement the other cases.

I also found that Co-pilot was able to explain weird Rust borrowing problems far better than the compiler. As I wrote about last year, Rust's borrowing rules sometimes don't work that great for my programming style, but Co-pilot was pretty good about explaining problems. I don't have a specific example, but think of searching a vector using vec.iter(), using the found value at some point, and then later trying to move the vector. In my opinion, this definitely makes it easier to program in Rust.

Now, Co-pilot did introduce some headaches as well. Although it was pretty good, it would sometimes introduce problematic completions. For instance, it really liked to complete print statements by adding an extra quote, e.g., println!("This is something: {:?}", something"); Not a huge deal, but it happened enough that it was irritating.

Frustration and Clear-headedness: The Fine Line Between Perseverance and Stubbornness

On some level, I think everyone knows this, but it's still worth saying: You are more effective when thinking clearly. I recall from graduate school that many of my fellow students would pull all nighters and submit papers in the wee hours of the morning. I never did that. My efficiency just plummets as I get tired.

I experienced a pretty funny (in retrospect) example of this on day 22. You can read the problem, it's not really that hard. One of the nice things about programming functionally is that it usually works or completely fails. There's not a lot of in-between. In Advent of Code, this means that if your solution works on the example, it usually works on the real input (ignoring performance). But on day 22, my code did not work on the real input. The problem is pretty simple, so it was infuriating. Rather than taking a break to clear my head, I just kept trying to debug it. I stayed up far past my bed time, becoming more and more frustrated. I even completely rewrote my implementation in Python before giving up and going to bed.

The next morning, I woke up and looked at the problem again. I had a simple idea. I actually had two solutions to the problem. I had a simple but slow solution that I was confident was correct, but was too slow to use on the real input. And I had my fast solution that I thought should be correct, but wasn't. My insight was that I could make "in between" problems by just taking a portion of the real input -- say 10%. And then see if my fast solution agreed with the slow one. It didn't. I then ran Lithium and reduced the input to a minimal example. From here, it was trivial to spot the fundamental problem I had in my recursive algorithm. I fixed it, and my solution worked. This whole process probably only took 30 minutes from the time I woke up. I futilely spent hours the previous night. And the worst part is that, even in the moment, I knew I was acting foolishly. I was just too frustrated to stop.

I don't know if this is universal, but when I am stressed or frustrated, I usually make mistakes, which naturally compound my stress and frustration! There is a fine line between perseverance and stubbornness, and I crossed it on day 22.

Last December, I did most of Advent of Code in Rust, which I had never used before. You can find my solutions here.

The Good

Modern syntax with LLVM codegen

I tend to program functionally, perhaps even excessively so. I try to express most concepts through map, filter, and fold. I tend to enjoy languages that make this easy. Fortunately, this is becoming the norm, even in non-functional languages such as Python, Java and C++.

Perhaps it is not too surprising then that Rust, as a new language, supports this style of programming as well:

let x: i32 = (1..42).map(|x| x+1).sum();
println!("x: {x}");

What is truly amazing about Rust though is how this function code is compiled to x86-64. At optimiation level 1, the computation of x evaluates to

        mov     dword ptr [rsp + 4], 902
        lea     rax, [rsp + 4]

Yes, the compiler is able to unfold and simplify the entire computation, which is pretty neat. But let's look at the code at optimization level 0:

        mov     ecx, 1
        xor     eax, eax
        mov     dl, 1
.LBB5_1:
.Ltmp27:
        movzx   edx, dl
        and     edx, 1
        add     edx, ecx
.Ltmp28:
        add     eax, ecx
        inc     eax
        mov     ecx, edx
.Ltmp29:
        cmp     edx, 42
        setb    dl
.Ltmp30:
        jb      .LBB5_1
.Ltmp31:
        sub     rsp, 72
        mov     dword ptr [rsp + 4], eax
        lea     rax, [rsp + 4]

So our functional computation of a range, a map, and a sum (which is a reduce) is compiled into a pretty simple loop. And keep in mind this is at optimization level 0.

