How to: Run Rust code on your NVIDIA GPU
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README.md

nvptx

How to: Run Rust code on your NVIDIA GPU

First steps

Since 2016-12-31, rustc can compile Rust code to PTX (Parallel Thread Execution) code, which is like GPU assembly, via --emit=asm and the right --target argument. This PTX code can then be loaded and executed on a GPU.

However, a few days later 128-bit integer support landed in rustc and broke compilation of the core crate for NVPTX targets (LLVM assertions). Furthermore, there was no nightly release between these two events so it was not possible to use the NVPTX backend with a nightly compiler.

Just recently (2017-05-18) I realized (thanks to this blog post) that we can work around the problem by compiling a fork of the core crate that doesn't contain code that involves 128-bit integers. Which is a bit unfortunate but, hey, if it works then it works.

Targets

The required targets are not built into the compiler (they are not in rustc --print target-list) but are available as JSON files in this repository:

If the host is running a 64-bit OS, you should use the nvptx64 target. Otherwise, use the nvptx target.

Minimal example

Here's a minimal example of emitting PTX from a Rust crate:

$ cargo new --lib kernel && cd $_

$ cat src/lib.rs
#![no_std]

fn foo() {}
# emitting debuginfo is not supported for the nvptx targets
$ edit Cargo.toml && tail -n2 $_
[profile.dev]
debug = false

# The JSON file must be in the current directory
$ test -f nvptx64-nvidia-cuda.json && echo OK
OK

# You'll need to use Xargo to build the `core` crate "on the fly"
# Install it if you don't already have it
$ cargo install xargo || true

# Then instruct Xargo to compile a fork of the core crate that contains no
# 128-bit integers
$ edit Xargo.toml && cat Xargo.toml
[dependencies.core]
git = "https://github.com/japaric/core64"

# Xargo has the exact same CLI as Cargo
$ xargo rustc --target nvptx64-nvidia-cuda -- --emit=asm
   Compiling core v0.0.0 (file://$SYSROOT/lib/rustlib/src/rust/src/libcore)
    Finished release [optimized] target(s) in 18.74 secs
   Compiling kernel v0.1.0 (file://$PWD)
    Finished debug [unoptimized] target(s) in 0.4 secs

The PTX code will be available as a .s file in the target directory:

$ find -name '*.s'
./target/nvptx64-nvidia-cuda/debug/deps/kernel-e916cff045dc0eeb.s

$ cat $(find -name '*.s')
.version 3.2
.target sm_20
.address_size 64

.func _ZN6kernel3foo17h24d36fb5248f789aE()
{
        .local .align 8 .b8     __local_depot0[8];
        .reg .b64       %SP;
        .reg .b64       %SPL;

        mov.u64         %SPL, __local_depot0;
        bra.uni         LBB0_1;
LBB0_1:
        ret;
}

Global functions

Although this PTX module (the whole file) can be loaded on the GPU, the function foo contained in it can't be "launched" by the host because it's a device function. Only global functions (AKA kernels) can be launched by the hosts.

To turn foo into a global function, its ABI must be changed to "ptx-kernel":

#![feature(abi_ptx)]
#![no_std]

extern "ptx-kernel" fn foo() {}

With that change the PTX of the foo function will now look like this:

.entry _ZN6kernel3foo17h24d36fb5248f789aE()
{
        .local .align 8 .b8     __local_depot0[8];
        .reg .b64       %SP;
        .reg .b64       %SPL;

        mov.u64         %SPL, __local_depot0;
        bra.uni         LBB0_1;
LBB0_1:
        ret;
}

foo is now a global function because it has the .entry directive instead of the .func one.

Avoiding mangling

With the CUDA API, one can retrieve functions from a PTX module by their name. foo's' final name in the PTX module has been mangled and looks like this: _ZN6kernel3foo17h24d36fb5248f789aE.

To avoid mangling the foo function add the #[no_mangle] attribute to it.

#![feature(abi_ptx)]
#![no_std]

#[no_mangle]
extern "ptx-kernel" fn foo() {}

This will result in the following PTX code:

.entry foo()
{
        .local .align 8 .b8     __local_depot0[8];
        .reg .b64       %SP;
        .reg .b64       %SPL;

        mov.u64         %SPL, __local_depot0;
        bra.uni         LBB0_1;
LBB0_1:
        ret;
}

With this change you can now refer to the foo function using the "foo" (C) string from within the CUDA API.

Optimization

So far we have been compiling the crate using the (default) "debug" profile which normally results in debuggable but slow code. Given that we can't emit debuginfo when using the nvptx targets, it makes more sense to build the crate using the "release" profile.

The catch is that we'll have to mark global functions as public otherwise the compiler will "optimize them away" and they won't make it into the final PTX file.

#![feature(abi_ptx)]
#![no_std]

#[no_mangle]
pub extern "ptx-kernel" fn foo() {}
$ cargo clean

$ xargo rustc --release --target nvptx64-nvidia-cuda -- --emit=asm

$ cat $(find -name '*.s')
.visible .entry foo()
{
        ret;
}

Examples

This repository contains runnable examples of executing Rust code on the GPU. Note that no effort has gone into ergonomically integrating both the device code and the host code :-).

There's a kernel directory, which is a Cargo project as well, that contains Rust code that's meant to be executed on the GPU. That's the "device" code.

You can convert that Rust code into a PTX module using the following command:

$ xargo rustc \
    --manifest-path kernel/Cargo.toml \
    --release \
    --target nvptx64-nvidia-cuda \
    -- --emit=asm

The PTX file will available in the kernel/target directory.

$ find kernel/target -name '*.s'
kernel/target/nvptx64-nvidia-cuda/release/deps/kernel-bb52137592af9c8c.s

The examples directory contains the "host" code. Inside that directory, there are 3 file; each file is an example program:

  • add - Add two (mathematical) vectors on the GPU
  • memcpy - memcpy on the GPU
  • rgba2gray - Convert a color image to grayscale

Each example program expects as first argument the path to the PTX file we generated previously. You can run each example with a command like this:

$ cargo run --example add -- $(find kernel/target -name '*.s')

The rgba2gray example additionally expects a second argument: the path to the image that will be converted to grayscale. That example also compares the runtime of converting the image on the GPU vs the runtime of converting the image on the CPU. Be sure to run that example in release mode to get a fair comparison!

$ cargo run --release --example rgba2gray -- $(find kernel/target -name '*.s') ferris.png
Image size: 1200x800 - 960000 pixels - 3840000 bytes

RGBA -> grayscale on the GPU
    Duration { secs: 0, nanos: 602024 } - `malloc`
    Duration { secs: 0, nanos: 718481 } - `memcpy` (CPU -> GPU)
    Duration { secs: 0, nanos: 1278006 } - Executing the kernel
    Duration { secs: 0, nanos: 306315 } - `memcpy` (GPU -> CPU)
    Duration { secs: 0, nanos: 322648 } - `free`
    ----------------------------------------
    Duration { secs: 0, nanos: 3227474 } - TOTAL

RGBA -> grayscale on the CPU
    Duration { secs: 0, nanos: 12299 } - `malloc`
    Duration { secs: 0, nanos: 4171570 } - conversion
    Duration { secs: 0, nanos: 493 } - `free`
    ----------------------------------------
    Duration { secs: 0, nanos: 4184362 } - TOTAL

Problems?

If you encounter any problem with the Rust -> PTX feature in the compiler, report it to this meta issue.

License

Licensed under either of

at your option.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.