Compiling CUDA C/C++ with LLVM — LLVM 3.9 documentation

# Compiling CUDA C/C++ with LLVM

- Introduction
- How to Build LLVM with CUDA Support
- How to Compile CUDA C/C++ with LLVM
- Detecting clang vs NVCC
- Flags that control numerical code
- Optimizations
- Publication
- Tutorial
- Obtaining Help

## Introduction

This document contains the user guides and the internals of compiling CUDA C/C++ with LLVM. It is aimed at both users who want to compile CUDA with LLVM and developers who want to improve LLVM for GPUs. This document assumes a basic familiarity with CUDA. Information about CUDA programming can be found in the CUDA programming guide.

## How to Build LLVM with CUDA Support

CUDA support is still in development and works the best in the trunk version of LLVM. Below is a quick summary of downloading and building the trunk version. Consult the Getting Started page for more details on setting up LLVM.

1. Checkout LLVM

       $ cd where-you-want-llvm-to-live
       $ svn co http://llvm.org/svn/llvm-project/llvm/trunk llvm

2. Checkout Clang

       $ cd where-you-want-llvm-to-live
       $ cd llvm/tools
       $ svn co http://llvm.org/svn/llvm-project/cfe/trunk clang

3. Configure and build LLVM and Clang

       $ cd where-you-want-llvm-to-live
       $ mkdir build
       $ cd build
       $ cmake [options] ..
       $ make

## How to Compile CUDA C/C++ with LLVM

We assume you have installed the CUDA driver and runtime. Consult the NVIDIA CUDA installation guide if you have not.

Suppose you want to compile and run the following CUDA program (`axpy.cu`) which multiplies a `float` array by a `float` scalar (AXPY).

    #include <iostream>

    __global__ void axpy(float a, float* x, float* y) {
      y[threadIdx.x] = a * x[threadIdx.x];
    }

    int main(int argc, char* argv[]) {
      const int kDataLen = 4;

      float a = 2.0f;
      float host_x[kDataLen] = {1.0f, 2.0f, 3.0f, 4.0f};
      float host_y[kDataLen];

      // Copy input data to device.
      float* device_x;
      float* device_y;
      cudaMalloc(&device_x, kDataLen * sizeof(float));
      cudaMalloc(&device_y, kDataLen * sizeof(float));
      cudaMemcpy(device_x, host_x, kDataLen * sizeof(float),
                 cudaMemcpyHostToDevice);

      // Launch the kernel.
      axpy<<<1, kDataLen>>>(a, device_x, device_y);

      // Copy output data to host.
      cudaDeviceSynchronize();
      cudaMemcpy(host_y, device_y, kDataLen * sizeof(float),
                 cudaMemcpyDeviceToHost);

      // Print the results.
      for (int i = 0; i < kDataLen; ++i) {
        std::cout << "y[" << i << "] = " << host_y[i] << "\n";
      }

      cudaDeviceReset();
      return 0;
    }

The command line for compilation is similar to what you would use for C++.

    $ clang++ axpy.cu -o axpy --cuda-gpu-arch=<GPU arch>  \
        -L<CUDA install path>/<lib64 or lib>              \
        -lcudart_static -ldl -lrt -pthread
    $ ./axpy
    y[0] = 2
    y[1] = 4
    y[2] = 6
    y[3] = 8

`<CUDA install path>` is the root directory where you installed CUDA SDK, typically `/usr/local/cuda`. `<GPU arch>` is the compute capability of your GPU. For example, if you want to run your program on a GPU with compute capability of 3.5, you should specify `--cuda-gpu-arch=sm_35`.

## Detecting clang vs NVCC

Although clang’s CUDA implementation is largely compatible with NVCC’s, you may still want to detect when you’re compiling CUDA code specifically with clang.

This is tricky, because NVCC may invoke clang as part of its own compilation process! For example, NVCC uses the host compiler’s preprocessor when compiling for device code, and that host compiler may in fact be clang.

When clang is actually compiling CUDA code – rather than being used as a subtool of NVCC’s – it defines the `__CUDA__` macro. `__CUDA_ARCH__` is defined only in device mode (but will be defined if NVCC is using clang as a preprocessor). So you can use the following incantations to detect clang CUDA compilation, in host and device modes:

    #if defined(__clang__) && defined(__CUDA__) && !defined(__CUDA_ARCH__)
      // clang compiling CUDA code, host mode.
    #endif

    #if defined(__clang__) && defined(__CUDA__) && defined(__CUDA_ARCH__)
      // clang compiling CUDA code, device mode.
    #endif

Both clang and nvcc define `__CUDACC__` during CUDA compilation. You can detect NVCC specifically by looking for `__NVCC__`.

## Flags that control numerical code

If you’re using GPUs, you probably care about making numerical code run fast. GPU hardware allows for more control over numerical operations than most CPUs, but this results in more compiler options for you to juggle.

Flags you may wish to tweak include:

- `-ffp-contract={on,off,fast}` (defaults to `fast` on host and device when compiling CUDA) Controls whether the compiler emits fused multiply-add operations.

  - `off`: never emit fma operations, and prevent ptxas from fusing multiply and add instructions.
  - `on`: fuse multiplies and adds within a single statement, but never across statements (C11 semantics). Prevent ptxas from fusing other multiplies and adds.
  - `fast`: fuse multiplies and adds wherever profitable, even across statements. Doesn’t prevent ptxas from fusing additional multiplies and adds.

  Fused multiply-add instructions can be much faster than the unfused equivalents, but because the intermediate result in an fma is not rounded, this flag can affect numerical code.

- `-fcuda-flush-denormals-to-zero` (default: off) When this is enabled, floating point operations may flush denormal inputs and/or outputs to 0. Operations on denormal numbers are often much slower than the same operations on normal numbers.

- `-fcuda-approx-transcendentals` (default: off) When this is enabled, the compiler may emit calls to faster, approximate versions of transcendental functions, instead of using the slower, fully IEEE-compliant versions. For example, this flag allows clang to emit the ptx `sin.approx.f32` instruction.

  This is implied by `-ffast-math`.

## Optimizations

CPU and GPU have different design philosophies and architectures. For example, a typical CPU has branch prediction, out-of-order execution, and is superscalar, whereas a typical GPU has none of these. Due to such differences, an optimization pipeline well-tuned for CPUs may be not suitable for GPUs.

LLVM performs several general and CUDA-specific optimizations for GPUs. The list below shows some of the more important optimizations for GPUs. Most of them have been upstreamed to `lib/Transforms/Scalar` and `lib/Target/NVPTX`. A few of them have not been upstreamed due to lack of a customizable target-independent optimization pipeline.

- Straight-line scalar optimizations. These optimizations reduce redundancy in straight-line code. Details can be found in the design document for straight-line scalar optimizations.
- Inferring memory spaces. This optimization infers the memory space of an address so that the backend can emit faster special loads and stores from it.
- Aggressive loop unrooling and function inlining. Loop unrolling and function inlining need to be more aggressive for GPUs than for CPUs because control flow transfer in GPU is more expensive. They also promote other optimizations such as constant propagation and SROA which sometimes speed up code by over 10x. An empirical inline threshold for GPUs is 1100. This configuration has yet to be upstreamed with a target-specific optimization pipeline. LLVM also provides loop unrolling pragmas and `__attribute__((always_inline))` for programmers to force unrolling and inling.
- Aggressive speculative execution. This transformation is mainly for promoting straight-line scalar optimizations which are most effective on code along dominator paths.
- Memory-space alias analysis. This alias analysis infers that two pointers in different special memory spaces do not alias. It has yet to be integrated to the new alias analysis infrastructure; the new infrastructure does not run target-specific alias analysis.
- Bypassing 64-bit divides. An existing optimization enabled in the NVPTX backend. 64-bit integer divides are much slower than 32-bit ones on NVIDIA GPUs due to lack of a divide unit. Many of the 64-bit divides in our benchmarks have a divisor and dividend which fit in 32-bits at runtime. This optimization provides a fast path for this common case.

## Publication

gpucc: An Open-Source GPGPU Compiler
Jingyue Wu, Artem Belevich, Eli Bendersky, Mark Heffernan, Chris Leary, Jacques Pienaar, Bjarke Roune, Rob Springer, Xuetian Weng, Robert Hundt
Proceedings of the 2016 International Symposium on Code Generation and Optimization (CGO 2016)
Slides for the CGO talk

## Tutorial

CGO 2016 gpucc tutorial

## Obtaining Help

To obtain help on LLVM in general and its CUDA support, see the LLVM community.