Boosting Build Performance for CUDA Projects: A Look at CUDA_SEPARABLE_COMPILATION

What it is

• Introduced in CMake version 3.8.
• It's a CMake property that controls how CUDA device code (code meant to run on the GPU) is compiled for a specific target (an executable or library you're building).

What it does

• When enabled (ON), it allows separate compilation of your CUDA device code (.cu files). This means:
  • The compiler processes the device code independently of the host code (CPU code).
  • The resulting compiled code (PTX, Portable Executable) can be reused across different executables or libraries that link to it.

Benefits

• Reduced memory usage
  The compiler doesn't need to hold all the code in memory at once during compilation.
• Improved modularity
  Separately compiled device code can be easily shared and reused across projects.
• Faster build times
  Subsequent builds that don't change the device code can reuse the pre-compiled PTX, leading to faster compilation.

How to use it

1. Enable for a target
   

   • Use the set_property command in your CMakeLists.txt file:

   set_property(TARGET my_cuda_executable PROPERTY CUDA_SEPARABLE_COMPILATION ON)
   

   Replace my_cuda_executable with the actual target name for your executable or library.

2. • The CUDA_SEPARABLE_COMPILATION property can inherit its value from the CMAKE_CUDA_SEPARABLE_COMPILATION variable if it's set globally before creating the target. This is useful for setting a default behavior.

When to use it

• If you want to improve build times, especially for large projects with frequently changing host code but relatively stable device code.
• If you have a project with multiple executables or libraries that share common CUDA device code.

Potential drawbacks

• May require additional setup or configuration for certain build environments.
• Can introduce additional complexity to the build system, especially for smaller projects.

In summary

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Basic Example

This example shows a simple project with a host program (main.cpp) and a CUDA kernel (device_functions.cu).

main.cpp

#include <iostream>

__global__ void my_kernel(int* data) {
  // Your CUDA kernel code here
}

int main() {
  // Allocate memory on the host
  int* data = new int[10];

  // Call the CUDA kernel
  my_kernel<<<1, 10>>>(data);

  // Free memory
  delete[] data;

  std::cout << "CUDA kernel execution complete!" << std::endl;

  return 0;
}

device_functions.cu

#include <cuda.h>

__global__ void my_kernel(int* data) {
  int idx = blockIdx.x * blockDim.x + threadIdx.x;
  if (idx < 10) {
    data[idx] = idx * idx;
  }
}

CMakeLists.txt

cmake_minimum_required(VERSION 3.8)
project(cuda_example)

find_package(CUDA REQUIRED)

# Enable separable compilation for the target
set_property(TARGET cuda_example PROPERTY CUDA_SEPARABLE_COMPILATION ON)

add_executable(cuda_example main.cpp)

target_link_libraries(cuda_example PRIVATE ${CUDA_LIBRARIES})

# Compile device code separately
cuda_compile(device_functions.cu OUTPUT device_functions.o)

# Add device object to the target
target_sources(cuda_example PRIVATE device_functions.o)

• The compiled object file is then added to the source files of the executable using target_sources.
• cuda_compile is used to compile device_functions.cu separately, generating device_functions.o.
• The set_property command enables separable compilation for the cuda_example target.

Project with Multiple Targets

This example showcases a project with two executables (executable1.cpp and executable2.cpp) that share a common CUDA kernel (shared_kernel.cu).

executable1.cpp

// Code specific to executable1
#include <iostream>

#include "shared_functions.h" // Header for shared functions

int main() {
  // Use functions from shared_functions.h and shared_kernel.cu
  int result = my_shared_function(10);
  std::cout << "Result from shared kernel: " << result << std::endl;

  return 0;
}

executable2.cpp

// Code specific to executable2
#include <iostream>

#include "shared_functions.h" // Header for shared functions

int main() {
  // Use functions from shared_functions.h and shared_kernel.cu
  int result = my_shared_function(20);
  std::cout << "Result from shared kernel: " << result << std::endl;

  return 0;
}

shared_functions.h

int my_shared_function(int value);

shared_kernel.cu

#include <cuda.h>

__global__ int my_shared_kernel(int value) {
  // Your CUDA kernel code here
  return value * value;
}

int my_shared_function(int value) {
  int result;
  my_shared_kernel<<<1, 1>>>(value, &result);
  cudaDeviceSynchronize();
  return result;
}

cmake_minimum_required(VERSION 3.8)
project(cuda_project)

find_package(CUDA REQUIRED)

# Enable separable compilation globally
set(CMAKE_CUDA_SEPARABLE_COMPILATION ON)

# Define header for shared functions
add_custom_target(shared_functions_header ALL
  COMMAND echo "#include \"shared_kernel.cu\"" > shared_functions.h
  VERBATIM)

# Compile device code separately
cuda_compile(shared_kernel.cu OUTPUT shared_kernel.o)

# Executable 1
add_executable(executable1 executable1.cpp)
target_link_libraries(executable1 PRIVATE ${CUDA_

---

Manual Compilation

• This approach offers more granular control over the compilation process, but can become cumbersome for larger projects.
• If you have a very small project with limited CUDA code, you can manually compile the device code alongside the host code using the cudaCompile command within your build system's scripting language (e.g., Python for tools like SCons or Bazel).

Precompiled Headers (PCH)

• While PCH doesn't directly address device code compilation, it can improve overall build times for host code, which can be beneficial for projects with a mix of host and device code.
• If your project has a significant amount of shared host code across multiple executables or libraries, you can leverage precompiled headers (PCH) to reduce redundant compilation.

Build System Integration

• However, they may require a steeper learning curve and deeper understanding of the build system's internals.
• These systems often leverage automatic dependency analysis and caching mechanisms to optimize compilation.
• Some more advanced build systems like Bazel or Ninja offer built-in support for handling dependencies between host and device code, potentially reducing the need for explicit CUDA_SEPARABLE_COMPILATION configuration.

Choosing the Right Approach

The best alternative depends on your project's specific characteristics:

• Build System Complexity
  If you're comfortable with advanced build systems, exploring their built-in CUDA handling features might be worthwhile.
• Code Sharing
  If you have a lot of shared host code, PCH can be beneficial.
• Project Size
  For small projects with minimal CUDA code, manual compilation might be sufficient.

Approach	Pros	Cons
Separable Comp.	- Faster builds (especially for large projects)	- Can add complexity to the build system
	- Improved code modularity
	- Reduced memory usage during compilation
Manual Comp.	- Granular control over compilation process	- Can be cumbersome for larger projects
Precompiled Hdr.	- Faster builds for shared host code	- Doesn't directly address device code compilation
Build System	- Automatic dependency management and caching (potentially)	- May require a steeper learning curve and deeper system understanding