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https://github.com/akhildevelops/cudaz

A Zig Cuda wrapper
https://github.com/akhildevelops/cudaz

zig-package

Last synced: 29 days ago
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A Zig Cuda wrapper

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README 

        
![AI Generated](cuda_zig.jpeg)

# Cuda library for Zig

### Works with latest zig v0.12.0

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.zig](./test.zig) file 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 fetch --save https://github.com/akhildevelops/cudaz/tarball/master`



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