VectorWare GPU code can now use Rust's portable SIMD. We share the implementation approach and what this unlocks for GPU programming.
At [VectorWare](/), we are building the first
[GPU-native software company](/blog/announcing-vectorware/). Today, we are excited to
announce that we can successfully use Rust's portable SIMD ( core::simd) on the GPU. This milestone marks a significant step towards our vision of enabling developers to write complex, high-performance applications that leverage the full power of GPU hardware
using familiar Rust abstractions.
Parallelism below the thread #
When we [brought Rust threads to the GPU](/blog/threads-on-gpu/), we mapped each
[ std::thread](https://doc.rust-lang.org/std/thread/) to a GPU
warp. This let us run many concurrent threads on the GPU but did not use the parallel
laneswithin each thread/warp.
On the CPU, the abstraction for parallelism within a thread is
SIMD. A single instruction
operates on several data elements packed into a vector unit: where scalar code adds
two numbers, a SIMD add takes two vectors of, say, eight f32
values and produces eight sums at once. This data parallelism is inside a single thread, below the level where the operating system schedules anything.
Rust's portable SIMD #
Historically, writing SIMD in Rust meant reaching for the architecture-specific vendor
intrinsics in [ core::arch](https://doc.rust-lang.org/core/arch/index.html), such as
[on x86-64 or](https://doc.rust-lang.org/beta/core/arch/x86_64/fn._mm256_add_ps.html)
_mm256_add_ps
vaddq_f32
Rust's portable SIMD instead adds a layer of abstraction above these intrinsics. It provides a single generic type Simd<T, N> that represents a vector of
N
elements of type T
. A program writes its arithmetic, comparisons,
reductions, and lane shuffles once against Simd
and the compiler lowers them to whatever
vector instructions the target CPU has.At VectorWare, we realized the GPU is just one more piece of vector hardware for
portable SIMD to target. As a bonus, portable SIMD lives in core
rather than std
and it does not even need the [ std support we brought to the
GPU](/blog/rust-std-on-gpu).
SIMT is SIMD #
GPUs execute in a model NVIDIA calls SIMT, or Single Instruction, Multiple Thread. A warp issues one instruction, and each of its 32 lanes runs that instruction on its own data. One instruction operating on many data elements is exactly what SIMD means, and the per-lane addressing that SIMT adds does not change that. A warp is a wide vector unit and a portable SIMD vector maps onto that unit directly.
For example, a Simd<i16, 32>
gives one
i16
element to each of the warp's 32 lanes, and adding two such vectors compiles to a single warp instruction in which every lane adds its element at once.
This new mapping completes the parallelism hierarchy from our earlier work. On the CPU, a thread contains SIMD lanes, and on the GPU [our std::thread is a
warp](/blog/threads-on-gpu/) whose hardware lanes play the same role. In both cases,
`core::simd`
drives those lanes.## A world first: `core::simd`
on the GPU
As with our earlier posts, this is hard to show visually because the code is ordinary
Rust. The same core::simd
types that lower to x86-64 SIMD on a laptop lower to warp
operations on the GPU, with no change to the source.
Here we define a small portable SIMD routine and call it from main
. It exercises
the core features of the model: elementwise arithmetic, a comparison that produces a
lane mask, a select
driven by that mask, and a horizontal reduction across lanes.
The entry point is a normal fn main
with no GPU-specific annotations. Our toolchain compiles it to a GPU kernel, and the result is printed from the device using our [ std
support](/blog/rust-std-on-gpu). Below is a recording of the program running on the GPU, producing the exact same output as [running it on the
CPU](https://play.rust-lang.org/?version=nightly&mode=debug&edition=2024&gist=c6fd3bb9bb99b2bb92b2255c3174ac7b).
Implementation #
As previously mentioned, the mapping rests on a single observation: a warp is a vector
unit whose lanes are individually addressable. Once Simd<T, N>
is laid out per lane, each family of operations has a direct warp-level counterpart.
SIMD elementwise operations are the easy case. Addition, multiplication, comparison, and
the other lane-wise operators come from ordinary Rust trait implementations on Simd
such as
Add. The GPU runs them natively. SIMD reductions such as reduce_sum and
reduce_max
SIMD cross-lane shuffles, such as simd_swizzle! and rotates, move elements between lanes. Because a SIMD lane is a GPU warp lane, these map onto the same warp shuffle primitives that make GPU lanes so good at exchanging data.
SIMD masks map just as cleanly. A Mask<T, N> gives one predicate to each SIMD lane.
performs a selection in every warp lane. Horizontal mask queries such as
`Mask::select`
[and](https://doc.rust-lang.org/core/simd/struct.Mask.html#method.any)
any
use GPU
all
vote and ballotinstructions.
