Category Archives: Rust

How to speed up the Rust compiler some more in 2019

In July I wrote about my efforts to speed up the Rust compiler in 2019. I also described how the Rust compiler has gotten faster in 2019, with compile time reductions of 20-50% on most benchmarks. Now that Q3 is finished it’s a good time to see how things have changed since then.

Speed improvements in Q3 2019

The following image shows changes in time taken to compile many of the standard benchmarks used on the Rust performance tracker. It compares a revision of the the compiler from 2019-07-23 with a revision of the compiler from 2019-10-09.

Screenshot of Rust compiler benchmark improvements for Q3

These are the wall-time results. There are three different build kinds measured for each one: a debug build, an optimized build, and a check build (which detects errors but doesn’t generate code). For each build kind there is a mix of incremental and non-incremental runs done. The numbers for the individual runs aren’t shown here but you can see them if you view the results directly on the site and click around. (Note that the site has had some reliability issues lately. Apologies if you have difficulty with that link.) The “avg” column shows the average change for those runs. The “min” and “max” columns show the minimum and maximum changes among those same runs.

There are a few regressions, mostly notably for the ctfe-stress-2 benchmark, which is an artificial stress test of compile-time function evaluation and so isn’t too much of a concern. But there are many more improvements, including double-digit improvements for clap-rs, inflate, unicode_normalization, keccak, wg-grammar, serde, deep-vector, script-servo, and style-servo. There have been many interesting things going on.


For a long time, profilers like Cachegrind and Callgrind have shown that 2-6% of the instructions executed by the Rust compiler occur in calls to memcpy. This seems high! Curious about this, I modified DHAT to track calls to memcpy, much in the way it normally tracks calls to malloc.

The results showed that most of the memcpy calls come from a relatively small number of code locations. Also, all the memcpy calls involved values that exceed 128 bytes. It turns out that LLVM will use inline code for copies of values that are 128 bytes or smaller. (Inline code will generally be faster, but memcpy calls will be more compact above a certain copy size.)

I was able to eliminate some of these memcpy calls in the following PRs.

#64302: This PR shrank the ObligationCauseCode type from 56 bytes to 32 bytes by boxing two of its variants, speeding up many benchmarks by up to 2.6%. The benefit mostly came because the PredicateObligation type (which contains an ObligationCauseCode) shrank from 136 bytes to 112 bytes, which dropped it below the 128 byte memcpy threshold. I also tried reducing the size of ObligationCauseCode to 24 bytes by boxing two additional variants, but this had worse performance because more allocations were required.

#64374: The compiler’s parser has this type:

pub type PResult<'a, T> = Result<T, DiagnosticBuilder<'a>

It’s used as the return type for a lot of parsing functions. The T value is always small, but DiagnosticBuilder was 176 bytes, so PResult had a minimum size of  184 bytes. And DiagnosticBuilder is only needed when there is a parsing error, so this was egregiously inefficient. This PR boxed DiagnosticBuilder so that PResult has a minimum size of 16 bytes, speeding up a number of benchmarks by up to 2.6%.

#64394: This PR reduced the size of the SubregionOrigin type from 120 bytes to 32 bytes by boxing its largest variant, which sped up many benchmarks slightly (by less than 1%). If you are wondering why this type caused memcpy calls despite being less than 128 bytes, it’s because it is used in a BTreeMap and the tree nodes exceeded 128 bytes.


One of the biggest causes of memcpy calls is within a data structure called ObligationForest, which represents a bunch of constraints (relating to type checking and trait resolution, I think) that take the form of a collection of N-ary trees. ObligationForest uses a single vector to store the tree nodes, and links between nodes are represented as numeric indices into that vector.

Nodes are regularly removed from this vector by a function called ObligationForest::compress. This operation is challenging to implement efficiently because the vector can contain thousands of nodes and nodes are removed only a few at a time, and order must be preserved, so there is a lot of node shuffling that occurs. (The numeric indices of all remaining nodes must be updated appropriately afterwards, which further constrains things.) The shuffling requires lots of  swap calls, and each one of those does three memcpy calls (let tmp = a; a = b; b = tmp, more or less). And each node is 176 bytes! While trying to get rid of these memcpy calls, I got very deep into ObligationForest and made the following PRs that aren’t related to the copying.

#64420: This PR inlined a hot function, speeding up a few benchmarks by up to 2.8%. The function in question is indirectly recursive, and LLVM will normally refuse to inline such functions. But I was able to work around this by using a trick: creating two variants of the function, one marked with #[inline(always)] (for the hot call sites) and one marked with #[inline(never)] (for the cold call sites).

#64500: This PR did a bunch of code clean-ups, some of which helped performance to the tune of up to 1.7%. The improvements came from factoring out some repeated expressions, and using iterators and retain instead of while loops in some places.

#64545: This PR did various things, improving performance by up to 13.8%. The performance wins came from: combining a split parent/descendants representation to avoid frequent chaining of iterators (chained iterators are inherently slower than non-chained iterators); adding a variant of the shallow_resolve function specialized for the calling pattern at a hot call site; and using explicit iteration instead of Iterator::all. (More about that last one below.)

#64627: This PR also did various things, improving performance by up to 18.4%. The biggest improvements came from: changing some code that dealt with a vector to special-case the 0-element and 1-element cases, which dominated; and inlining an extremely hot function (using a variant of the abovementioned #[inline(always)]#[inline(never)] trick).

These PRs account for most of the improvements to the following benchmarks: inflatekeccak, cranelift-codegen, and serde. Parts of the ObligationForest code was so hot for these benchmarks (especially inflate and keccak) that it was worth micro-optimizing them to the nth degree. When I find hot code like this, there are always two approaches: (a) try to speed it up, or (b) try to avoid calling it. In this case I did (a), but I do wonder if the users of ObligationForest could be more efficient in how they use it.

The above PRs are a nice success story, but I should also mention that I tried a ton of other micro-optimizations that didn’t work.

  • I tried drain_filter in compress. It was slower.
  • I tried several invasive changes to the data representation, all of which ended up slowing things down.
  • I tried using swap_and_remove instead of swap in compress, This gave speed-ups, but changed the order that predicates are processed in, which changed the order and/or contents of error messages produced in lots of tests. I was unable to tell if these error message changes were legitimate — some were simple, but some were not — so I abandoned all approaches that altered predicate order.
  • I tried boxing ObligationForest nodes, to reduce the number of bytes copied in compress. It reduced the amount of copying, but was a net slowdown because it increased the number of allocations performed.
  • I tried inlining some other functions, for no benefit.
  • I used unsafe code to remove the swap calls, but the speed-up was only 1% in the best case and I wasn’t confident that my code was panic-safe, so I abandoned that effort.
  • There were even a number of seemingly innocuous code clean-ups that I had to abandon because they hurt performance measurably. I think this is because the code is so hot in some benchmarks that even tiny changes can affect code generation adversely. (I generally use instruction counts rather than wall time to make these evaluations, because instruction counts have very low variance.)

