25
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I took the code from kostyas benchmarks for the Rust Brainfuck interpreter and tried to optimize it. There is also a discussion on Reddit about the poor performance of Rust in the Benchmark.

Before my improvements the code needed 16.81s to complete the benchmark and used 6.2Mb of memory, after that the code only needs 4.89s to run but the memory consumption is nearly unchanged.

How can I improve the code and maybe reduce memory usage?

The file with my changes is also on Github.

use std::fs::File;
use std::path::Path;
use std::io::prelude::*;
use std::vec::Vec;
use std::io;
use std::env;
use std::collections::BTreeMap;

struct Tape {
  pos: usize,
  tape: Vec<isize>
}

impl Tape {
  fn new() -> Tape { Tape { pos: 0, tape: vec![0] } }
  fn get(&self) -> isize { self.tape[self.pos] }
  fn getc(&self) -> u8 { self.get() as u8 }
  fn inc(&mut self) { self.tape[self.pos] += 1; }
  fn dec(&mut self) { self.tape[self.pos] -= 1; }
  fn advance(&mut self) { self.pos += 1; if self.tape.len() <= self.pos { self.tape.push(0) } }
  fn devance(&mut self) { if self.pos > 0 { self.pos -= 1; } }
}

struct Program {
  code: Vec<u8>,
  bracket_map: BTreeMap<usize, usize>
}

impl Program {
  fn new(content: Vec<u8>) -> Program {
    let mut code = Vec::new();
    let mut bracket_map = BTreeMap::new();
    let mut leftstack = Vec::new();

    for (pc, b) in content.iter().filter(|&&x| x == b'+' || x == b'-' || x == b'.' || x == b','
            || x == b'<' || x == b'>' || x == b'[' || x == b']').map(|&x| x).enumerate() {
      if b == b'[' {
        leftstack.push(pc);
      } else if b == b']' {
        if let Some(left) = leftstack.pop() {
          bracket_map.insert(left, pc);
          bracket_map.insert(pc, left);
        }
      }
      code.push(b);
    }
    Program{ code: code, bracket_map: bracket_map }
  }

  fn run(&self) {
    let mut pc: usize = 0;
    let mut tape = Tape::new();
    let mut stdout = io::stdout();

    while pc < self.code.len() {
      match self.code[pc] {
        b'+' => tape.inc(),
        b'-' => tape.dec(),
        b'>' => tape.advance(),
        b'<' => tape.devance(),
        b'[' => { if tape.get() == 0 { pc = self.bracket_map[&pc]; } },
        b']' => { if tape.get() != 0 { pc = self.bracket_map[&pc]; } },
        b'.' => { stdout.write(&[tape.getc()]).unwrap(); stdout.flush().unwrap() },
        _ => unreachable!()
      }
      pc += 1;
    }
  }
}

fn main() {
  let mut buf = Vec::new();
  {
    let arg1 = env::args().nth(1).unwrap();
    let path = Path::new(&arg1);
    let mut file = File::open(&path).unwrap();
    file.read_to_end(&mut buf).unwrap();
  }
  Program::new(buf).run();
}

More information: The program is compiled with rustc -C opt-level=3 brainfuck/brainfuck.rs -o brainfuck_rs, the memory usage is determined by a ruby script. C++ performs a lot better regading memory usage it only needs 1.6Mb.

Brainfuck rogram used for benchmark:

 Benchmark brainf*ck program
>++[<+++++++++++++>-]<[[>+>+<<-]>[<+>-]++++++++
[>++++++++<-]>.[-]<<>++++++++++[>++++++++++[>++
++++++++[>++++++++++[>++++++++++[>++++++++++[>+
+++++++++[-]<-]<-]<-]<-]<-]<-]<-]++++++++++.

I am using Rust nightly rust-nightly-bin-1.2.0_2015.06.06-1 on Arch Linux running on 4 Intel(R) Core(TM) i5-2450M CPU @ 2.50GHz.

