Speedup build cu files

[lexasub]

Compiling llama.cpp can be an extremely slow process, primarily due to the .cu files. These CUDA source files often take a significant amount of time to compile, and it’s unclear whether all of them need to be compiled for every specific build configuration. Ideally, if it’s possible to avoid compiling unnecessary .cu files based on conditional features or specific hardware capabilities, it would be great to have an option to easily disable unused components during the build process.

Unfortunately, we have no control over NVIDIA’s compiler (nvcc), which is a major factor in this bottleneck. However, there might be some ways to optimize the build process:

Splitting .cu Files: If the .cu files are large and contain multiple independent parts, breaking them into smaller chunks could potentially speed up the compilation process. This approach would allow the compiler to handle smaller units of work, which could reduce memory usage and improve parallelism during compilation.

Selective Compilation: Introducing build flags or configuration options to exclude unnecessary .cu files for specific builds could save time and resources. For example, if certain features or hardware-specific optimizations are not required, those parts of the code could be skipped during compilation.

Precompiled Objects: If certain .cu files don’t change often, precompiling them into object files and reusing them across builds could reduce compilation time.

Parallel Compilation: Ensuring that the build process takes full advantage of all available CPU cores (e.g., using ninja -j with an appropriate number of jobs) can help speed up the process, especially on multi-core systems.

That said, on lower-end hardware, such as dual-core CPUs, the compilation process will inevitably be slower(30m-1h compilation), and the memory usage during the build can be significant. While it’s tempting to try and optimize the .cu files further, this can be a risky and unstable path, as it may introduce bugs or compatibility issues.

For now, the best approach might be to explore splitting or modularizing the .cu files and adding build options for conditional compilation. If anyone has additional ideas or proven methods to improve the performance of CUDA file compilation, they would be highly valuable for the community.