Amusingly enough, the memcpy calls in compress were what started all this, and despite the big wins, I didn’t manage to get rid of them!

Inlining and code bloat

I mentioned above that in #64545 I got an improvement by replacing a hot call to Iterator::all with explicit iteration. The reason I tried this was that I looked at the implementation of Iterator::all and saw that it was surprisingly complicated: it wrapped the given predicate in a closure that
returned a LoopState, passed that closure to try_for_each which
wrapped the first closure in a second closure, and passed that second closure
to try_fold which did the actual iteration using the second
closure. Phew!

Just for kicks I tried replacing this complex implementation with the obvious, simple implementation, and got a small speed-up on keccak, which I was using for most of my performance testing. So I did the same thing for three similar Iterator methods (any, find and find_map), submitted #64572, and did a CI perf run. The results were surprising and extraordinary: 1-5% reductions for many benchmarks, but double-digits for some, and 20-50% reductions for some clap-rs runs. Uh, what? Investigation showed that the reduction came from LLVM doing less work during code generation. These functions are all marked with #[inline] and so the simpler versions result in less code for LLVM to process. Sure enough, the big wins all came in debug and opt builds, with little effect on check builds.

This surprised me greatly. There’s been a long-running theory that the LLVM IR produced by the Rust compiler’s front end is low quality, that LLVM takes a long time to optimize it, and more front-end optimization could speed up LLVM’s code generation. #64572 demonstrates a related, but much simpler prospect: we can speed up compilation by making commonly inlined library functions smaller. In hindsight, it makes sense that this would have an effect, but the size of the effect is nonetheless astounding to me.

But there’s a trade-off. Sometimes a simpler, smaller function is slower. For the iterator methods there are some cases where that is true, so the library experts were unwilling to land #64572 as is. Fortunately, it was possible to obtain much of the potential compile time improvements without compromising runtime.

  • In #64600, scottmcm removed an aggressive specialization of try_fold  for slices that had an unrolled loop that called the given closure four times. This got about 60% of the improvements of #64572.
  • In #64885, andjo403 simplified the four Iterator methods to call try_fold directly, removing one closure layer. This got about another 15% of the improvements of #64572.

I had a related idea, which was to use simpler versions for debug builds and complex versions for opt builds. I tried three different ways of doing this.

  • Use if cfg!(debug_assertions) within the method bodies.
  • Have two versions of each method, one marked with #[cfg(debug_assertions)], the other marked with #[cfg(not(debug_assertions))].
  • Mark each method with #[cfg_attr(debug_assertions, inline)] so that the methods are inlined only in optimized builds.

None of these worked; they either had little effect or made things worse. I’m hazy on the details of how library functions get incorporated; maybe there’s another way to make this idea work.

In a similar vein, Alex Crichton opened #64846, which changes hashbrown (Rust’s hash table implementation) so it is less aggressive about inlining. This got some sizeable improvements on some benchmarks (up to 18% on opt builds of cargo) but also caused small regressions for a lot of other benchmarks. In this case, the balance between “slower hash tables” and “less code to compile” is delicate, and a final decision is yet to be made.

Overall, this is an exciting new area of investigation for improving Rust compile times. We definitely want new tooling to help identify which library functions are causing the most code bloat. Hopefully these functions can be tweaked so that compile times improve without hurting runtime performance much.


As well as all the above stuff, which all arose due to my investigations into memcpy calls, I had a few miscellaneous improvements that arose from normal profiling.

#65089: In #64673, simulacrum got up to 30% wins on the unicode_normalization benchmark by special-casing a type size computation that is extremely hot. (That benchmark is dominated by large match expressions containing many integral patterns.) Inspired by this, in this PR I made a few changes that moved the special case to a slightly earlier point that avoided even more unnecessary operations, for wins of up to 11% on that same benchmark.

#64949: The following pattern occurs in a few places in the compiler.

let v = self.iter().map(|p| p.fold_with(folder)).collect::<SmallVec<[_; 8]>>()

I.e. we map some values into a SmallVec. A few of these places are very hot, and in most cases the number of elements produced is 0, 1, or 2. This PR changed those hot locations to handle one or more of the 0/1/2 cases directly without using iteration and SmallVec::collect, speeding up numerous benchmarks by up to 7.8%.

#64801: This PR avoided a chained iterator in a hot location, speeding up the wg-grammar benchmark by up to 1.9%.

Finally, in #64112 I tried making pipelinined compilation more aggressive by moving crate metadata writing before type checking and borrow checking. Unfortunately, it wasn’t much of a win, and it would slightly delay error message emission when compiling code with errors, so I abandoned the effort.

Visualizing Rust compilation

Speeding up the Rust compiler isn’t the only way to make a Rust project build faster. Changing the crate structure of a project can also make a big difference. The good news here is that Eric Huss has implemented an amazing tool for visualizing Rust compilation, which can be used to identify inefficient crate structures in Rust projects.

The tool extremely easy to use. First, update to the latest Nightly:

rustup update nightly

Then just add -Ztimings to your build command, e.g.:

cargo +nightly build -Ztimings

At the end of the build it will print the name of an HTML file containing the data. Here’s part of the visualization for the Rust compiler itself:

Screenshot of -Ztimings output when compiling the Rust compiler

Full data is available here. (I recommend moving the “Scale” slider to 7 or 8 so that horizontal scrolling isn’t necessary.)

Two things leap out from this visualization.

  • The rustc crate takes about twice as long as any other crate to compile. It is the “long pole” of the build, and its presence serializes the build significantly. Breaking it up could improve compilation time quite a bit. I filed #65031 about this.
  • Pipelined compilation (released in Rust 1.38) is a huge win for the compiler itself. Pipelining allows a dependent crate to start building as soon as metadata is produced. In the visualization, this corresponds to point where the bar for a graph changes colour from light blue to purple. Imagine if the rustc crate had to finish before all the crates below it could even start! It used to take about 45 minutes for an optimized stage 2 build on my fast Linux desktop machine; thanks to pipelining it now takes about 26 minutes.

I also filed #65088 to add -Ztimings support to the Rust compiler’s own build system. (Enabling the visualization isn’t as simple for the compiler as it is for most Rust projects. The compiler’s build system is complicated by the fact that it’s a bootstrapping compiler that has to be built multiple times.)

We have already heard from multiple people that they used it fix inefficiencies in their crate structure, speeding up their builds significantly. Anyone who works on a sizeable Rust project should try out this tool.

The Rust compiler is still getting faster

A key theme of the Rust 2019 roadmap is maturity. This covers a variety of topics, but a crucial one is compile times. For example, the roadmap itself has the following as the first main theme for the compiler team.