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  • 2
    \$\begingroup\$ Is there a reason you think the memory usage can be optimized further? The current numbers are pretty good and 6 MB (I suppose you meant MB instead of Mb) is about negligible in the current era. \$\endgroup\$ – Mast Jun 6 '15 at 15:10
  • 1
    \$\begingroup\$ I don't know the rust standard library well enough, but you might be able to squeeze a few more cycles (and perhaps even save memory) by using a hash-table-based container instead of the BTreeMap. \$\endgroup\$ – glampert Jun 6 '15 at 17:31
  • 1
    \$\begingroup\$ A big part of the improvement comes from removing the hash table. Hashing was too slow. \$\endgroup\$ – Pyfisch Jun 6 '15 at 17:38
  • \$\begingroup\$ @glampert Yah; Rust's hash table isn't particularly fast. \$\endgroup\$ – Veedrac Jun 6 '15 at 18:59
  • \$\begingroup\$ Using VecMap is the obvious improvement. After that you're probably either going to need to write a JIT or implement a brainfuck optimizer. You might be able to avoid a few branches with a bit of unsafe, but it's not likely to help much. \$\endgroup\$ – Veedrac Jun 6 '15 at 19:17
4
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Profile

You can only improve what you can measure. So first of all let us run callgrind to check where we spent most of our time:

$ rustc -C opt-level=3 -g brainfuck.rs
$ valgrind --tool=callgrind --dump-instr=yes --collect-jumps=yes --simulate-cache=yes ./brainfuck ahpla.bf
$ callgrind_annotate callgrind.out.*

We will end up with something similar to the following profile:

--------------------------------------------------------------------------------
Profile data file 'callgrind.out.29071' (creator: callgrind-3.11.0)
--------------------------------------------------------------------------------
I1 cache: 32768 B, 64 B, 8-way associative
D1 cache: 32768 B, 64 B, 8-way associative
LL cache: 16777216 B, 64 B, 16-way associative
Timerange: Basic block 0 - 10458028484
Trigger: Program termination
Profiled target:  ./brainfuck ahpla.bf (PID 29071, part 1)
Events recorded:  Ir Dr Dw I1mr D1mr D1mw ILmr DLmr DLmw
Events shown:     Ir Dr Dw I1mr D1mr D1mw ILmr DLmr DLmw
Event sort order: Ir Dr Dw I1mr D1mr D1mw ILmr DLmr DLmw
Thresholds:       99 0 0 0 0 0 0 0 0
Include dirs:
User annotated:
Auto-annotation:  off

--------------------------------------------------------------------------------
            Ir             Dr            Dw  I1mr  D1mr  D1mw  ILmr  DLmr  DLmw
--------------------------------------------------------------------------------
49,487,884,719 13,086,961,954 6,269,022,579 2,411 3,281 1,553 2,167 2,109 1,422  PROGRAM TOTALS

--------------------------------------------------------------------------------
            Ir            Dr            Dw I1mr D1mr D1mw ILmr DLmr DLmw  file:function
--------------------------------------------------------------------------------
16,380,206,099 5,113,395,424 1,588,909,048   12    4    1   12    4    .  brainfuck.rs:brainfuck::main::h21788f29898deec2 [/home/zeta/misc/brainfuck]
14,242,423,355 2,080,022,568 3,380,036,673    2    0    0    2    .    .  /checkout/src/liballoc/btree/search.rs:alloc::btree::search::search_tree::h9cb21dc425abeb49 [/home/zeta/misc/brainfuck]
 4,593,384,291   895,564,901           241    6    0    0    6    .    .  /checkout/src/liballoc/vec.rs:brainfuck::main::h21788f29898deec2
 2,917,813,479             .             .    .    .    .    .    .    .  /checkout/src/libcore/slice/mod.rs:alloc::btree::search::search_tree::h9cb21dc425abeb49
 2,426,711,760   808,903,920             0    1    1    0    1    .    .  /checkout/src/libcore/cmp.rs:alloc::btree::search::search_tree::h9cb21dc425abeb49
 1,848,909,595 1,848,909,577             0    3    0    0    3    .    .  /checkout/src/liballoc/raw_vec.rs:brainfuck::main::h21788f29898deec2
 1,560,016,932   780,008,466   260,002,823    1    1    0    1    .    .  /checkout/src/libcore/option.rs:brainfuck::main::h21788f29898deec2
 1,560,016,926   520,005,642             0    1    0    0    1    .    .  /checkout/src/liballoc/btree/node.rs:alloc::btree::search::search_tree::h9cb21dc425abeb49
 1,300,014,107   260,002,821   260,002,823    .    .    .    .    .    .  /checkout/src/liballoc/btree/map.rs:brainfuck::main::h21788f29898deec2
 1,097,796,554             .             .    .    .    .    .    .    .  /checkout/src/libcore/iter/mod.rs:alloc::btree::search::search_tree::h9cb21dc425abeb49
 1,040,011,284   260,002,821   780,008,463    .    .    .    .    .    .  /checkout/src/liballoc/btree/node.rs:brainfuck::main::h21788f29898deec2
   260,003,033   260,002,823           207    0    0    3    0    0    1  /checkout/src/libcore/ptr.rs:brainfuck::main::h21788f29898deec2

Your program executes a total of 49,487,884,719 instructions. Only 16,380,206,099 of those originate from your main, the rest originates from other functions. We only have (semi-)direct control over 30% of the used instructions. That's bad.