Scalar values in the surrounding code, such as a loop counter or a constant, are computed
identically by every lane and so are simply replicated across the warp just like in ordinary
CUDA. This is the same uniform-versus-varying distinction that data-parallel languages like
ISPC make explicit, except here it falls out of Rust's own types: a
plain f32
is uniform, a Simd<f32, 32>
is varying.
Working with lanes #
The one place the abstraction and the hardware do not line up is lane count.
On the CPU a Simd<T, N>
allows any N
from 1 through 64, but GPU hardware has a fixed
width: 32 lanes on NVIDIA and 32 or 64 on AMD. The mapping is one to one only when `N`
matches that width. A smaller N
leaves some lanes idle while a larger N
gives some or all lanes more than one element to process.
When there is more work than the warp is wide, we need a way to say which lanes do what. It helps to think of the warp as a small "machine" of its own: a fixed set of primitives for moving and combining data across lanes, plus invariants about which lanes are active and how much data each one holds. "Programming" it means placing work onto lanes within those rules.
At VectorWare, we give that machine an IR. Rather than a standalone data structure, we encode it in Rust's type system using types, generics, const generics, and trait bounds. A program is composed of typed operations: ballots, shuffles, reductions, scans, gathers, scatters, atomics, and strip mining for vectors wider than the warp. Operands, execution shape, and capacity are typed too. Because the operations carry their shape in the types, many invalid programs cannot be constructed at all.
The IR needs no interpreter on the GPU. Each operation lowers straight to the corresponding instructions with zero cost over hand-written PTX. The same types let us run it on the CPU too. We built a reference interpreter that executes the IR deterministically, a kind of Miri for warp-lane programming. We use it to simulate GPU code and for [differential
testing](https://en.wikipedia.org/wiki/Differential_testing). Our work targets NVIDIA today, but nothing here is CUDA specific. AMD wavefronts and Vulkan subgroups expose similar primitives and semantics. The IR itself is architecture-agnostic Rust.
Benefits #
The same source runs on the CPU and the GPU. Code and libraries that already use portable SIMD become candidates for GPU execution without a rewrite.
Unmodified CPU code can use GPU lane-level parallelism. GPU-aware code can still go further by using
core::arch
intrinsics that map directly to PTX.
A Simd<T, N>
is an ordinary owned value. The borrow checker, lifetimes, and type checking apply to it exactly as they do on the CPU. We are not adding a GPU-specific vector type or a new set of annotations. We are mapping Rust's existing portable SIMD onto the GPU's native execution model. At
VectorWare, we are making GPUs behave like a normal Rust platform.
Downsides #
Portable SIMD is still unstable in Rust. It requires the nightly
#![feature(portable_simd)]
, and its surface may change before it
stabilizes.
Vectors narrower than the warp leave lanes idle, and vectors wider than the warp turn each operation into more instructions. The abstraction is only zero cost when the vector width matches the number of warp lanes.
Not every cross-lane operation maps to an efficient warp instruction. Shuffles that match
the hardware's supported patterns are cheap, but arbitrary permutations may need several
instructions or a trip through shared memory. Horizontal operations like reductions and
all
/any
also act as synchronization points within the warp, which constrains how freely the scheduler can overlap work.
We had to change the compiler to make the abstraction sound when interacting with other Rust features. As this is uncharted territory, we are not yet confident we have covered every case.
Future work #
With SIMD, threads, and async all mapped onto the GPU, the natural next step is composing
them: threads spreading work across warps, core::simd
spreading data across the lanes
within each warp, and async structuring the concurrency between them.
We are also interested in lowering matrix-shaped SIMD onto the GPU's tensor
cores, and in auto-vectorizing
ordinary scalar Rust loops into Simd
operations so that code gets warp-level
parallelism without being written against core::simd
at all. As members of the Rust compiler team, we are keen to explore how much of this can happen in the compiler itself.
A vector representation shared across the CPU and the GPU is valuable, though it is not clear that today's portable SIMD types are the right basis for one.
For one thing, they largely sit in a world of their own within the core
and std
APIs. More exploration is necessary.
Is VectorWare only focused on Rust? #
The speed at which we are able to make progress on the GPU is a testament to the power of Rust's abstractions and ecosystem.
As a company, we understand that not everyone uses Rust. Our future products will support multiple programming languages and runtimes. However, we believe Rust is uniquely well suited to building high-performance, reliable GPU-native applications and that is what we are most excited about.
Follow along #
Follow us on [X](https://x.com/vectorware),
[Bluesky](https://bsky.app/profile/vectorware.com),
[LinkedIn](https://www.linkedin.com/company/vectorware/), or subscribe to our
blog to stay updated on our progress. We will be sharing more about our work in the coming months. You can also reach us at hello@vectorware.com.