Improving “core strength” by lowering raw compilation times and also generating better code (which in turn can help with compilation times)

The roadmap explainer post has a “polish” section that has the following as the first example.

Compile times and IDE support

I previously wrote about one period of improvement in Rust compiler speed. How are things going in 2019?

Speed improvements in 2019

The following image shows changes in time taken to compile the standard benchmarks used on the Rust performance tracker. It compares the compiler from 2019-01-01 with the compiler from 2019-07-24 (the most recent data at the time of writing).

Table showing Rust compiler speedups between 2019-01-01 and 2019-07-24

These are the wall-time results for 29 benchmarks. There are three different build kinds measured for each one: a debug build, an optimized build, and a check build (which detects errors but doesn’t generate code). For each build kind there is a mix of incremental and non-incremental runs done. The numbers for the individual runs aren’t shown here but you can see them if you view the results directly on the site and click around. The “avg” column shows the average change for those runs. The “min” and “max” columns show the minimum and maximum changes among those same runs.

The table has 261 numbers. The thing to take away is that 258 of them are negative, representing a decrease in compile time. Most of the “avg” values are in the range -20% to -40%. The “min” values (representing the best time reduction for each build kind) range from -12.4% to -51.3%. Even the “max” values (representing the worst time reduction for each build kind) are mostly better than -10%. These are pleasing results.

speed improvements since late 2017

What happens if we look further back? The image below compares the compiler from 2017-11-12 (the earliest date for which I could get data from the site) against the compiler from 2019-07-24, a period of just over 20 months.

Table showing Rust compiler speedups between 2017-11-12 and 2019-07-24

These are the wall-time results for only 18 benchmarks, because the benchmark suite was smaller in late 2017. Check builds were also not measured then. You can view the results directly on the site.

My initial thought from looking at the “avg” results was “the compiler is twice as fast” but closer inspection shows that’s not quite true; the average “avg” result is 42%. (I know that averaging averages is statistically dubious, I did it just to get a rough feel.) Overall, the results are significantly better than those for 2019: the “avg” values range from -19.9% to -61.3%, and the “min” values are mostly better than -60%.

(And don’t forget that time reduction percentages can be misleading when they get large. A 50% time reduction means the compiler is twice as fast; a 75% time reduction means the compiler is four times as fast; a 90% time reduction means the compiler is ten times as fast.)

All this is good news. The Rust compiler has long had a reputation for being slow. I still wouldn’t describe it as fast, but it is clearly a lot faster than it used to be. Many thanks to all those who made this happen, and I would be happy to hear from anyone who wants to help continue the trend!

Thanks to theZcuber for a Reddit post that was the starting point for this article.

How to speed up the Rust compiler in 2019

I have written previously about my efforts to speed up the Rust compiler in 2016 (part 1, part 2) and 2018 (part 1, part 2, NLL edition). It’s time for an update on the first half of 2019.

Faster globals

libsyntax has three tables in a global data structure, called Globals, storing information about spans (code locations), symbols, and hygiene data (which relates to macro expansion). Accessing these tables is moderately expensive, so I found various ways to improve things.

#59693: Every element in the AST has a span, which describes its position in the original source code. Each span consists of an offset, a length, and a third value that is related to macro expansion. The three fields are 12 bytes in total, which is a lot to attach to every AST element, and much of the time the three fields can fit in much less space. So the compiler used a 4 byte compressed form with a fallback to a hash table stored in Globals for spans that didn’t fit in 4 bytes. This PR changed that to 8 bytes. This increased memory usage and traffic slightly, but reduced the fallback rate from roughly 10-20% to less than 1%, speeding up many workloads, the best by an amazing 14%.

#61253: There are numerous operations that accessed the hygiene data, and often these were called in pairs or trios, thus repeating the hygiene data lookup. This PR introduced compound operations that avoid the repeated lookups. This won 10% on packed-simd, up to 3% on numerous other workloads.

#61484: Similar to #61253, this won up to 2% on many benchmarks.

#60630: The compiler has an interned string type, called symbol. It used this inconsistently. As a result, lots of comparisons were made between symbols and ordinary strings, which required a lookup of the string in the symbols table and then a char-by-char comparison. A symbol-to-symbol comparison is much cheaper, requiring just an integer comparison. This PR removed the symbol-to-string comparison operations, forcing more widespread use of the symbol type. (Fortunately, most of the introduced symbol uses involved statically-known, pre-interned strings, so there weren’t additional interning costs.) This won up to 1% on various benchmarks, and made the use of symbols more consistent.

#60815: Similar to #60630, this also won up to 1% on various benchmarks.

#60467, #60910, #61035, #60973: These PRs avoiding some more unnecessary symbol interning, for sub-1% wins.


The following improvements didn’t have any common theme.

#57719: This PR inlined a very hot function, for a 4% win on one workload.

#58210: This PR changed a hot assertion to run only in debug builds, for a 20%(!) win on one workload.

#58207: I mentioned string interning earlier. The Rust compiler also uses interning for a variety of other types where duplicate values are common, including a type called LazyConst. However, the intern_lazy_const function was buggy and didn’t actually do any interning — it just allocated a new LazyConst without first checking if it had been seen before! This PR fixed that problem, reducing peak memory usage and page faults by 59% on one benchmark.

#59507: The pretty-printer was calling write! for every space of
indentation, and on some workloads the indentation level can exceed 100. This PR reduced it to a single write! call in the vast majority of cases, for up to a
7% win on a few benchmarks.

#59626: This PR changed the preallocated size of one data structure to better match what was needed in practice, reducing peak memory usage by 20 MiB on some workloads.

#61612: This PR optimized a hot path within the parser, whereby constant tokens were uselessly subjected to repeated “is it a keyword?” tests, for up to a 7% win on programs with large constants.

Profiling improvements

The following changes involved improvements to our profiling tools.

#59899: I modified the output of -Zprint-type-sizes so that enum variants are listed from largest to smallest. This makes it much easier to see outsized variants, especially for enums with many variants.

#62110: I improved the output of the -Ztime-passes flag by removing some uninteresting entries that bloated the output and adding a measurement for the total compilation time.

Also, I improved the profiling support within the rustc-perf benchmark suite. First, I added support for profiling with OProfile. I admit I haven’t used it enough yet to gain any wins. It seg faults about half the time when I run it, which isn’t encouraging.

Second, I added support for profiling with the new version of DHAT. This blog post is about 2019, but it’s worth mentioning some improvements I made with the new DHAT’s help in Q4 of 2018, since I didn’t write a blog post about that period: #55167, #55346, #55383, #55384, #55501, #55525, #55556, #55574, #55604, #55777, #55558, #55745, #55778, #55905, #55906, #56268, #56090, #56269, #56336, #56369, #56737, and (ena crate) #14.

