https://github.com/akhildevelops/cudaz
Cuda library for Zig
https://github.com/akhildevelops/cudaz
zig-package
Last synced: 3 days ago
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Cuda library for Zig
- Host: GitHub
- URL: https://github.com/akhildevelops/cudaz
- Owner: akhildevelops
- License: other
- Created: 2023-11-27T03:25:51.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-29T12:37:10.000Z (about 1 month ago)
- Last Synced: 2025-03-29T13:30:39.778Z (about 1 month ago)
- Topics: zig-package
- Language: Zig
- Homepage:
- Size: 77.1 KB
- Stars: 85
- Watchers: 1
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: Readme.md
- License: LICENSE.md
Awesome Lists containing this project
- awesome-zig - akhildevelops/cudaz
README
# Cuda library for Zig
This library helps to interact with NVIDIA GPUs from zig. Provides high level interface to communicate with GPU. It can detect cuda installation and link to a project's binary on Linux/MacOS. Check [Customization](https://github.com/akhildevelops/cudaz/tree/main#Customization) to give cuda manual path.
## The library provides below features:
- Memory Allocation in GPU with defined size.
- Copying data from host to gpu and viceversa.
- Compiling (.cu) and loading kernels (.ptx) both from file and text.
- Running kernels with grid/blocks/threads configuration.
- [Generate random numbers](test/rng.zig)
Check [test](./test) folder for code samples.
>Scroll below to go through an example of incrementing each value in an array parallely using GPU.
### Install
Download and save the library path in `build.zig.zon` file by running
#### zig 0.14.0
`zig fetch --save https://github.com/akhildevelops/cudaz/archive/0.2.0.tar.gz`
#### zig 0.13.0
`zig fetch --save https://github.com/akhildevelops/cudaz/archive/0.1.0.tar.gz`
Add cudaz module in your project's `build.zig` file that will link to your project's binary.
```zig
//build.zig
const std = @import("std");
pub fn build(b: *std.Build) !void {
// exe points to main.zig that uses cudaz
const exe = b.addExecutable(.{ .name = "main", .root_source_file = .{ .path = "src/main.zig" }, .target = b.host });
// Point to cudaz dependency
const cudaz_dep = b.dependency("cudaz", .{});
// Fetch and add the module from cudaz dependency
const cudaz_module = cudaz_dep.module("cudaz");
exe.root_module.addImport("cudaz", cudaz_module);
// Dynamically link to libc, cuda, nvrtc
exe.linkLibC();
exe.linkSystemLibrary("cuda");
exe.linkSystemLibrary("nvrtc");
// Run binary
const run = b.step("run", "Run the binary");
const run_step = b.addRunArtifact(exe);
run.dependOn(&run_step.step);
}
```
### Increment Array using GPU
```zig
// src/main.zig
const std = @import("std");
const Cuda = @import("cudaz");
const CuDevice = Cuda.Device;
const CuCompile = Cuda.Compile;
const CuLaunchConfig = Cuda.LaunchConfig;
// Cuda Kernel
const increment_kernel =
\\extern "C" __global__ void increment(float *out)
\\{
\\ int i = blockIdx.x * blockDim.x + threadIdx.x;
\\ out[i] = out[i] + 1;
\\}
;
pub fn main() !void {
// Initialize allocator
var GP = std.heap.GeneralPurposeAllocator(.{}){};
defer _ = GP.deinit();
const allocator = GP.allocator();
// Initialize GPU
const device = try CuDevice.default();
defer device.free();
// Copy data from host to GPU
const data = [_]f32{ 1.2, 2.8, 0.123 };
const cu_slice = try device.htodCopy(f32, &data);
defer cu_slice.free();
// Compile and load the Kernel
const ptx = try CuCompile.cudaText(increment_kernel, .{}, allocator);
defer allocator.free(ptx);
const module = try CuDevice.loadPtxText(ptx);
const function = try module.getFunc("increment");
// Run the kernel on the data
try function.run(.{&cu_slice.device_ptr}, CuLaunchConfig{ .block_dim = .{ 3, 1, 1 }, .grid_dim = .{ 1, 1, 1 }, .shared_mem_bytes = 0 });
// Retrieve incremented data back to the system
const incremented_arr = try CuDevice.syncReclaim(f32, allocator, cu_slice);
defer incremented_arr.deinit();
}
```
For running above code system refer to the example project: [increment](./example/increment)
## Examples:
- [Incrementing array in GPU](example/increment/)
- [Sending Custom Types to GPU](example/custom_type/)
## Customization
- It is intelligent to identify and link to installed cuda libraries. If needed, provide cuda installation path manually by mentioning build parameter `zig build -DCUDA_PATH=`.
Inspired from Rust Cuda library: https://github.com/coreylowman/cudarc/tree/main