Load CUDA Kernel at Runtime Using CUDA Driver APIs

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Introduction

In some situations, we would like to dynamically open some libraries and run some of the functions from the library. This can save some memory for the program if some libraries are not always used. To load C and C++ libraries and functions, we can use dlopen and dlsym functions to open a shared library and get the address of a function that is specified with extern "C".

In CUDA, sometimes the CUDA kernel functions were not compiled into a library and they were compiled into PTX, CUBIN, or FATBIN files. In this case, we can use the CUDA driver APIs to load the PTX, CUBIN, or FATBIN files and run the CUDA kernel functions.

Build PTX/CUBIN/FATBIN Files

There is no header file for the CUDA kernel function. The CUDA kernel function has to be specified with extern "C". Otherwise, CUDA Driver APIs cannot find the CUDA kernel function using the function name. The CUDA kernel file will be compiled into PTX, CUBIN, or FATBIN files.

vector_add.cu
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// Build commands
// ptx
// nvcc --ptx --gpu-architecture=compute_80 vector_add.cu -o vector_add.ptx
// cubin
// nvcc --cubin --gpu-architecture=compute_80 --gpu-code=sm_86 vector_add.cu -o
// vector_add.cubin
// fatbin
// nvcc --fatbin --gpu-architecture=compute_80 --gpu-code=sm_86 vector_add.cu -o
// vector_add.fatbin

#include <cuda_runtime.h>

// The extern "C" is necessary.
extern "C" __global__ void vector_add(int const* a, int const* b, int* c, unsigned int n)
{
    unsigned int const stride{blockDim.x * gridDim.x};
    unsigned int const start_idx{blockDim.x * blockIdx.x + threadIdx.x};
    for (unsigned int i{start_idx}; i < n; i += stride)
    {
        c[i] = a[i] + b[i];
    }
}

To compile the CUDA kernel function into PTX, CUBIN, or FATBIN files, please run the following commands. Adjust the --gpu-architecture and --gpu-code flags according to the GPU architecture and the GPU SM that you are using.

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$ nvcc --ptx --gpu-architecture=compute_80 vector_add.cu -o vector_add.ptx
$ nvcc --cubin --gpu-architecture=compute_80 --gpu-code=sm_86 vector_add.cu -o vector_add.cubin
$ nvcc --fatbin --gpu-architecture=compute_80 --gpu-code=sm_86 vector_add.cu -o vector_add.fatbin

The sizes of the PTX, CUBIN, and FATBIN files are different. To understand the differences between PTX, CUBIN, and FATBIN files, please refer to my previous blog post CUDA Compilation.

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$ ls -l
-rw-rw-r-- 1 1000 1000   699 Feb 24 19:30 vector_add.cu
-rw-r--r-- 1 root root  2984 Feb 24 19:29 vector_add.cubin
-rw-r--r-- 1 root root  3072 Feb 24 19:29 vector_add.fatbin
-rw-r--r-- 1 root root  1279 Feb 24 19:29 vector_add.ptx

It’s also possible to build PTX/CUBIN/FATBIN files using CMake. In our case, we could use the following CMakeLists.txt file.

CMakeLists.txt
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cmake_minimum_required(VERSION 3.28)

project(Build-CUBIN VERSION 0.0.1 LANGUAGES CXX CUDA)

set(CMAKE_CXX_STANDARD 14)
set(CMAKE_CXX_STANDARD_REQUIRED ON)

# Find CUDA Toolkit
find_package(CUDAToolkit REQUIRED)

# PTX
add_library(vector_add_ptx_sm86 OBJECT vector_add.cu)
set_target_properties(vector_add_ptx_sm86 PROPERTIES CUDA_PTX_COMPILATION ON)
set_target_properties(vector_add_ptx_sm86 PROPERTIES CUDA_ARCHITECTURES "86-virtual")

add_library(vector_add_ptx_sm80 OBJECT vector_add.cu)
set_target_properties(vector_add_ptx_sm80 PROPERTIES CUDA_PTX_COMPILATION ON)
set_target_properties(vector_add_ptx_sm80 PROPERTIES CUDA_ARCHITECTURES "80-virtual")

add_library(vector_add_ptx_sm70 OBJECT vector_add.cu)
set_target_properties(vector_add_ptx_sm70 PROPERTIES CUDA_PTX_COMPILATION ON)
set_target_properties(vector_add_ptx_sm70 PROPERTIES CUDA_ARCHITECTURES "70-virtual")