Finally, I wrote up brief descriptions for all the benchmarks in rustc-perf.

pipelined compilation

The improvements above (and all the improvements I’ve done before that) can be described as micro-optimizations, where I used profiling data to optimize a small piece of code.

But it’s also worth thinking about larger, systemic improvements to Rust compiler speed. In this vein, I worked in Q2 with Alex Crichton on pipelined compilation, a feature that increases the amount of parallelism available when building a multi-crate Rust project by overlapping the compilation of dependent crates. In diagram form, a compilation without pipelining looks like this:

         metadata            metadata
0s        5s       10s       15s       20s                30s

With pipelined compilation, it looks like this:

0s        5s       10s       15s                25s

I did the work on the Rust compiler side, and Alex did the work on the Cargo side.

For more details on how it works, how to use it, and lots of measurements, see this thread. The effects are highly dependent on a project’s crate structure and the compiling machine’s configuration. We have seen speed-ups as high as 1.84x, while some projects see no speed-up at all. At worst, it should make things only negligibly slower, because it’s not causing any additional work, just changing the order in which certain things happen.

Pipelined compilation is currently a Nightly-only feature. There is a tracking issue for stabilizing the feature here.

Future work

I have a list of things I want to investigate in Q3.

  • For pipelined compilation, I want to try pushing metadata creation even earlier in the compiler front-end, which may increase the speed-ups some more.
  • The compiler uses memcpy a lot; not directly, but the generated code uses it for value moves and possibly other reasons. In “check” builds that don’t do any code generation, typically 2-8% of all instructions executed occur within memcpy. I want to understand why this is and see if it can be improved. One possibility is moves of excessively large types within the compiler; another possibility is poor code generation. The former would be easier to fix. The latter would be harder to fix, but would benefit many Rust programs.
  • Incremental compilation sometimes isn’t very effective. On some workloads, if you make a tiny change and recompile incrementally it takes about the same time as a full non-incremental compilation. Perhaps a small change to the incremental implementation could result in some big wins.
  • I want to see if there are other hot paths within the parser that could be improved, like in #61612.

I also have various pieces of Firefox work that I need to do in Q3, so I might not get to all of these. If you are interested in working on these ideas, or anything else relating to Rust compiler speed, please get in touch.

How to get the size of Rust types with -Zprint-type-sizes

When optimizing Rust code it’s sometimes useful to know how big a type is, i.e. how many bytes it takes up in memory. std::mem::size_of can tell you, but often you want to know the exact layout as well. For example, an enum might be surprisingly big, in which case you probably will want to know if, for example, there is one variant that is much bigger than the others.

The -Zprint-type-sizes option does exactly this. Just pass it to a nightly version of rustc — it isn’t enabled on release versions, unfortunately — and it’ll print out details of the size, layout, and alignment of all types in use. For example, for this type:

enum E {
    C(u64, u8, u64, u8),

it prints the following, plus info about a few built-in types:

print-type-size type: `E`: 32 bytes, alignment: 8 bytes
print-type-size     discriminant: 1 bytes
print-type-size     variant `A`: 0 bytes
print-type-size     variant `B`: 7 bytes
print-type-size         padding: 3 bytes
print-type-size         field `.0`: 4 bytes, alignment: 4 bytes
print-type-size     variant `C`: 23 bytes
print-type-size         field `.1`: 1 bytes
print-type-size         field `.3`: 1 bytes
print-type-size         padding: 5 bytes
print-type-size         field `.0`: 8 bytes, alignment: 8 bytes
print-type-size         field `.2`: 8 bytes
print-type-size     variant `D`: 31 bytes
print-type-size         padding: 7 bytes
print-type-size         field `.0`: 24 bytes, alignment: 8 bytes

It shows:

  • the size and alignment of the type;
  • for enums, the size of the discriminant;
  • for enums, the size of each variant;
  • the size, alignment, and ordering of all fields (note that the compiler has reordered variant C‘s fields to minimize the size of E);
  • the size and location of all padding.

Every detail you could possibly want is there. Brilliant!

For rustc developers, there’s an extra-special trick for getting the size of a type within rustc itself. Put code like this into a file

extern crate syntax;
use syntax::ast::Expr;
fn main() {
    let _x = std::mem::size_of::<Expr>();

and then compile it like this:

RUSTC_BOOTSTRAP=1 rustc -Zprint-type-sizes

I won’t pretend to understand how it works, but the use of rustc_private and RUSTC_BOOTSTRAP somehow let you see inside rustc while using it, rather than while compiling it. I have used this trick for PRs such as this one.

How to speed up the Rust compiler in 2018: NLL edition

Niko Matsakis recently blogged about the Rust compiler’s new borrow checker, which implements non-lexical lifetimes (NLL). The new borrow checker is a really nice improvement to Rust, because it accepts many sound programs that the old borrow checker rejected.

In the blog post, Niko wrote briefly about the performance of the new borrow checker.

Finally, those of you who read the previous posts may remember that the performance of the NLL checker was a big stumbling block. I’m happy to report that the performance issues were largely addressed: there remains some slight overhead to using NLL, but it is largely not noticeable in practice, and I expect we’ll continue to improve it over time.

This paragraph is true, but glosses over a lot of details! This post will be about my contributions to this performance work.


Before I describe individual improvements, it’s worth mentioning that the new borrow checker uses bitsets (1D) and bit matrices (2D) heavily. A number of my wins involved these data structures.

#51869: This PR changed some code so that it overwrote an existing dense bitset rather than replacing it with a newly created one of the same size, reducing instruction counts for most benchmarks, the best by 1.5%.

#51870: This PR reused a structure containing two bitsets rather than recreating it afresh for every statement in a basic block, reducing instruction counts for numerous benchmarks, the best by 1%.

#52250: The compiler has a SparseBitMatrix type. Rows were added on demand, and each row was implemented as a sparse bitset using a BTreeMap. In practice, many of the rows were relatively dense, with 10–90% of the bits being set. This PR changed SparseBitMatrix to use a dense representation for rows, reducing instruction counts on one benchmark by 33% and by 1% on a few others. The PR had a mixed effect on memory usage, increasing the peak on some benchmarks and reducing it on others. (Never fear! #54318 below ended up fixing the regressions.)

#52342: This PR avoided a bunch of allocations in Canonicalizer methods, reducing instruction counts on numerous benchmarks, the best by 2%.

#53383: Further profiling showed that some dense bitsets were large, but had very few bits set within them, so the dense representation was wasteful. This PR implemented a new hybrid bitset type that uses a sparse representation for bitsets with up to 8 bits set and switches to a dense representation beyond that, and used it to replace dense bitsets in several places, reducing instruction counts on the five slowest benchmarks by 55%, 21%, 16%, 10% and 9%, and reducing peak memory usage of three benchmarks by 53%, 33%, and 9%.