By contrast, let's take a look at how OCaml handles this. First, the included OCaml standard library is not so great, so writing the program is more awkward:

let r = List.init 42 (fun x -> x + 1) in
let x = List.map (fun x -> x+1) r in
let x = List.fold_left (+) 0 x in
Printf.printf "x: %x\n" x

But let's look at the assembly with aggressive optimizations:

camlExample__entry:
        leaq    -328(%rsp), %r10
        cmpq    32(%r14), %r10
        jb      .L122
.L123:
        subq    $8, %rsp
.L121:
        movl    $85, %ebx
        movl    $5, %eax
        call    camlExample__init_aux_432@PLT
.L124:
        call    caml_alloc2@PLT
.L125:
        leaq    8(%r15), %rsi
        movq    $2048, -8(%rsi)
        movq    $5, (%rsi)
        movq    %rax, 8(%rsi)
        movq    camlExample__Pmakeblock_arg_247@GOTPCREL(%rip), %rdi
        movq    %rsp, %rbp
        movq    56(%r14), %rsp
        call    caml_initialize@PLT
        movq    %rbp, %rsp
        movq    camlExample__Pmakeblock_arg_247@GOTPCREL(%rip), %rax
        movq    (%rax), %rax
        call    camlExample__map_503@PLT
.L126:
        call    caml_alloc2@PLT
.L127:
        leaq    8(%r15), %rsi
        movq    $2048, -8(%rsi)
        movq    $5, (%rsi)
        movq    %rax, 8(%rsi)
        movq    camlExample__x_77@GOTPCREL(%rip), %rdi
        movq    %rsp, %rbp
        movq    56(%r14), %rsp
        call    caml_initialize@PLT
        movq    %rbp, %rsp
        movq    camlExample__x_77@GOTPCREL(%rip), %rax
        movq    (%rax), %rax
        movq    8(%rax), %rbx
        movl    $5, %eax
        call    camlExample__fold_left_558@PLT
.L128:
        movq    camlExample__x_75@GOTPCREL(%rip), %rdi
        movq    %rax, %rsi
        movq    %rsp, %rbp
        movq    56(%r14), %rsp
        call    caml_initialize@PLT
        movq    %rbp, %rsp
        movq    camlExample__const_block_49@GOTPCREL(%rip), %rdi
        movq    camlExample__Pmakeblock_637@GOTPCREL(%rip), %rbx
        movq    camlStdlib__Printf__anon_fn$5bprintf$2eml$3a20$2c14$2d$2d48$5d_409_closure@GOTPCREL(%rip), %rax
        call    camlCamlinternalFormat__make_printf_4967@PLT
.L129:
        movq    camlExample__full_apply_240@GOTPCREL(%rip), %rdi
        movq    %rax, %rsi
        movq    %rsp, %rbp
        movq    56(%r14), %rsp
        call    caml_initialize@PLT
        movq    %rbp, %rsp
        movq    camlExample__full_apply_240@GOTPCREL(%rip), %rax
        movq    (%rax), %rbx
        movq    camlExample__x_75@GOTPCREL(%rip), %rax
        movq    (%rax), %rax
        movq    (%rbx), %rdi
        call    *%rdi
.L130:
        movl    $1, %eax
        addq    $8, %rsp
        ret
.L122:
        push    $34
        call    caml_call_realloc_stack@PLT
        popq    %r10
        jmp     .L123

The Bad

Lambda problem

In general, the Rust compiler's error messages are quite helpful. This is important, because dealing (fighting) with the borrow checker is a frequence occurrence. Unfortunately, there are some cases that, despite a lot of effort, I still don't really understand.

Here is a problem that I posted on stack overflow. It's a bit contrived, but it happened because I had a very functional solution to part 1 of an Advent of Code problem. The easiest way to solve the second part was to add a mutation.

Here is the program:

fn main(){
  let v1=vec![1];
  let v2=vec![3];
  let mut v3=vec![];
  v1.iter().map(|x|{
    v2.iter().map(|y|{
      v3.push(*y);
    })
  });
}

And here is the error:

error: captured variable cannot escape `FnMut` closure body
 --> src/main.rs:6:5
  |
4 |     let mut v3=vec![];
  |         ------ variable defined here
5 |     v1.iter().map(|x|{
  |                     - inferred to be a `FnMut` closure
6 | /     v2.iter().map(|y|{
7 | |       v3.push(*y);
  | |       -- variable captured here
8 | |     })
  | |______^ returns a reference to a captured variable which escapes the closure body

The suggestions I received on stack overflow were basically "use loops". This was very disappointing for an example where the closures' scopes are clearly limited.

Anyway, it's still early days for Rust, so I hope that problems like this will be improved over time. Overall, it seems like a great language for doing systems development, but I still think a garbage collected language is better for daily driving.