# CUBIN
add_library(vector_add_cubin_sm86 OBJECT vector_add.cu)
set_target_properties(vector_add_cubin_sm86 PROPERTIES CUDA_CUBIN_COMPILATION ON)
set_target_properties(vector_add_cubin_sm86 PROPERTIES CUDA_ARCHITECTURES "86-real")

add_library(vector_add_cubin_sm80 OBJECT vector_add.cu)
set_target_properties(vector_add_cubin_sm80 PROPERTIES CUDA_CUBIN_COMPILATION ON)
set_target_properties(vector_add_cubin_sm80 PROPERTIES CUDA_ARCHITECTURES "80-real")

add_library(vector_add_cubin_sm70 OBJECT vector_add.cu)
set_target_properties(vector_add_cubin_sm70 PROPERTIES CUDA_CUBIN_COMPILATION ON)
set_target_properties(vector_add_cubin_sm70 PROPERTIES CUDA_ARCHITECTURES "70-real")

# FATBIN
add_library(vector_add_fatbin OBJECT vector_add.cu)
set_target_properties(vector_add_fatbin PROPERTIES CUDA_FATBIN_COMPILATION ON)
set_target_properties(vector_add_fatbin PROPERTIES CUDA_ARCHITECTURES "80-virtual;80-real;80-real")

Load and Run PTX/CUBIN/FATBIN Files Using CUDA Driver APIs

To load and run the CUDA kernel functions from the PTX, CUBIN, or FATBIN files at the runtime, CUDA driver APIs shall be used.

run_vector_add.cpp
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// Build command
// g++ run_vector_add.cpp -I/usr/local/cuda/include -L/usr/local/cuda/lib64
// -lcuda -o run_vector_add

// Using CUDA driver API is sufficient.
#include <cuda.h>
#include <fstream>
#include <iostream>
#include <string>
#include <vector>

#define CHECK_CUDA_DRIVER_ERROR(val)                                           \
    check_driver((val), #val, __FILE__, __LINE__)
void check_driver(CUresult err, char const* func, char const* file, int line)
{
    if (err != CUDA_SUCCESS)
    {
        std::cerr << "CUDA Driver Error at: " << file << ":" << line
                  << std::endl;
        char const* err_string{nullptr};
        std::cerr << cuGetErrorString(err, &err_string) << std::endl;
        std::cerr << err_string << " " << func << std::endl;
        std::exit(EXIT_FAILURE);
    }
}

int main(int argc, char** argv)
{
    // Check the number of command line arguments.
    if (argc != 2)
    {
        std::cerr << "Usage: " << argv[0] << " <ptx_cubin_fatbin_file_path>"
                  << std::endl;
        return 1;
    }
    // Read the CUDA kernel library file path from the command line.
    std::string const ptx_cubin_fatbin_file_path{argv[1]};

    // CUDA device 0 is used.
    CUdevice cuda_device{0};
    CUfunction vector_add_kernel{};
    CUmodule cuda_module{};
    CUcontext cuda_context{};
    CUstream cuda_stream{};

    // Currently, this flag must be 0.
    unsigned int const cuda_driver_init_flags{0};
    // Initialize the CUDA driver API.
    CHECK_CUDA_DRIVER_ERROR(cuInit(cuda_driver_init_flags));
    // Currently, this flag must be 0.
    unsigned int const cuda_context_init_flags{0};
    // Create a CUDA context.
    CHECK_CUDA_DRIVER_ERROR(
        cuCtxCreate(&cuda_context, cuda_context_init_flags, cuda_device));
    // Create a CUDA stream.
    CHECK_CUDA_DRIVER_ERROR(
        cuStreamCreate(&cuda_stream, CU_STREAM_NON_BLOCKING));

    // Make sure that the cubin/ptx/fatbin file exists.
    std::ifstream cuda_kernel_library_file{ptx_cubin_fatbin_file_path};
    if (!cuda_kernel_library_file)
    {
        std::cerr << "Error: The cubin/ptx/fatbin file does not exist."
                  << std::endl;
        return 1;
    }

    // Create a CUDA module from the cubin/ptx/fatbin file.
    CHECK_CUDA_DRIVER_ERROR(
        cuModuleLoad(&cuda_module, ptx_cubin_fatbin_file_path.c_str()));

    // Get the CUDA kernel function from the CUDA module.
    CHECK_CUDA_DRIVER_ERROR(
        cuModuleGetFunction(&vector_add_kernel, cuda_module, "vector_add"));