#53513: This PR force-inlined a function at one hot callsite, reducing instruction counts on two benchmarks by 1%.

#53551: This PR avoided some clone calls, reducing instruction counts for one benchmark by 0.5%.

#53733: This PR added special handling for a very common and simple case in unroll_place, reducing the instruction counts on one benchmark by 25%.

#53942: A function called precompute_borrows_out_of_scope does a traversal of one or more basic blocks. In order to detect whether a basic block had been previously visited, it recorded the ID of every visited statement in a hash table. Some basic blocks can have many statements, resulting in many hash table lookups. This PR changed the code to record the ID of visited basic blocks in the hash table instead of visited statements — trickier than it sounds because the analysis can start in the middle of a basic block, in which case the first half might need to be eventually visited — reducing instruction counts on one benchmark by 60%.

#54211: Liveness analysis created an array that could get very large. Each element in the array was a struct containing two u32s and a bool. Each of those elements took up 12 bytes, but only 9 of the bytes held data. This PR split the array into three separate arrays, one per field, making the code slightly less readable but reducing peak memory usage on one benchmark by 20%. (Fun fact: those u32s used to be usizes, but I shrunk them back in May.)

#54213: This PR tweaked some code so that the lifetimes of two large data structures didn’t overlap, reducing peak memory usage by 27% on one benchmark and 8% on another. (Note: those data structures are dominated by, you guessed it, bitsets!)

#54318: This PR changed SparseBitMatrix so that each instantiated row used the hybrid bitset representation from #53383 instead of the dense representation, reducing peak memory usage by 14–45% on four benchmarks, and 96% (from 29.1GB to 1.2GB) on one external crate! This PR also fixed the peak memory regression that #52250 introduced for a few benchmarks.

#54420: I subsequently realized that #54211 didn’t go far enough. Some debugging println! statements showed that both of the u32s in each liveness entry almost always held a special value that meant “invalid”. When data is repetitive, compression is possible: I could use a packed representation where the common (INVALID, INVALID, true) and (INVALID, INVALID, false) cases were represented by special u32 values, and all other triples were represented by a u32 index into an auxiliary table. This PR changed the representation as described, reducing instruction counts on numerous benchmarks, the best by 16%, and reducing peak memory usage on numerous benchmarks, the best by 38%. (I also tried a more compact representation where each element was a single byte; it reduced peak memory usage some more by the instruction count reduction was less, so I went with the earlier approach.)

Progress and current status

You probably noticed that some of the improvements in the previous section were large, and I wasn’t the only one working on NLL performance; Niko Matsakis and David Wood also contributed some big wins. This is because the new borrow checker’s performance was originally, to be honest, terrible. This is understandable; the focus had been on functionality and correctness, which is fair enough for a large and complex new component. Nonetheless, in June I was very nervous about its performance.

To be more specific, “check” builds (which don’t generate code) ran as much as 50x slower with the new borrow checker on some benchmarks. And multiple benchmarks were disabled on CI because they were simply too slow or used too much memory.

Issue #52028 tells a representative story. It was originally filed because the html5ever benchmark was triggering out-of-memory failures on CI. Measurements with Massif and DHAT showed that its peak heap memory usage was over 14 GB, largely caused by a single 12 GB allocation! In comparison, the peak with the old borrow checker was roughly 200–300 MB. If you read through that issue, you can see that over a period of 2.5 months we reduced the memory usage from 14 GB, to 10 GB, to 2 GB, to 1.2 GB, to 600 MB, to 501 MB, and finally to 266 MB.

And things are pretty good now. The instruction counts on “check” builds for all benchmarks are at most 18% higher with the new borrow checker than the old borrow checker, and are typically around 5%. (And note that “check” builds are the worst-case scenario; non-“check” builds will see a smaller relative slowdown because of the extra time needed for code generation, which is unaffected by the borrow checker.) Memory usage is similar: all benchmarks except one have peak memory usage that is at most 20% higher, with the typical value around 3%. (The one remaining exceptional benchmark uses 2.7x memory.) The worse numbers generally occur on programs containing very large constants.

I’m not entirely happy even with this level of performance regression, but for now I have run out of ideas for improving it further. The new borrow checker is a lot more sophisticated and tracks a lot more data, so strict performance parity is a tough ask. Nonetheless, given how bad performance was a few months ago, I’m happy that we’ve got it down to a level where most people probably won’t notice any difference. Given that the new borrow checker makes Rust a significantly nicer and easier language to use, I hope it’s an acceptable trade-off.

San Francisco Oxidation meeting notes

At last week’s Mozilla All Hands meeting in San Francisco we had an Oxidation meeting about the use of Rust in Firefox. It was low-key, being mostly about status and progress. The notes are here for those who are interested.

How to speed up the Rust compiler some more in 2018

I recently wrote about some work I’ve done to speed up the Rust compiler. Since then I’ve done some more.

rustc-perf improvements

Since my last post, rustc-perf — the benchmark suite, harness and visualizer — has seen some improvements. First, some new benchmarks were added: cargo, ripgrep, sentry-cli, and webrender. Also, the parser benchmark has been removed because it was a toy program and thus not a good benchmark.

Second, I added support for several new profilers: Callgrind, Massif, rustc’s own -Ztime-passes, and the use of ad hoc eprintln! statements added to rustc. (This latter case is more useful than it might sound; in combination with post-processing it can be very helpful, as we will see below.)

Finally, the graphs shown on the website now have better y-axis scaling, which makes many of them easier to read. Also, there is a new dashboard view that shows performance across rustc releases.

Failures and incompletes

After my last post, multiple people said they would be interested to hear about optimization attempts of mine that failed. So here is an incomplete selection. I suggest that rustc experts read through these, because there is a chance they will be able to identify alternative approaches that I have overlooked.

nearest_common_ancestors 1: I managed to speed up this hot function in a couple of ways, but a third attempt failed. The representation of the scope tree is not done via a typical tree data structure; instead there is a HashMap of child/parent pairs. This means that moving from a child to its parent node requires a HashMap lookup, which is expensive. I spent some time designing and implementing an alternative data structure that stored nodes in a vector and the child-to-parent links were represented as indices to other elements in the vector. This meant that child-to-parent moves only required stepping through the vector. It worked, but the speed-up turned out to be very small, and the new code was significantly more complicated, so I abandoned it.

nearest_common_ancestors 2: A different part of the same function involves storing seen nodes in a vector. Searching this unsorted vector is O(n), so I tried instead keeping it in sorted order and using binary search, which gives O(log n) search. However, this change meant that node insertion changed from amortized O(1) to O(n) — instead of a simple push onto the end of the vector, insertion could be at any point, which which required shifting all subsequent elements along. Overall this change made things slightly worse.

