Rust Faster!

03 October 2015

This is a co-production with Veedrac, who wrote most of the awesome technical details.

A staple of all performance discussion is the great Computer Language Benchmarks Game. Despite the fact that many of the benchmarks say more about the resourcefulness of certain members of the respective programming language communities than the languages themselves, this site is often cited in discussions of the relative merits of programming languages.

Since one of the core tenets of Rust is “Fast”, we have some motivation to score well on those benchmarks. Unfortunately, we’re lagging behind C/C++ in some of them right now. teXitoi, Veedrac and yours truly set out semi-independently to rectify the situation. The following discusses the performance tricks we pulled.

fasta and fasta-redux

I am actually the one responsible for the slow fasta_redux implementation. I submitted it as a baseline to benchmark against, but didn’t get around to actually optimize it. However, it bugged me, so I at least inserted a BufWriter to at least have faster IO.

Meanwhile, Veedrac had a few insights on how to optimize the fasta benchmark. The PR is here.

In this case, for the single-core benchmarks Haskell is in the lead. This is surprising, because Haskell rarely outpaces well-tuned Fortran or C. So the Haskell program must be doing something very right.

There are really two different compute-intensive parts to the problem and the code has a clever attempt for both. The first is to repeat a string with line breaks. Of course, Rust’s solution was to make a cyclic iterator and pass that to the line-breaking algorithm, which is roughly

let mut it = alu .as_bytes () .iter () .cycle () .map (| c | * c ); for i in 0 .. n { line [ i ] = it .next () .unwrap (); } line [ n ] = '

' as u8 ;

Elegant and pretty fast, but not blindingly fast. The Haskell code does a quite clever alternative; it writes line-sized slices directly out of the input string:

hPutBuf stdout (plusPtr buf pos) lineLen B.putStrLn ""

which approximately translated to Rust means println!("{}", buf[pos..pos+line_len]) sans bounds checks. To avoid special casing when the line cycles past the end of the string, the string is extended by a line’s length. Of course, this translates very well to Rust – only without the unsafety!

The second problem is to write the output generated live from a simple random number generator, passed through a small but frequent linear search. However, since the generator only produces 139968 separate numbers, the Haskell code just caches the results from the linear search in an array for each possible input. This means we already know the answer of the linear search for any result the RNG could throw at us.

On that note, the RNG is predefined by the Benchmarks Game. It’s a simple Linear Congruential Generator with defined stride and modulus. This ensures that all implementations use the same stream of random numbers, but the algorithm has another interesting property: It’s possible to fast-forward the generator using a modulus-power function (Sn mod M), which can be efficiently calculated in O(log n).

This is used to split the RNG into multiple independent streams that run fully parallel on all CPUs. The previous multicore entry could only parallelize adding the line breaks, which a more efficient loop largely removes the need for. Parallelizing everything, though, makes a big difference.

The fasta_redux benchmark is very similar; the output is actually the same. However, the size and shape of the lookup table is predefined by the benchmark rules (and per the rules we need some constant factor to index into the table and also need to do a linear search from our index). So apart from that lookup, the code is exactly the same, and shares all of its awesomeness.

spectralnorm

Rust didn’t do as good as it could on this benchmark because the Benchmarks Game site uses stable which cannot use unstable APIs, notably this means we cannot use SIMD directly. teXitoi had the idea to rewrite the hottest code in a way that lets LLVM auto-vectorize it. He wrote about it in an issue on his repo and I implemented it.

The hot function in question:

fn A ( i : usize , j : usize ) -> f64 { (( i + j ) * ( i + j + 1 ) / 2 + i + 1 ) as f64 }

And here is the version that uses custom autovectorizable usizex2 and f64x2 types:

fn Ax2 ( i : usizex2 , j : usizex2 ) -> f64x2 { (( i + j ) * ( i + j + usizex2 ( 1 , 1 )) / usizex2 ( 2 , 2 ) + i + usizex2 ( 1 , 1 )) .into () }

This looks only a smidgen more complex than the original, but it also does twice the work. The implementations of usizex2 and f64x2 are actually pretty boring; they just distribute the operations to their contents, e.g.

struct usizex2 ( usize , usize ); impl std :: ops :: Add for usizex2 { type Output = Self ; fn add ( self , rhs : Self ) -> Self { usizex2 ( self . 0 + rhs . 0 , self . 1 + rhs . 1 ) } } //... similar implementations for Div and Mul

Note that this doesn’t do anything fancy, it really only wraps the two operations. LLVM does the cool stuff behind the scenes. With this. calling the A -function changes from:

let bot = f64x2 ( a ( i , j ), a ( i , j + 1 ));

to

let bot = a ( usizex2 ( i , i ), usizex2 ( j , j + 1 ));

for a nice 20% speedup. Note that the official Rust version can and does use nightly and thus SIMD. In a few weeks this interim version can hopefully be replaced again, yielding to the even faster official version.