    // The number of elements in the vectors.
    unsigned int num_elements{8192};

    std::vector<int> host_vector_a(num_elements, 1);
    std::vector<int> host_vector_b(num_elements, 2);
    std::vector<int> host_vector_c(num_elements, -1);
    std::vector<int> host_vector_c_reference(num_elements, -2);
    // Initialize the input vectors.
    for (size_t i{0}; i < num_elements; ++i)
    {
        host_vector_a.at(i) = i;
        host_vector_b.at(i) = i;
    }
    // Compute the reference result.
    for (size_t i{0}; i < num_elements; ++i)
    {
        host_vector_c_reference.at(i) =
            host_vector_a.at(i) + host_vector_b.at(i);
    }

    // Allocate device memory for the input vectors.
    CUdeviceptr device_vector_a{};
    CUdeviceptr device_vector_b{};
    CUdeviceptr device_vector_c{};
    CHECK_CUDA_DRIVER_ERROR(
        cuMemAlloc(&device_vector_a, num_elements * sizeof(int)));
    CHECK_CUDA_DRIVER_ERROR(
        cuMemAlloc(&device_vector_b, num_elements * sizeof(int)));
    CHECK_CUDA_DRIVER_ERROR(
        cuMemAlloc(&device_vector_c, num_elements * sizeof(int)));

    // Copy the input vectors from the host to the device.
    CHECK_CUDA_DRIVER_ERROR(cuMemcpyHtoD(device_vector_a, host_vector_a.data(),
                                         num_elements * sizeof(int)));
    CHECK_CUDA_DRIVER_ERROR(cuMemcpyHtoD(device_vector_b, host_vector_b.data(),
                                         num_elements * sizeof(int)));

    // Set the kernel parameters.
    void* kernel_params[]{&device_vector_a, &device_vector_b, &device_vector_c,
                          &num_elements};
    // Launch the CUDA kernel.
    unsigned int const block_size_x{256};
    unsigned int const block_size_y{1};
    unsigned int const block_size_z{1};
    unsigned int const grid_size_x{(num_elements + block_size_x - 1) /
                                   block_size_x};
    unsigned int const grid_size_y{1};
    unsigned int const grid_size_z{1};
    unsigned int const shared_memory_size{0};
    CHECK_CUDA_DRIVER_ERROR(cuLaunchKernel(
        vector_add_kernel, grid_size_x, grid_size_y, grid_size_z, block_size_x,
        block_size_y, block_size_z, shared_memory_size, cuda_stream,
        kernel_params, nullptr));
    CHECK_CUDA_DRIVER_ERROR(cuStreamSynchronize(cuda_stream));

    // Copy the result vector from the device to the host.
    CHECK_CUDA_DRIVER_ERROR(cuMemcpyDtoH(host_vector_c.data(), device_vector_c,
                                         num_elements * sizeof(int)));

    // Verify the result.
    for (size_t i{0}; i < num_elements; ++i)
    {
        if (host_vector_c.at(i) != host_vector_c_reference.at(i))
        {
            std::cerr << "Error: The result is incorrect." << std::endl;
            return 1;
        }
    }

    // Free device memory.
    CHECK_CUDA_DRIVER_ERROR(cuMemFree(device_vector_a));
    CHECK_CUDA_DRIVER_ERROR(cuMemFree(device_vector_b));
    CHECK_CUDA_DRIVER_ERROR(cuMemFree(device_vector_c));

    // Destroy the CUDA stream.
    CHECK_CUDA_DRIVER_ERROR(cuStreamDestroy(cuda_stream));

    // Destroy the CUDA module.
    CHECK_CUDA_DRIVER_ERROR(cuModuleUnload(cuda_module));

    // Destroy the CUDA context.
    CHECK_CUDA_DRIVER_ERROR(cuCtxDestroy(cuda_context));

    return 0;
}

To build the program, please run the following commands.

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$ g++ -o run_vector_add run_vector_add.cpp -I/usr/local/cuda/include -L/usr/local/cuda/lib64 -lcuda

To run the program, please run the following commands. There shall be no error encountered.

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$ ./run_vector_add vector_add.ptx
$ ./run_vector_add vector_add.cubin
$ ./run_vector_add vector_add.fatbin

Load and Run PTX/CUBIN/FATBIN Strings Using CUDA Driver APIs

It is also common to see the PTX, CUBIN, or FATBIN strings are embedded in the C++ source code. For example, the TensorRT multi-head attention kernel cubins were represented as a string in the C++ source code. In this case, the PTX, CUBIN, or FATBIN strings are converted from the PTX, CUBIN, or FATBIN files using some scripts.