PredicateObligation SmallVec: There is a type Vec<PredicationObligation> that is instantiated frequently, and the vectors often have few elements. I tried using a SmallVec instead, which avoids the heap allocations when the number of elements is below a threshold. (A trick I’ve used multiple times.) But this made things significantly slower! It turns out that these Vecs are copied around quite a bit, and a SmallVec is larger than a Vec because the elements are inline. Furthermore PredicationObligation is a large type, over 100 bytes. So what happened was that memcpy calls were inserted to copy these SmallVecs around. The slowdown from the extra function calls and memory traffic easily outweighed the speedup from avoiding the Vec heap allocations.

SipHasher128: Incremental compilation does a lot of hashing of data structures in order to determine what has changed from previous compilation runs. As a result, the hash function used for this is extremely hot. I tried various things to speed up the hash function, including LEB128-encoding of usize inputs (a trick that worked in the past) but I failed to speed it up.

LEB128 encoding: Speaking of LEB128 encoding, it is used a lot when writing metadata to file. I tried optimizing the LEB128 functions by special-casing the common case where the value is less than 128 and so can be encoded in a single byte. It worked, but gave a negligible improvement, so I decided it wasn’t worth the extra complication.

Token shrinking: A previous PR shrunk the Token type from 32 to 24 bytes, giving a small win. I tried also replacing the Option<ast::Name> in Literal with just ast::Name and using an empty name to represent “no name”. That  change reduced it to 16 bytes, but produced a negligible speed-up and made the code uglier, so I abandoned it.

#50549: rustc’s string interner was structured in such a way that each interned string was duplicated twice. This PR changed it to use a single Rc‘d allocation, speeding up numerous benchmark runs, the best by 4%. But just after I posted the PR, @Zoxc posted #50607, which allocated the strings out of an arena, as an alternative. This gave better speed-ups and was landed instead of my PR.

#50491: This PR introduced additional uses of LazyBTreeMap (a type I had previously introduced to reduce allocations) speeding up runs of multiple benchmarks, the best by 3%. But at around the same time, @porglezomp completed a PR that changed BTreeMap to have the same lazy-allocation behaviour as LazyBTreeMap, rendering my PR moot. I subsequently removed the LazyBTreeMap type because it was no longer necessary.

#51281: This PR, by @Mark-Simulacrum, removed an unnecessary heap allocation from the RcSlice type. I had looked at this code because DHAT’s output showed it was hot in some cases, but I erroneously concluded that the extra heap allocation was unavoidable, and moved on! I should have asked about it on IRC.


#50339: rustc’s pretty-printer has a buffer that can contain up to 55 entries, and each entry is 48 bytes on 64-bit platforms. (The 55 somehow comes from the algorithm being used; I won’t pretend to understand how or why.) Cachegrind’s output showed that the pretty printer is invoked frequently (when writing metadata?) and that the zero-initialization of this buffer was expensive. I inserted some eprintln! statements and found that in the vast majority of cases only the first element of the buffer was ever touched. So this PR changed the buffer to default to length 1 and extend when necessary, speeding up runs for numerous benchmarks, the best by 3%.

#50365: I had previously optimized the nearest_common_ancestor function. Github user @kirillkh kindly suggested a tweak to the code from that PR which reduced the number comparisons required. This PR implemented that tweak, speeding up runs of a couple of benchmarks by another 1–2%.

#50391: When compiling certain annotations, rustc needs to convert strings from unescaped form to escaped form. It was using the escape_unicode function to do this, which unnecessarily converts every char to \u{1234} form, bloating the resulting strings greatly. This PR changed the code to use the escape_default function — which only escapes chars that genuinely need escaping — speeding up runs of most benchmarks, the best by 13%. It also slightly reduced the on-disk size of produced rlibs, in the best case by 15%.

#50525: Cachegrind showed that even after the previous PR, the above string code was still hot, because string interning was happening on the resulting string, which was unnecessary in the common case where escaping didn’t change the string. This PR added a scan to determine if escaping is necessary, thus avoiding the re-interning in the common case, speeding up a few benchmark runs, the best by 3%.

#50407: Cachegrind’s output showed that the trivial methods for the simple BytePos and CharPos types in the parser are (a) extremely hot and (b) not being inlined. This PR annotated them so they are inlined, speeding up most benchmarks, the best by 5%.

#50564: This PR did the same thing for the methods of the Span type, speeding up incremental runs of a few benchmarks, the best by 3%.

#50931: This PR did the same thing for the try_get function, speeding up runs of many benchmarks, the best by 1%.

#50418: DHAT’s output showed that there were many heap allocations of the cmt type, which is refcounted. Some code inspection and ad hoc instrumentation with eprintln! showed that many of these allocated cmt instances were very short-lived. However, some of them ended up in longer-lived chains, in which the refcounting was necessary. This PR changed the code so that cmt instances were mostly created on the stack by default, and then promoted to the heap only when necessary, speeding up runs of three benchmarks by 1–2%. This PR was a reasonably large change that took some time, largely because it took me five(!) attempts (the final git branch was initially called cmt5) to find the right dividing line between where to use stack allocation and where to use refcounted heap allocation.

#50565: DHAT’s output showed that the dep_graph structure, which is a IndexVec<DepNodeIndex,Vec<DepNodeIndex>>, caused many allocations, and some eprintln! instrumentation showed that the inner Vec‘s were mostly only a few elements. This PR changed the Vec<DepNodeIndex> to SmallVec<[DepNodeIndex;8]>, which avoids heap allocations when the number of elements is less than 8, speeding up incremental runs of many benchmarks, the best by 2%.

#50566: Cachegrind’s output shows that the hottest part of rustc’s lexer is the bump function, which is responsible for advancing the lexer onto the next input character. This PR streamlined it slightly, speeding up most runs of a couple of benchmarks by 1–3%.

#50818: Both Cachegrind and DHAT’s output showed that the opt_normalize_projection_type function was hot and did a lot of heap allocations. Some eprintln! instrumentation showed that there was a hot path involving this function that could be explicitly extracted that would avoid unnecessary HashMap lookups and the creation of short-lived Vecs. This PR did just that, speeding up most runs of serde and futures by 2–4%.

#50855: DHAT’s output showed that the macro parser performed a lot of heap allocations, particular on the html5ever benchmark. This PR implemented ways to avoid three of them: (a) by storing a slice of a Vec in a struct instead of a clone of the Vec; (b) by introducing a “ref or box” type that allowed stack allocation of the MatcherPos type in the common case, but heap allocation when necessary; and (c) by using Cow to avoid cloning a PathBuf that is rarely modified. These changes sped up runs of html5ever by up to 10%, and by up to 3%. I was particularly pleased with these changes because they all involved non-trivial changes to memory management that required the introduction of additional explicit lifetimes. I’m starting to get the hang of that stuff… explicit lifetimes no longer scare me the way they used to. It certainly helps that rustc’s error messages do an excellent job of explaining where explicit lifetimes need to be added.

