Parallel compilation - Rust Compiler Development Guide (original) (raw)
Rust Compiler Development Guide
Parallel compilation
As of November 2024, most of the rust compiler is now parallelized.
- The codegen part is executed concurrently by default. You can use the
-C codegen-units=noption to control the number of concurrent tasks. - The parts after HIR lowering to codegen such as type checking, borrowing checking, and mir optimization are parallelized in the nightly version. Currently, they are executed in serial by default, and parallelization is manually enabled by the user using the
-Z threads = noption. - Other parts, such as lexical parsing, HIR lowering, and macro expansion, are still executed in serial mode.
The following sections are kept for now but are quite outdated.
Code generation
During monomorphization the compiler splits up all the code to be generated into smaller chunks called codegen units. These are then generated by independent instances of LLVM running in parallel. At the end, the linker is run to combine all the codegen units together into one binary. This process occurs in the rustc_codegen_ssa::base module.
Data structures
The underlying thread-safe data-structures used in the parallel compiler can be found in the rustc_data_structures::sync module. These data structures are implemented differently depending on whether parallel-compiler is true.
| data structure | parallel | non-parallel |
|---|---|---|
| Lock | (parking_lot::Mutex) | (std::cell::RefCell) |
| RwLock | (parking_lot::RwLock) | (std::cell::RefCell) |
| MTLock | (Lock) | (T) |
| ReadGuard | parking_lot::RwLockReadGuard | std::cell::Ref |
| MappedReadGuard | parking_lot::MappedRwLockReadGuard | std::cell::Ref |
| WriteGuard | parking_lot::RwLockWriteGuard | std::cell::RefMut |
| MappedWriteGuard | parking_lot::MappedRwLockWriteGuard | std::cell::RefMut |
| LockGuard | parking_lot::MutexGuard | std::cell::RefMut |
- These thread-safe data structures are interspersed during compilation which can cause lock contention resulting in degraded performance as the number of threads increases beyond 4. So we audit the use of these data structures which leads to either a refactoring so as to reduce the use of shared state, or the authoring of persistent documentation covering the specific of the invariants, the atomicity, and the lock orderings.
- On the other hand, we still need to figure out what other invariants during compilation might not hold in parallel compilation.
WorkerLocal
WorkerLocal is a special data structure implemented for parallel compilers. It holds worker-locals values for each thread in a thread pool. You can only access the worker local value through the Deref impl on the thread pool it was constructed on. It panics otherwise.
WorkerLocal is used to implement the Arena allocator in the parallel environment, which is critical in parallel queries. Its implementation is located in the rustc_data_structures::sync::worker_local module. However, in the non-parallel compiler, it is implemented as (OneThread<T>), whose Tcan be accessed directly through Deref::deref.
Parallel iterator
The parallel iterators provided by the rayon crate are easy ways to implement parallelism. In the current implementation of the parallel compiler we use a custom fork of rayon to run tasks in parallel.
Some iterator functions are implemented to run loops in parallel when parallel-compiler is true.
| Function(Omit Send and Sync) | Introduction | Owning Module |
|---|---|---|
| par_iter<T: IntoParallelIterator>(t: T) -> T::Iter | generate a parallel iterator | rustc_data_structure::sync |
| par_for_each_in<T: IntoParallelIterator>(t: T, for_each: impl Fn(T::Item)) | generate a parallel iterator and run for_each on each element | rustc_data_structure::sync |
| Map::par_body_owners(self, f: impl Fn(LocalDefId)) | run f on all hir owners in the crate | rustc_middle::hir::map |
| Map::par_for_each_module(self, f: impl Fn(LocalDefId)) | run f on all modules and sub modules in the crate | rustc_middle::hir::map |
| ModuleItems::par_items(&self, f: impl Fn(ItemId)) | run f on all items in the module | rustc_middle::hir |
| ModuleItems::par_trait_items(&self, f: impl Fn(TraitItemId)) | run f on all trait items in the module | rustc_middle::hir |
| ModuleItems::par_impl_items(&self, f: impl Fn(ImplItemId)) | run f on all impl items in the module | rustc_middle::hir |
| ModuleItems::par_foreign_items(&self, f: impl Fn(ForeignItemId)) | run f on all foreign items in the module | rustc_middle::hir |
There are a lot of loops in the compiler which can possibly be parallelized using these functions. As of August 2022, scenarios where the parallel iterator function has been used are as follows:
| caller | scenario | callee |
|---|---|---|
| rustc_metadata::rmeta::encoder::prefetch_mir | Prefetch queries which will be needed later by metadata encoding | par_iter |
| rustc_monomorphize::collector::collect_crate_mono_items | Collect monomorphized items reachable from non-generic items | par_for_each_in |
| rustc_interface::passes::analysis | Check the validity of the match statements | Map::par_body_owners |
| rustc_interface::passes::analysis | MIR borrow check | Map::par_body_owners |
| rustc_typeck::check::typeck_item_bodies | Type check | Map::par_body_owners |
| rustc_interface::passes::hir_id_validator::check_crate | Check the validity of hir | Map::par_for_each_module |
| rustc_interface::passes::analysis | Check the validity of loops body, attributes, naked functions, unstable abi, const bodys | Map::par_for_each_module |
| rustc_interface::passes::analysis | Liveness and intrinsic checking of MIR | Map::par_for_each_module |
| rustc_interface::passes::analysis | Deathness checking | Map::par_for_each_module |
| rustc_interface::passes::analysis | Privacy checking | Map::par_for_each_module |
| rustc_lint::late::check_crate | Run per-module lints | Map::par_for_each_module |
| rustc_typeck::check_crate | Well-formedness checking | Map::par_for_each_module |
There are still many loops that have the potential to use parallel iterators.
Query system
The query model has some properties that make it actually feasible to evaluate multiple queries in parallel without too much effort:
- All data a query provider can access is via the query context, so the query context can take care of synchronizing access.
- Query results are required to be immutable so they can safely be used by different threads concurrently.
When a query foo is evaluated, the cache table for foo is locked.
- If there already is a result, we can clone it, release the lock and we are done.
- If there is no cache entry and no other active query invocation computing the same result, we mark the key as being "in progress", release the lock and start evaluating.
- If there is another query invocation for the same key in progress, we release the lock, and just block the thread until the other invocation has computed the result we are waiting for. Cycle error detection in the parallel compiler requires more complex logic than in single-threaded mode. When worker threads in parallel queries stop making progress due to interdependence, the compiler uses an extra thread (named deadlock handler) to detect, remove and report the cycle error.
The parallel query feature still has implementation to do, most of which is related to the previous Data Structures and Parallel Iterators. See this open feature tracking issue.
Rustdoc
As of November 2022, there are still a number of steps to complete before rustdoc rendering can be made parallel (see a open discussion of parallel rustdoc).
Resources
Here are some resources that can be used to learn more: