Compiling CUDA C/C++ with LLVM


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

Below is a quick summary of downloading and building LLVM. Consult the Getting Started page for more details on setting up LLVM.

  1. Checkout LLVM

    $ cd where-you-want-llvm-to-live
    $ svn co llvm
  2. Checkout Clang

    $ cd where-you-want-llvm-to-live
    $ cd llvm/tools
    $ svn co 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 ( which multiplies a float array by a float scalar (AXPY).

#include <helper_cuda.h> // for checkCudaErrors

#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;
  checkCudaErrors(cudaMalloc(&device_x, kDataLen * sizeof(float)));
  checkCudaErrors(cudaMalloc(&device_y, kDataLen * sizeof(float)));
  checkCudaErrors(cudaMemcpy(device_x, host_x, kDataLen * sizeof(float),

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

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

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

  return 0;

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

$ clang++ -o axpy -I<CUDA install path>/samples/common/inc -L<CUDA install path>/<lib64 or lib> -lcudart_static -lcuda -ldl -lrt -pthread
$ ./axpy
y[0] = 2
y[1] = 4
y[2] = 6
y[3] = 8

Note that helper_cuda.h comes from the CUDA samples, so you need the samples installed for this example. <CUDA install path> is the root directory where you installed CUDA SDK, typically /usr/local/cuda.


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. Details can be found in the design document for memory space inference.
  • 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.