39a52ceb4f
During instance initialization, we build two sorts of arrays eagerly:
- We create an "anyfunc" (a `VMCallerCheckedAnyfunc`) for every function
in an instance.
- We initialize every element of a funcref table with an initializer to
a pointer to one of these anyfuncs.
Most instances will not touch (via call_indirect or table.get) all
funcref table elements. And most anyfuncs will never be referenced,
because most functions are never placed in tables or used with
`ref.func`. Thus, both of these initialization tasks are quite wasteful.
Profiling shows that a significant fraction of the remaining
instance-initialization time after our other recent optimizations is
going into these two tasks.
This PR implements two basic ideas:
- The anyfunc array can be lazily initialized as long as we retain the
information needed to do so. For now, in this PR, we just recreate the
anyfunc whenever a pointer is taken to it, because doing so is fast
enough; in the future we could keep some state to know whether the
anyfunc has been written yet and skip this work if redundant.
This technique allows us to leave the anyfunc array as uninitialized
memory, which can be a significant savings. Filling it with
initialized anyfuncs is very expensive, but even zeroing it is
expensive: e.g. in a large module, it can be >500KB.
- A funcref table can be lazily initialized as long as we retain a link
to its corresponding instance and function index for each element. A
zero in a table element means "uninitialized", and a slowpath does the
initialization.
Funcref tables are a little tricky because funcrefs can be null. We need
to distinguish "element was initially non-null, but user stored explicit
null later" from "element never touched" (ie the lazy init should not
blow away an explicitly stored null). We solve this by stealing the LSB
from every funcref (anyfunc pointer): when the LSB is set, the funcref
is initialized and we don't hit the lazy-init slowpath. We insert the
bit on storing to the table and mask it off after loading.
We do have to set up a precomputed array of `FuncIndex`s for the table
in order for this to work. We do this as part of the module compilation.
This PR also refactors the way that the runtime crate gains access to
information computed during module compilation.
Performance effect measured with in-tree benches/instantiation.rs, using
SpiderMonkey built for WASI, and with memfd enabled:
```
BEFORE:
sequential/default/spidermonkey.wasm
time: [68.569 us 68.696 us 68.856 us]
sequential/pooling/spidermonkey.wasm
time: [69.406 us 69.435 us 69.465 us]
parallel/default/spidermonkey.wasm: with 1 background thread
time: [69.444 us 69.470 us 69.497 us]
parallel/default/spidermonkey.wasm: with 16 background threads
time: [183.72 us 184.31 us 184.89 us]
parallel/pooling/spidermonkey.wasm: with 1 background thread
time: [69.018 us 69.070 us 69.136 us]
parallel/pooling/spidermonkey.wasm: with 16 background threads
time: [326.81 us 337.32 us 347.01 us]
WITH THIS PR:
sequential/default/spidermonkey.wasm
time: [6.7821 us 6.8096 us 6.8397 us]
change: [-90.245% -90.193% -90.142%] (p = 0.00 < 0.05)
Performance has improved.
sequential/pooling/spidermonkey.wasm
time: [3.0410 us 3.0558 us 3.0724 us]
change: [-95.566% -95.552% -95.537%] (p = 0.00 < 0.05)
Performance has improved.
parallel/default/spidermonkey.wasm: with 1 background thread
time: [7.2643 us 7.2689 us 7.2735 us]
change: [-89.541% -89.533% -89.525%] (p = 0.00 < 0.05)
Performance has improved.
parallel/default/spidermonkey.wasm: with 16 background threads
time: [147.36 us 148.99 us 150.74 us]
change: [-18.997% -18.081% -17.285%] (p = 0.00 < 0.05)
Performance has improved.
parallel/pooling/spidermonkey.wasm: with 1 background thread
time: [3.1009 us 3.1021 us 3.1033 us]
change: [-95.517% -95.511% -95.506%] (p = 0.00 < 0.05)
Performance has improved.
parallel/pooling/spidermonkey.wasm: with 16 background threads
time: [49.449 us 50.475 us 51.540 us]
change: [-85.423% -84.964% -84.465%] (p = 0.00 < 0.05)
Performance has improved.
```
So an improvement of something like 80-95% for a very large module (7420
functions in its one funcref table, 31928 functions total).
wasmtime
A standalone runtime for WebAssembly
A Bytecode Alliance project
Guide | Contributing | Website | Chat
Installation
The Wasmtime CLI can be installed on Linux and macOS with a small install script:
$ curl https://wasmtime.dev/install.sh -sSf | bash
Windows or otherwise interested users can download installers and binaries directly from the GitHub Releases page.
Example
If you've got the Rust compiler installed then you can take some Rust source code:
fn main() {
println!("Hello, world!");
}
and compile/run it with:
$ rustup target add wasm32-wasi
$ rustc hello.rs --target wasm32-wasi
$ wasmtime hello.wasm
Hello, world!
Features
-
Lightweight. Wasmtime is a standalone runtime for WebAssembly that scales with your needs. It fits on tiny chips as well as makes use of huge servers. Wasmtime can be embedded into almost any application too.
-
Fast. Wasmtime is built on the optimizing Cranelift code generator to quickly generate high-quality machine code at runtime.
-
Configurable. Whether you need to precompile your wasm ahead of time, or interpret it at runtime, Wasmtime has you covered for all your wasm-executing needs.
-
WASI. Wasmtime supports a rich set of APIs for interacting with the host environment through the WASI standard.
-
Standards Compliant. Wasmtime passes the official WebAssembly test suite, implements the official C API of wasm, and implements future proposals to WebAssembly as well. Wasmtime developers are intimately engaged with the WebAssembly standards process all along the way too.
Language Support
You can use Wasmtime from a variety of different languages through embeddings of the implementation:
- Rust - the
wasmtimecrate - C - the
wasm.h,wasi.h, andwasmtime.hheaders or usewasmtimeConan package - [C++] - the
wasmtime-cpprepository or usewasmtime-cppConan package - Python - the
wasmtimePyPI package - .NET - the
WasmtimeNuGet package - Go - the
wasmtime-gorepository
Documentation
📚 Read the Wasmtime guide here! 📚
The wasmtime guide is the best starting point to learn about what Wasmtime can do for you or help answer your questions about Wasmtime. If you're curious in contributing to Wasmtime, it can also help you do that!
It's Wasmtime.