Using CUDA in Go: The go-cuda Library

Introduction

CUDA (Compute Unified Device Architecture) is a parallel computing platform and software model developed by NVIDIA. It allows developers to use graphics processing units (GPUs) to perform computations, significantly accelerating the execution of computationally intensive tasks. In this article, we will introduce the library go-cudawhich provides simple and convenient interfaces for working with CUDA in the Go programming language.

Installing the library

1. Initialize your Go project: If you don't have a project yet, create one and initialize the Go module.

   go mod init your_project

2. Download the library go-cuda:
   go get github.com/Fugilove/go-cuda/src/cuda

   Examples of using

   Example 1: Vector addition

   In this example, we will create a simple program to add two vectors using CUDA.

// examples/vec_add.go
package main

/*
#include <cuda_runtime.h>
extern void VecAdd(float* A, float* B, float* C, int N);
*/
import "C"
import (
    "fmt"
    "unsafe"
    "github.com/Fugilove/go-cuda/src/cuda"
)

func main() {
    N := 1024
    size := N * 4

    cuda.Init()

    h_A := make([]float32, N)
    h_B := make([]float32, N)
    h_C := make([]float32, N)

    for i := 0; i < N; i++ {
        h_A[i] = float32(i)
        h_B[i] = float32(i * 2)
    }

    d_A := cuda.AllocateMemory(size)
    d_B := cuda.AllocateMemory(size)
    d_C := cuda.AllocateMemory(size)

    cuda.CopyToDevice(d_A, unsafe.Pointer(&h_A[0]), size)
    cuda.CopyToDevice(d_B, unsafe.Pointer(&h_B[0]), size)

    C.VecAdd((*C.float)(d_A), (*C.float)(d_B), (*C.float)(d_C), C.int(N))

    cuda.CopyToHost(unsafe.Pointer(&h_C[0]), d_C, size)

    for i := 0; i < 10; i++ {
        fmt.Printf("h_C[%d] = %f\n", i, h_C[i])
    }

    cuda.FreeMemory(d_A)
    cuda.FreeMemory(d_B)
    cuda.FreeMemory(d_C)
}

Example 2: Memory Management

This example demonstrates how to allocate and free memory on a device using go-cuda.

// examples/memory_management.go
package main

import (
    "fmt"
    "github.com/Fugilove/go-cuda/src/cuda"
)

func main() {
    size := 1024 * 4

    cuda.Init()

    d_ptr := cuda.AllocateMemory(size)
    fmt.Println("Memory allocated on device")

    cuda.FreeMemory(d_ptr)
    fmt.Println("Memory freed on device")
}

Example 3: Device Management

The example demonstrates how to get the number of CUDA devices and their properties.

// examples/device_management.go
package main

import (
    "fmt"
    "github.com/Fugilove/go-cuda/src/cuda"
)

func main() {
    cuda.Init()

    count := cuda.GetDeviceCount()
    fmt.Printf("Number of CUDA devices: %d\n", count)

    for i := 0; i < count; i++ {
        name := cuda.GetDeviceProperties(i)
        fmt.Printf("Device %d: %s\n", i, name)
    }
}

Example 4: Streams and Events

The example demonstrates the use of CUDA threads and events to synchronize tasks.

// examples/streams_events.go
package main

import (
    "fmt"
    "github.com/Fugilove/go-cuda/src/cuda"
)

func main() {
    cuda.Init()

    stream := cuda.CreateStream()
    event := cuda.CreateEvent()

    // Запуск некоторых ядер (не реализовано здесь) и использование потока и события

    cuda.RecordEvent(event, stream)
    cuda.SynchronizeEvent(event)

    fmt.Println("Event synchronized")

    cuda.DestroyEvent(event)
    cuda.DestroyStream(stream)
}

In conclusion, I can say that this is an interesting experiment “it may not work” but I will be glad to receive feedback and if you find any errors I will gladly accept them