Bottlenecks

Unfortunately rustc inlines Program::new, Program::run and all Tape functions. Just to make sure that new() doesn't take a large part of the program let's remove run for a second and check again:

$ sed -i 's/.run();/;/'
$ rustc -C opt-level=3 -g brainfuck.rs
$ time ./brainfuck ahpla.bf

real 0m0.002s
user 0m0.004s
sys 0m0.000s

As we could have guessed, new() takes almost no time. run() and the BTreeMap's are the culprits.

Memory usage

What about memory usage? Well, that part has improved on its own. Your code uses 1.6 MB on my system. Apparently BTreeMap's implementation has been greatly improved regarding memory usage.

Use rustfmt and clippy

While rustfmt's output is up to personal preference, clippy can provide valuable input. For example map(|&x| x) is cloned.

Use easy to read code for non-bottleneck functions

Your filter dominates new:

for (pc, b) in content.iter().filter(|&&x| x == b'+' || x == b'-' || x == b'.' || x == b','
            || x == b'<' || x == b'>' || x == b'[' || x == b']').map(|&x| x).enumerate() 

That's mouthful and hard to maintain. Since this isn't the performance critical part, let's change it:

for (pc, b) in content.iter().filter(|x| b"+-,.[]<>".contains(x)).cloned().enumerate() 

That's a lot easier to read, isn't it?

Use a \$\mathcal O(1) \$ data structure with low constants for jumps

We can decrease the time tremendously if we use a Vec instead of a BTreeMap. We sacrifice a little bit of memory, but that's almost not noticeable.

BTreeMaps are implemented as B-trees. Trees always have some level of indirection: you have to traverse a tree structure and compare keys or values. But BTreeMap and std::map differ on a fundamental level: BTreeMap has do do additional comparisons to find the correct value in a local array, whereas std::map only needs to find the correct node. The additional cost in BTreeMap isn't noticeable if we traverse the whole tree, but it gets annoying if we look for single elements over and over, especially in a small data set.

So let's use a Vec instead and employ all other suggestions while we're at it:

struct Program {
    code: Vec<u8>,
    jumps: Vec<usize>,
}

impl Program {
    fn new(content: &[u8]) -> Program {
        let code: Vec<u8> = content
            .iter()
            .filter(|x| b"-+,.[]<>".contains(x))
            .cloned()
            .collect();

        let mut jumps = vec![0; code.len() + 1];
        let mut leftstack = Vec::new();

        for (pc, &b) in code.iter().enumerate() {
            if b == b'[' {
                leftstack.push(pc);
            } else if b == b']' {
                if let Some(left) = leftstack.pop() {
                    jumps[left] = pc;
                    jumps[pc] = left;
                }
            }
        }
        Program { code, jumps }
    }

    fn run(&self) {
        let mut pc: usize = 0;
        let mut tape = Tape::new();
        let mut stdout = io::stdout();

        while pc < self.code.len() {
            match self.code[pc] {
                b'+' => tape.inc(),
                b'-' => tape.dec(),
                b'>' => tape.advance(),
                b'<' => tape.devance(),
                b'[' => {
                    if tape.get() == 0 {
                        pc = self.jumps[pc];
                    }
                }
                b']' => {
                    if tape.get() != 0 {
                        pc = self.jumps[pc];
                    }
                }
                b'.' => {
                    stdout.write(&[tape.getc()]).unwrap();
                    stdout.flush().unwrap()
                }
                _ => unreachable!(),
            }
            pc += 1;
        }
    }
}

How does this fare against your variant?

$ ./bench.rb ./bf-cr ahpla.bf
ZYXWVUTSRQPONMLKJIHGFEDCBA
3.10s, 1.5Mb

$ ./bench.rb ./brainfuck-orig ahpla.bf
ZYXWVUTSRQPONMLKJIHGFEDCBA
9.73s, 1.5Mb

Note that the memory usage reported by bench.rb isn't that stable. Either way, we can see that the Vec doesn't take any noticeable memory in this case, but tremendously improves the runtime.

This is a detail we only could find since we measured with callgrind and saw that BTreeMap specific methods contributed to the runtime.

Ending remarks

kostya later added a variant that uses a minified AST. That one is even faster but uses more memory since we don't need to jump anymore. One can probably improve that one, though.

Other than the mentioned suggestions your code is easy to read and understand. Well done.

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