#50932: DHAT’s output showed that a lot of HashSet instances were being created in order to de-duplicate the contents of a commonly used vector type. Some eprintln! instrumentation showed that most of these vectors only had 1 or 2 elements, in which case the de-duplication can be done trivially without involving a HashSet. (Note that the order of elements within this vector is important, so de-duplication via sorting wasn’t an option.) This PR added special handling of these trivial cases, speeding up runs of a few benchmarks, the best by 2%.

#50981: The compiler does a liveness analysis that involves vectors of indices that represent variables and program points. In rare cases these vectors can be enormous; compilation of the inflate benchmark involved one that has almost 6 million 24-byte elements, resulting in 143MB of data. This PR changed the type used for the indices from usize to u32, which is still more than large enough, speeding up “clean incremental” builds of inflate by about 10% on 64-bit platforms, as well as reducing their peak memory usage by 71MB.

What’s next?

These improvements, along with those recently done by others, have significantly sped up the compiler over the past month or so: many workloads are 10–30% faster, and some even more than that. I have also seen some anecdotal reports from users about the improvements over recent versions, and I would be interested to hear more data points, especially those involving rustc nightly.

The profiles produced by Cachegrind, Callgrind, and DHAT are now all looking very “flat”, i.e. with very little in the way of hot functions that stick out as obvious optimization candidates. (The main exceptions are the SipHasher128 functions I mentioned above, which I haven’t been able to improve.) As a result, it has become difficult for me to make further improvements via “bottom-up” profiling and optimization, i.e. optimizing hot leaf and near-leaf functions in the call graph.

Therefore, future improvement will likely come from “top-down” profiling and optimization, i.e. observations such as “rustc spends 20% of its time in phase X, how can that be improved”? The obvious place to start is the part of compilation taken up by LLVM. In many debug and opt builds LLVM accounts for up to 70–80% of instructions executed. It doesn’t necessarily account for that much time, because the LLVM parts of execution are parallelized more than the rustc parts, but it still is the obvious place to focus next. I have looked at a small amount of generated MIR and LLVM IR, but there’s a lot to take in. Making progress will likely require a lot broader understanding of things than many of the optimizations described above, most of which require only a small amount of local knowledge about a particular part of the compiler’s code.

If anybody reading this is interested in trying to help speed up rustc, I’m happy to answer questions and provide assistance as much as I can. The #rustc IRC channel is also a good place to ask for help.

The Rust compiler is getting faster

TL;DR: The Rust compiler has gotten 1.06x–4x faster over the past month.

As changes are made to the Rust compiler, a suite of benchmarks measuring compile time is run regularly on the development version. The data is viewable at The default view is graphical, showing data from the past month.

Screenshot of showing measurements of the html5ever benchmark

The screenshot above shows the graphs for a single benchmark called “html5ever”, which consists of an old version of the project of the same name. Each one shows measurements for a different kind of build: a debug build, a “check” build (which detect errors but don’t generate code), and an optimized build. Within each graph there are the following three data series.

  • Clean: a normal build.
  • Baseline incremental: an incremental build with no prior incremental runs. Such a build is a little slower than a normal build, because it does normal compilation and also gathers information to guide subsequent incremental builds.
  • Clean incremental: an incremental build run immediately after a baseline incremental build. This is the best-case scenario for incremental compilation in which the minimal amount of work is done.

If you visit the site yourself you’ll see that most of the benchmarks have more than three data series, including ones for incremental builds done after small code changes (a more realistic use case), and one for builds with non-lexical lifetimes enabled.

The x-axis shows time and the y-axis shows instruction counts. Other units of measurements are available, including cycles, time, and memory usage. Instruction counts are shown as the default; this isn’t ideal because it’s only a proxy for the measurement that really matters (time)… but it’s a pretty good proxy, and it has a lot lower variation than the time measurements, which is important when detecting changes.

This graphical view is particularly useful for detecting major changes. For example, you can see that in early May there was a major regression for “clean” and “baseline incremental” builds, which Alex Crichton fixed a few days later.

As well as the graphical view, the site also provides a textual “compare” view, which can be reached via the link at the top left of each page. This view compares measurements from two revisions of the compiler; by default it compares the most recently measured revision with one from a month ago. (It can also be used locally, which is very useful to evaluate changes that speed up the compiler.)

The screenshot above is of the “compare” view at the time of writing. Each line corresponds to a single graph from the graphical view. (If you visit the site and click on an individual entry it will expand and show all of the measurements. The resemblance between those measurements and this screenshot will of course diminish over time.) The “avg” column shows the average change across all the data series. The “min” and “max” columns show the minimum and maximum changes for any of the data series. The “serde” and “script-servo” lines are empty because those benchmarks were added to the suite less than a month ago, so no comparison can be made.

The table has many numbers, but the thing to take away is that they are almost all significantly negative, meaning that compile time has reduced. The “avg” numbers range from 6% to 38%; the “min” numbers (i.e. best result) go as high as 75%; the “max” numbers (i.e. worst result) go as high as 36%.

In conclusion: the Rust compiler has gotten significantly faster in the past month. Across a wide range of programs, and a wide range of build configurations, compile times have reduced by between 6% and 75%. To put it another way, the compiler has gotten between 1.06x and 4x faster.

These benefits are available right now to users of the Nightly channel. Users of the Release channel will see them more gradually, spread across one or two versions released over the next few months.

How to speed up the Rust compiler in 2018

18 months ago I wrote about some work I did to speed up the Rust compiler (rustc). I’ve recently taken this work up again. Also, in the meantime rustc’s build system has been replaced and its benchmark suite has been overhauled. So it’s a good time for an update.

Getting the code

The steps for getting the rustc code haven’t changed. First, I fork the main Rust repository on GitHub. Then I make two local clones: a base clone that I won’t modify, which serves as a stable comparison point (rust0), and a second clone where I make my modifications (rust1). I use commands something like this:

for r in rust0 rust1 ; do
  git clone$user/rust $r
  cd $r
  git remote add upstream
  git remote set-url origin$user/rust

Building the Rust compiler

The compiler’s build system is complex with many possible invocations and configurations. I’ll cover the absolute minimum information required to understand how I’ve been using it.

First, you need a config.toml file, which sits at the root of the repository and dictates the compiler’s configuration. I used the provided config.toml.example as a starting point, cut it down a lot, and ended up with the following.

optimize = true
release-debuginfo = true
assertions = false

optimize = true
codegen-units = 1
debug-assertions = false
debuginfo = true
debuginfo-lines = true
jemalloc = false     # EDIT: this used to be `use-jemalloc = false`

It’s possible that some of these lines just restate defaults, but I figure it doesn’t hurt to be explicit. This configuration has the following characteristics.

  • It results in the production of a fully optimized rustc, which is important for profiling. The exception to this is that I disable jemalloc, because DHAT doesn’t work when jemalloc is enabled.
  • It has maximal debug info present, which ensures that profiles are as detailed as possible.

Building rustc can be confusing, particularly because of the multiple compiler stages and the terminology around them. Here is the command I use.

./ build --stage 2 src/rustc

This produces a stage 2 compiler that can handle procedural macros and so is suitable for profiling the benchmark suite.

The benchmark suite

rustc’s benchmark suite is called rustc-perf. It consists of two parts:

  • collector: The 24 benchmark programs, 14 of which are “real” code (i.e. crates used in real applications), and 10 of which are toy programs or stress test microbenchmarks. Also, the harness code that runs and measures them.
  • site: Code for displaying measurements as a website.

The test harness is very thorough. For each benchmark, it measures Debug, Opt, and Check (no code generation) invocations. Furthermore, within each of those categories, it does five or more runs, including a normal build and various kinds of incremental builds. A full benchmarking run measures over 400 invocations of rustc. Note however that only the compilation of the final crate is measured in each benchmark; compilation of dependent crates is not included.

rustc-perf was created primarily for the site, which tracks rustc’s performance over time. I recently modified it so it could be used to compare two builds of rustc on a local machine, which is a fundamental operation when optimizing. This can be done by running the suite and site locally and navigating to the local “compare” page, which looks like this:

Screenshot of rustc-perf's compare page

Note that rustc-perf uses perf-stat for its measurements, so the benchmarking functionality currently only works on Linux.

I also extended rustc-perf so the benchmarks can be run under a profiler. I implemented support for perf-record, Cachegrind, and DHAT, because they are the profilers that I am most familiar with; it isn’t hard to add support for other profilers (including non-Linux ones). The advantage of integrating support for a profiler intto rustc-perf is that it gets the profiler invocations underneath the Cargo invocations, ensuring that the right rustc invocations are measured.


Here are the improvements I’ve made to rustc over the past few weeks.

#49993: Cachegrind’s output showed that derived methods for the Token type were taking up significant time. This PR changed the ‘#’ counts in raw Lit variants from usize to u16, which reduced the size of Token from 32 to 24 bytes, speeding up some of the runs for coercions and html5ever by 1%.

#50051: Rust’s Option::ok_or function transforms an Option<T> value into a Result<T, E>. It’s a bit of a footgun… as the docs say:

Arguments passed to ok_or are eagerly evaluated; if you are passing the result of a function call, it is recommended to use ok_or_else, which is lazily evaluated.

DHAT’s output showed that one hot allocation site involved the creation of a String while getting the value of the MIRI_BACKTRACE environment variable. This seemed to me like a strange thing to be happening frequently, and it turns out that it was at the end of a chain of calls that were only needed in the (rare) error case of an ok_or call. This PR changed the code to use a trivial closure with ok_or_else, speeding up runs for a lot of benchmarks, the best by 6%.

#50052: DHAT’s output showed that the char_lit function, which parses “\u{…}” literals, was doing a lot of allocations while stripping out ‘_’ chars. This PR avoided that allocation, speeding up various runs — particularly ones for regex, futures, clap, coercions, hyper, and encoding — the best by 6%.

#50106: Cachegrind’s output showed that the nearest_common_ancestor function, which computes the lowest common ancestor of two nodes in a scope tree, was very hot. The algorithm in use constructed the full scope chain for each node, and then worked backward from the end of the two scope chains until a difference was found. This is a reasonable algorithm in many circumstances, but some ad hoc instrumentation (eprintln! statements plus some simple post-processing) showed that the scope chains usually only differ by a handful of elements at the front and then have very long common tails, with dozens or even hundreds of elements. This PR switched to a different algorithm that looks for differences from the front of the scope chain, speeding up runs for many benchmarks, the best by 8%.

#50174: By default, Rust’s HashSet and HashMap use a hash function that is very high quality but also very slow. Therefore, the Rust compiler internally uses different types, FxHashSet and FxHashMap, which are identical to the standard ones except they use a much faster hash function. Unfortunately, it’s easy to forget about them and use the standard hash tables instead. Cachegrind’s output showed that the default hash function code (SipHash) was executed a lot, and that one particularly hot hash table (the symbol interner) was using HashMap. This PR (trivially) changed that table to an FxHashMap, speeding up runs for numerous benchmarks, the best by 5%.

#50240: Some of Rust’s standard containers, such as Vec, HashSet, and HashMap, have the nice property that by default they don’t allocate until an element is inserted. This is good because it’s surprising how often such containers are created but never inserted into. DHAT’s output showed that such behaviour would also help with a couple of the compiler’s uses of BTreeMap. I tried and failed to implement this behaviour directly in BTreeMap; according to Gankro, “BTreeMap is some of the most complex unsafe code in libstd” and “I just scared off a grizzled firefox dev explaining it“! Instead this PR introduced a thin wrapper type (LazyBTreeMap) around BTreeMap and used it in the handful of relevant places within the compiler, speeding up the runs for several benchmarks, the best by 3%. #50266 is open to do the general fix for BTreeMap, whereupon LazyBTreeMap will be able to be removed.

#50246: Cachegrind’s output showed that a function named dump_allocs was hot for some benchmarks. This sounded to me like a logging or debugging function of some kind, and investigation confirmed that it was traversing data structures in order to build up strings that went unused in the standard case where logging is disabled. This PR (trivially) changed this function and a couple of related ones to be no-ops if logging is disabled, speeding up runs for coercions, tuple-stress, html5ever, and encoding, the best by almost 15%! This shows how not doing unclever things is often as important as doing clever things when it comes to optimizing software.

Update: It’s worth noting that I also made three or four optimization attempts that didn’t work out — where I made a change that seemed like it should help, based on profiling data, but the effect was negligible. Success isn’t guaranteed!

Future work

All of the PRs mentioned above (except for the aborted BTreeMap change) involved small, simple changes to the Rust compiler’s code. I’m not a rustc expert, but I do know how to use a couple of profilers well, and I’ve been able to make a difference. I’m sure there are more improvements of this nature to be made, and I encourage other people to try profiling rustc with their favourite profilers to see what they can find. This is valuable because rustc’s speed is something that Rust users often complain about. And it’s fun, if you like that sort of thing 🙂  I’m happy to help people, and the members of the #rustc IRC channel are very friendly and helpful.

Having said that, in a lot of cases, especially for opt builds, the majority of execution time is within LLVM, which rustc uses for code generation. Speeding up LLVM itself may be difficult, but I hope/suspect there is room for improvement in the way that rustc interacts with LLVM. If anyone has ideas on that front I’d love to hear about them.