For example, we could use the following Python scripts to convert the PTX, CUBIN, or FATBIN files we just built to the PTX, CUBIN, or FATBIN strings.

convert_file_to_chars.py
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# Open a specified file and convert every byte to a list of unsigned chars in hexadecimal format,
# then write the list to a new text file.
# Usage: python convert_file_to_chars.py --input_bin_file <input_file> --output_char_file <output_file>

import argparse
import os


def main():

    parser = argparse.ArgumentParser(
        description='Convert a binary file to a list of unsigned chars')
    parser.add_argument('--input_bin_file',
                        type=str,
                        help='The input binary file')
    parser.add_argument('--output_char_file',
                        type=str,
                        help='The output char file')

    args = parser.parse_args()

    if not os.path.isfile(args.input_bin_file):
        print('The input binary file does not exist')
        return

    with open(args.input_bin_file, 'rb') as f:
        content = f.read()
    chars = list(content)
    # Convert each char to a string in hexadecimal format "0x??"
    chars = ['0x{:02x}'.format(c) for c in chars]
    with open(args.output_char_file, 'w') as f:
        f.write(', '.join(chars))


if __name__ == "__main__":

    main()
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$ python3 convert_file_to_chars.py --help
usage: convert_file_to_chars.py [-h] [--input_bin_file INPUT_BIN_FILE]
                                [--output_char_file OUTPUT_CHAR_FILE]

Convert a binary file to a list of unsigned chars

options:
  -h, --help            show this help message and exit
  --input_bin_file INPUT_BIN_FILE
                        The input binary file
  --output_char_file OUTPUT_CHAR_FILE
                        The output char file

The PTX, CUBIN, or FATBIN strings should then be copied to the C++ source code.

vector_add_ptx_cubin_fatbin_sm86.hpp
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extern unsigned char vector_add_int_ptx[];
extern unsigned char const vector_add_int_cubin[];
extern unsigned char vector_add_int_fatbin[];
vector_add_ptx_cubin_fatbin_sm86.cpp
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#include "vector_add_ptx_cubin_fatbin_sm86.hpp"

unsigned char vector_add_int_ptx[] = {
    0x2f, 0x2f, 0x0a, 0x2f, 0x2f, 0x20, 0x47, 0x65, 0x6e, 0x65, 0x72, 0x61,
    0x74, 0x65, 0x64, 0x20, 0x62, 0x79, 0x20, 0x4e, 0x56, 0x49, 0x44, 0x49,
    0x41, 0x20, 0x4e, 0x56, 0x56, 0x4d, 0x20, 0x43, 0x6f, 0x6d, 0x70, 0x69,
    0x6c, 0x65, 0x72, 0x0a, 0x2f, 0x2f, 0x0a, 0x2f, 0x2f, 0x20, 0x43, 0x6f,
    0x6d, 0x70, 0x69, 0x6c, 0x65, 0x72, 0x20, 0x42, 0x75, 0x69, 0x6c, 0x64,
    0x20, 0x49, 0x44, 0x3a, 0x20, 0x43, 0x4c, 0x2d, 0x33, 0x34, 0x30, 0x39,
    0x37, 0x39, 0x36, 0x37, 0x0a, 0x2f, 0x2f, 0x20, 0x43, 0x75, 0x64, 0x61,
    0x20, 0x63, 0x6f, 0x6d, 0x70, 0x69, 0x6c, 0x61, 0x74, 0x69, 0x6f, 0x6e,
    0x20, 0x74, 0x6f, 0x6f, 0x6c, 0x73, 0x2c, 0x20, 0x72, 0x65, 0x6c, 0x65,
    0x61, 0x73, 0x65, 0x20, 0x31, 0x32, 0x2e, 0x34, 0x2c, 0x20, 0x56, 0x31,
    0x32, 0x2e, 0x34, 0x2e, 0x31, 0x33, 0x31, 0x0a, 0x2f, 0x2f, 0x20, 0x42,
    0x61, 0x73, 0x65, 0x64, 0x20, 0x6f, 0x6e, 0x20, 0x4e, 0x56, 0x56, 0x4d,
    0x20, 0x37, 0x2e, 0x30, 0x2e, 0x31, 0x0a, 0x2f, 0x2f, 0x0a, 0x0a, 0x2e,
    0x76, 0x65, 0x72, 0x73, 0x69, 0x6f, 0x6e, 0x20, 0x38, 0x2e, 0x34, 0x0a,
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    0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xef, 0x00, 0x00, 0x00,
    0x0b, 0x00, 0x00, 0x70, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0x04, 0x00, 0x00,
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    0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
    0x00, 0x00, 0x00, 0x00, 0x6d, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
    0x42, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
    0x00, 0x00, 0x00, 0x00, 0x48, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
    0x7c, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
    0x0b, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x32, 0x00, 0x00, 0x00,
    0x01, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00,
    0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
    0x03, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x0e, 0x80, 0x00, 0x00, 0x00,
    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
    0x06, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x00,
    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xa8, 0x00, 0x00, 0x00,
    0x00, 0x00, 0x00, 0x00, 0xa8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
    0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
    0x05, 0x00, 0x00, 0x00, 0x48, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
    0x00, 0x00, 0x00, 0x00, 0xb8, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
    0xb8, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,
    0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00,
    0x00, 0x0b, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
    0xa8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xa8, 0x00, 0x00, 0x00,
    0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00};

