How is CUDA compiled?

The compilation of CUDA code is a sophisticated, multi-stage process orchestrated by NVIDIA's nvcc compiler driver, which fundamentally separates the host (CPU) code from the device (GPU) code. This dual-path approach ensures that each part of your application is optimized for its respective processor.

The CUDA Compilation Trajectory: A Dual-Path Approach

At its core, CUDA compilation involves a specialized "trajectory" that distinctly processes the CPU-bound logic and the GPU-bound kernels. This separation is crucial for generating highly optimized code for both architectures.

Key Stages of CUDA Compilation

The nvcc compiler acts as a front-end, intelligently directing different parts of your source code to the appropriate compilers and tools. Here's a breakdown of the typical compilation workflow:

Source Code Separation: nvcc first parses the .cu (CUDA C++) source files. It identifies and separates the device functions (CUDA kernels and helper functions destined for the GPU) from the host code (standard C++ functions for the CPU).
Device Code Compilation:
- The device functions are compiled using proprietary NVIDIA compilers and an assembler.
- This process often first generates an intermediate representation called PTX (Parallel Thread Execution) assembly code. PTX is a high-level, virtual instruction set architecture for parallel computation.
- Subsequently, ptxas (the PTX assembler) translates the PTX code into SASS (Streaming Assembly), which is the native machine code for a specific NVIDIA GPU architecture.
Host Code Compilation:
- The host code, stripped of its CUDA device functions, is then compiled by a standard C++ host compiler (e.g., GCC, Clang, Microsoft Visual C++). This generates standard CPU object files.
Embedding Fatbinaries:
- The compiled GPU functions (the SASS or PTX code) are packaged into a special container called a fatbinary image.
- This fatbinary is then embedded directly into the host object files or the final host executable. A fatbinary can contain code for multiple GPU architectures, allowing the same executable to run on different NVIDIA GPUs.
Linking:
- Finally, a standard linker combines the host object files, the embedded fatbinary, and any necessary CUDA runtime libraries to produce the final executable application.

The Role of `nvcc`

The nvcc command is not a compiler in itself, but rather a compiler driver. It orchestrates the entire compilation process by invoking a sequence of tools:

It acts as a preprocessor, separating host and device code.
It calls the appropriate host compiler (like g++ or cl.exe) for the host C++ code.
It invokes NVIDIA's proprietary device compiler for the CUDA device code.
It manages the generation and embedding of fatbinaries.
It ultimately calls the system's linker to produce the final executable.

This abstraction simplifies the developer's experience, as they typically only need to invoke nvcc rather than managing each individual compilation step.

Understanding Fatbinaries

A fatbinary is a crucial concept in CUDA compilation. It's a single binary object that can contain multiple versions of the same device code, each tailored for different NVIDIA GPU architectures (e.g., sm_75 for Turing, sm_86 for Ampere, sm_89 for Ada Lovelace).

Benefits of Fatbinaries:

Portability: A single executable can run efficiently on various NVIDIA GPUs without recompilation.
Runtime Selection: When the application runs, the CUDA driver automatically selects and loads the most appropriate kernel code for the detected GPU.
Forward Compatibility: Including PTX code in a fatbinary provides a degree of forward compatibility. If a future GPU architecture is encountered for which no specific SASS code is present, the driver can just-in-time compile the PTX code for that new architecture.

Developers typically specify which GPU architectures to target using nvcc flags like -gencode arch=compute_XX,code=sm_YY, where XX refers to the virtual architecture (PTX) and YY refers to the physical architecture (SASS).

Example Compilation Flow with `nvcc`

Stage	Input	Output	Tools Involved
CUDA Frontend (`nvcc`)	`.cu` (CUDA C++) file	Host `.cpp` & Device `.gpu`	`nvcc`
Host Compilation	Host `.cpp` file	Host `.o` (object file)	Standard C++ Compiler (e.g., `g++`)
Device Compilation	Device `.gpu` file	PTX Assembly	NVIDIA Device Compiler
PTX Assembly	PTX Assembly	SASS (Machine Code)	`ptxas` (PTX Assembler)
Fatbinary Generation	SASS/PTX, Host `.o` metadata	Fatbinary embedded into Host `.o`	`nvlink` (`nvcc` manages)
Linking	Host `.o` with Fatbinary	Executable (`.exe`/a.out)	System Linker (`ld`)

This sophisticated compilation process is what enables CUDA applications to harness the parallel processing power of NVIDIA GPUs while seamlessly integrating with traditional CPU-based workflows.