How to compile PTX code

Learn how to compile ptx code with practical examples, diagrams, and best practices. Covers cuda, nvcc, ptx development techniques with visual explanations.

Compiling PTX Code: A Deep Dive into NVIDIA's Parallel Thread Execution

Learn the essential steps and tools for compiling PTX (Parallel Thread Execution) code, NVIDIA's low-level virtual assembly language for GPUs, to optimize your CUDA applications.

PTX (Parallel Thread Execution) is a low-level, assembly-like virtual instruction set architecture (ISA) designed by NVIDIA. It serves as an intermediate representation between high-level CUDA C/C++ code and the actual machine code executed by NVIDIA GPUs. Understanding how to compile PTX code is crucial for advanced CUDA development, debugging, and performance optimization. This article will guide you through the process, from generating PTX to compiling it into executable SASS (Streaming Assembler) code.

Understanding the CUDA Compilation Workflow

Before diving into PTX compilation, it's important to grasp the overall CUDA compilation process. When you compile a CUDA C/C++ source file (.cu), nvcc (NVIDIA CUDA Compiler) performs several stages. It first separates host code from device code. The device code is then compiled into PTX, which is an architecture-independent intermediate representation. Finally, this PTX is further compiled into SASS, the GPU's native machine code, which is specific to a particular GPU architecture (e.g., sm_75 for Turing, sm_86 for Ampere).

flowchart TD
    A[CUDA C/C++ Source (.cu)] --> B{nvcc Compiler}
    B --> C[Host Code (C/C++)]
    B --> D[Device Code (CUDA C/C++)]
    D --> E[PTX Intermediate Representation]
    E --> F[SASS Machine Code]
    C --> G[Host Executable]
    F --> H[GPU Execution]
    G --"Calls"--> H

Simplified CUDA Compilation Workflow

Generating PTX from CUDA C/C++

The first step in working with PTX is to generate it from your CUDA C/C++ source code. The nvcc compiler provides options to output PTX directly. This is useful for inspecting the intermediate representation, understanding how your high-level code translates to GPU instructions, and sometimes for manual optimization or debugging.

nvcc -ptx my_kernel.cu -o my_kernel.ptx

Compiling a CUDA C/C++ file to PTX

This command will compile my_kernel.cu and output the PTX code into my_kernel.ptx. You can then open this .ptx file with a text editor to view the generated assembly-like instructions. The PTX code will contain directives for the target architecture, function definitions, memory operations, and arithmetic instructions.

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When generating PTX, you might want to specify the target compute capability using the -arch flag (e.g., -arch=sm_75). While PTX is designed to be architecture-independent, specifying the target can sometimes influence the generated PTX for better optimization or feature utilization.

Compiling PTX to SASS (GPU Machine Code)

Once you have a PTX file, you can compile it into SASS, the actual machine code that runs on the GPU. This process is typically handled automatically by nvcc when you compile a .cu file, but you can also compile a standalone .ptx file. This is particularly useful if you've manually written or modified PTX code and want to test it.

nvcc my_kernel.ptx -o my_kernel.o -arch=sm_75 -c
nvcc host_code.cpp my_kernel.o -o my_program

Compiling PTX to an object file and linking with host code

In this example:

nvcc my_kernel.ptx -o my_kernel.o -arch=sm_75 -c compiles the my_kernel.ptx file into an object file (.o) containing the SASS code for compute capability sm_75. The -c flag indicates compilation only, without linking.
nvcc host_code.cpp my_kernel.o -o my_program then links this object file with your host C++ code (host_code.cpp) to create the final executable my_program.

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Ensure that the -arch flag used during PTX compilation matches the target GPU architecture you intend to run the code on. Mismatched architectures can lead to performance issues or runtime errors.

Advanced PTX Compilation and Inspection

For deeper analysis, you can use tools like cuobjdump to inspect the SASS code generated from your PTX or CUDA binaries. This can reveal low-level details about instruction scheduling, register usage, and memory access patterns, which are invaluable for advanced optimization.

nvcc my_kernel.cu -o my_program
cuobjdump -sass my_program

Inspecting SASS code from a compiled CUDA executable

The cuobjdump -sass command will disassemble the SASS code embedded within your CUDA executable, allowing you to see the actual machine instructions executed by the GPU. This is the ultimate level of detail for understanding GPU execution.

1. Step 1: Write CUDA Kernel

Develop your CUDA C/C++ kernel in a .cu file, focusing on correctness and initial functionality.

2. Step 2: Generate PTX

Use nvcc -ptx your_kernel.cu -o your_kernel.ptx to generate the PTX intermediate representation. Inspect this file to understand the compiler's output.

3. Step 3: Compile PTX to Object File

Compile the generated (or manually written) PTX file into an object file using nvcc your_kernel.ptx -o your_kernel.o -arch=sm_XX -c, specifying your target compute capability.

4. Step 4: Link with Host Code

Link the PTX-compiled object file with your host application code: nvcc host_app.cpp your_kernel.o -o final_executable.

5. Step 5: Inspect SASS (Optional)

For advanced optimization, use cuobjdump -sass final_executable to view the final SASS machine code and identify potential bottlenecks.