Finally, these PTX, CUBIN, and FATBIN bytes can be loaded into the CUDA driver API using the cuModuleLoadData function, similar to the cuModuleLoad function we used previously.

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// Build command
// g++ run_vector_add_sm86.cpp vector_add_ptx_cubin_fatbin_sm86.cpp
// -I/usr/local/cuda/include -L/usr/local/cuda/lib64 -lcuda -o
// run_vector_add_sm86

// Using CUDA driver API is sufficient.
#include <cuda.h>
#include <fstream>
#include <iostream>
#include <string>
#include <vector>

#include "vector_add_ptx_cubin_fatbin_sm86.hpp"

#define CHECK_CUDA_DRIVER_ERROR(val)                                           \
    check_driver((val), #val, __FILE__, __LINE__)
void check_driver(CUresult err, char const* func, char const* file, int line)
{
    if (err != CUDA_SUCCESS)
    {
        std::cerr << "CUDA Driver Error at: " << file << ":" << line
                  << std::endl;
        char const* err_string{nullptr};
        std::cerr << cuGetErrorString(err, &err_string) << std::endl;
        std::cerr << err_string << " " << func << std::endl;
        std::exit(EXIT_FAILURE);
    }
}

int main(int argc, char** argv)
{
    // The user can specify whether to use the PTX, CUBIN, or FATBIN bytes.
    if (argc != 2)
    {
        std::cerr << "Usage: " << argv[0] << " ptx|cubin|fatbin" << std::endl;
        return 1;
    }

    // CUDA device 0 is used.
    CUdevice cuda_device{0};
    CUfunction vector_add_kernel{};
    CUmodule cuda_module{};
    CUcontext cuda_context{};
    CUstream cuda_stream{};

    // Currently, this flag must be 0.
    unsigned int const cuda_driver_init_flags{0};
    // Initialize the CUDA driver API.
    CHECK_CUDA_DRIVER_ERROR(cuInit(cuda_driver_init_flags));
    // Currently, this flag must be 0.
    unsigned int const cuda_context_init_flags{0};
    // Create a CUDA context.
    CHECK_CUDA_DRIVER_ERROR(
        cuCtxCreate(&cuda_context, cuda_context_init_flags, cuda_device));
    // Create a CUDA stream.
    CHECK_CUDA_DRIVER_ERROR(
        cuStreamCreate(&cuda_stream, CU_STREAM_NON_BLOCKING));

    // Create a CUDA module from the text string.
    if (std::string{argv[1]} == "ptx")
    {
        CHECK_CUDA_DRIVER_ERROR(cuModuleLoadData(
            &cuda_module, static_cast<void const*>(vector_add_int_ptx)));
    }
    else if (std::string{argv[1]} == "cubin")
    {
        CHECK_CUDA_DRIVER_ERROR(cuModuleLoadData(
            &cuda_module, static_cast<void const*>(vector_add_int_cubin)));
    }
    else if (std::string{argv[1]} == "fatbin")
    {
        CHECK_CUDA_DRIVER_ERROR(cuModuleLoadData(
            &cuda_module, static_cast<void const*>(vector_add_int_fatbin)));
    }
    else
    {
        std::cerr << "Error: The byte type is not supported." << std::endl;
        return 1;
    }

    // Get the CUDA kernel function from the CUDA module.
    CHECK_CUDA_DRIVER_ERROR(
        cuModuleGetFunction(&vector_add_kernel, cuda_module, "vector_add"));