k_nucleotide

k_nucleotide is a test of how fast your Hash map is. The game has mostly devolved to one of specialization – specialized maps, specialized hashes, etc., so there’s no surprise the Rust code uses a custom hash map in its current incantation. Unfortunately, it looks like this:

struct Entry { code : Code , count : usize , next : Option < Box < Entry >> , } struct Table { items : Vec < Option < Box < Entry >>> }

For anyone familiar with hash maps, this is a standard chained hash map with linked lists. Linked lists aren’t great for cache efficiency, especially as every lookup involves at least one extra pointer dereference. Here’s a rough diagram:

items: vec![ . . . . . . . . . ] | | | | | 0 2 8 C E | | 1 9

One quick improvement is to remove the first Box used:

struct Table { items: Vec<Option<Entry>> // was Vec<Option<Box<Entry>>> }

which results in

items: vec![ 0 2 . . 8 . C E . ] | | 1 9

Unfortunately, this is still suboptimal when the vector starts getting crowded. The updated code uses Robin Hood Hashing, a simple type of open addressing known for its high speed and great efficiency. It’s even used in Rust’s HashMap ! The main property is that the vector ends up sorted by hash, which in our case is just the key:

items: vec![ 0 1 2 . 8 9 C E . ]

This helps cache locality a lot, and also removes the space overhead of having pointers lying around.

Once lookup is optimized a lot, reading the lines starts to look quite slow. By the rules, we’re restricted to line-by-line reading. However, the lines() iterator being used is not allocation efficient. Simply moving to input.read_until(b'

', &mut line) (or read_line if you want a String ) makes a big difference.

The code spends a lot of time calling to_ascii_uppercase and then

fn pack_symbol ( c : u8 ) -> u8 { match c as char { 'A' => 0 , 'C' => 1 , 'G' => 2 , 'T' => 3 , _ => panic! ( "{}" , c as char ), } }

needed for the optimized hash of the string. The precise numbers don’t matter; all that matters is that each gets a unique 2-bit encoding. It would really help to optimize this.

The first act is to precompact the array – perform the match during reading and pack four symbols into each byte. This reduces memory costs and removes the need to pack repeatedly, but it still doesn’t make the first pack cheap.

The second act is a fun little bithack. See if you can spot it:

letter ASCII encoding A 1000001 a 1100001 C 1000011 c 1100011 G 1000111 g 1100111 T 1010100 t 1110100

The two bits one from the end form a unique, case-independent mapping. Our resulting code is just (byte >> 1) & 0b11 , or two assembly instructions.

A final little bit of performance advice is to make sure all threads do the same amount of work. Seven threads were spawned, but there are only four cores and some finished long before others. Combining workloads so only four threads run and finish in about the same time gave a shockingly sizable throughput boost.

Choice quote from the source:

// Yes, this is hilarious pub fn eat(&mut self, cereal: &[u8]) { ...

thread_ring

This one isn’t even in the list of comparable benchmarks, because it divides languages using native threads from languages that have some abstraction. Rust of course falls in the former category, and those tend to do worse on this benchmark, but at least we could give the other OS-thread-based programs a run for their money.

The basic idea here is to pass a token around a bunch of threads for a long time. And by a bunch of threads, I mean 503. That many real threads causes problems. Almost all of the time is spent pausing and resuming them.

The previous version passes around the token with channels, which is much improved by just using a semaphore waiting on a mutex – basically a channel specialized to a one element buffer. However, this still has poor behavior for the multithreaded case, since the system scheduler seems to get overloaded or confused and we end up wasting yet more of our time waiting for the correct thread to get loaded in when we want it to.

Spin locks on atomics help solve this problem. Exchanging values with atomics means we never have to pause a thread, nor wait for it to resume. An atomic exchange uses live threads and no scheduling. The only problem now is having 503 live threads at the same time, which we can solve by using the previous semaphores as a thread suspender and prefetcher. This seems to make it more obvious to the scheduler which threads should be running when, and gives it a little more headroom to do so too.

Here’s the actual main loop:

loop { lock.lock(); let input_value = input.lock(); output.unlock(input_value.saturating_sub(1)); unlock.unlock(); if input_value == 1 { println!("{}", thread_id); } if input_value <= 1 { return; } }

lock.lock() suspends the thread until the “prefetcher” – the thread two hops back – resumes it. let input_value = input.lock() waits for a value on the atomic spin lock. output.unlock(input_value.saturating_sub(1)) passes this to the next thread, which the thread prior will have already unlocked just in time. unlock.unlock(); is our time to be a prefetcher and unlock the thread next along.

Some have noted that this solution can exhibit severe slowdowns when scaling up the number of threads. However, our preliminary measurements show that it works quite well within the specific parameters of the Benchmarks Game.