    // The number of elements in the vectors.
    unsigned int num_elements{8192};

    std::vector<int> host_vector_a(num_elements, 1);
    std::vector<int> host_vector_b(num_elements, 2);
    std::vector<int> host_vector_c(num_elements, -1);
    std::vector<int> host_vector_c_reference(num_elements, -2);
    // Initialize the input vectors.
    for (size_t i{0}; i < num_elements; ++i)
    {
        host_vector_a.at(i) = i;
        host_vector_b.at(i) = i;
    }
    // Compute the reference result.
    for (size_t i{0}; i < num_elements; ++i)
    {
        host_vector_c_reference.at(i) =
            host_vector_a.at(i) + host_vector_b.at(i);
    }

    // Allocate device memory for the input vectors.
    CUdeviceptr device_vector_a{};
    CUdeviceptr device_vector_b{};
    CUdeviceptr device_vector_c{};
    CHECK_CUDA_DRIVER_ERROR(
        cuMemAlloc(&device_vector_a, num_elements * sizeof(int)));
    CHECK_CUDA_DRIVER_ERROR(
        cuMemAlloc(&device_vector_b, num_elements * sizeof(int)));
    CHECK_CUDA_DRIVER_ERROR(
        cuMemAlloc(&device_vector_c, num_elements * sizeof(int)));

    // Copy the input vectors from the host to the device.
    CHECK_CUDA_DRIVER_ERROR(cuMemcpyHtoD(device_vector_a, host_vector_a.data(),
                                         num_elements * sizeof(int)));
    CHECK_CUDA_DRIVER_ERROR(cuMemcpyHtoD(device_vector_b, host_vector_b.data(),
                                         num_elements * sizeof(int)));

    // Set the kernel parameters.
    void* kernel_params[]{&device_vector_a, &device_vector_b, &device_vector_c,
                          &num_elements};
    // Launch the CUDA kernel.
    unsigned int const block_size_x{256};
    unsigned int const block_size_y{1};
    unsigned int const block_size_z{1};
    unsigned int const grid_size_x{(num_elements + block_size_x - 1) /
                                   block_size_x};
    unsigned int const grid_size_y{1};
    unsigned int const grid_size_z{1};
    unsigned int const shared_memory_size{0};
    CHECK_CUDA_DRIVER_ERROR(cuLaunchKernel(
        vector_add_kernel, grid_size_x, grid_size_y, grid_size_z, block_size_x,
        block_size_y, block_size_z, shared_memory_size, cuda_stream,
        kernel_params, nullptr));
    CHECK_CUDA_DRIVER_ERROR(cuStreamSynchronize(cuda_stream));

    // Copy the result vector from the device to the host.
    CHECK_CUDA_DRIVER_ERROR(cuMemcpyDtoH(host_vector_c.data(), device_vector_c,
                                         num_elements * sizeof(int)));

    // Verify the result.
    for (size_t i{0}; i < num_elements; ++i)
    {
        if (host_vector_c.at(i) != host_vector_c_reference.at(i))
        {
            std::cerr << "Error: The result is incorrect." << std::endl;
            return 1;
        }
    }

    // Free device memory.
    CHECK_CUDA_DRIVER_ERROR(cuMemFree(device_vector_a));
    CHECK_CUDA_DRIVER_ERROR(cuMemFree(device_vector_b));
    CHECK_CUDA_DRIVER_ERROR(cuMemFree(device_vector_c));

    // Destroy the CUDA stream.
    CHECK_CUDA_DRIVER_ERROR(cuStreamDestroy(cuda_stream));

    // Destroy the CUDA module.
    CHECK_CUDA_DRIVER_ERROR(cuModuleUnload(cuda_module));

    // Destroy the CUDA context.
    CHECK_CUDA_DRIVER_ERROR(cuCtxDestroy(cuda_context));

    return 0;
}

To build the program, please run the following command.

1
$ g++ run_vector_add_sm86.cpp vector_add_ptx_cubin_fatbin_sm86.cpp -I/usr/local/cuda/include -L/usr/local/cuda/lib64 -lcuda -o run_vector_add_sm86

To run the program, please run the following commands. There shall be no error encountered.

1
2
3
$ ./run_vector_add_sm86 ptx
$ ./run_vector_add_sm86 cubin
$ ./run_vector_add_sm86 fatbin

References

Load CUDA Kernel at Runtime Using CUDA Driver APIs

https://leimao.github.io/blog/CUDA-Driver-Runtime-Load-Run-Kernel/

Author

Lei Mao

Posted on

06-30-2025

Updated on

06-30-2025

Licensed under

 

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