RPCS3/llvm-mirror
1
0
Fork 0
mirror of https://github.com/RPCS3/llvm-mirror.git synced 2024-11-22 10:42:39 +01:00
Code Issues Projects Releases Wiki Activity

[doc] improve the doc for CUDA

Browse Source 
1. Mentioned that CUDA support works best with trunk.
2. Simplified the example by removing its dependency on the CUDA samples.
3. Explain the --cuda-gpu-arch flag.

llvm-svn: 259307
This commit is contained in:
Jingyue Wu 2016-01-30 23:48:47 +00:00
parent 2f77371cea
commit 8437a2db93
1 changed files with 21 additions and 17 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

38
docs/CompileCudaWithLLVM.rst
View File

@ -18,9 +18,11 @@ familiarity with CUDA. Information about CUDA programming can be found in the
How to Build LLVM with CUDA Support
===================================
Below is a quick summary of downloading and building LLVM. Consult the `Getting
Started <http://llvm.org/docs/GettingStarted.html>`_ page for more details on
setting up LLVM.
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
<http://llvm.org/docs/GettingStarted.html>`_ page for more details on setting
up LLVM.
#. Checkout LLVM
@ -60,8 +62,6 @@ which multiplies a ``float`` array by a ``float`` scalar (AXPY).
.. code-block:: c++
#include <helper_cuda.h> // for checkCudaErrors
#include <iostream>
__global__ void axpy(float a, float* x, float* y) {
@ -78,25 +78,25 @@ which multiplies a ``float`` array by a ``float`` scalar (AXPY).
// 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),
cudaMemcpyHostToDevice));
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.
checkCudaErrors(cudaDeviceSynchronize());
checkCudaErrors(cudaMemcpy(host_y, device_y, kDataLen * sizeof(float),
cudaMemcpyDeviceToHost));
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";
}
checkCudaErrors(cudaDeviceReset());
cudaDeviceReset();
return 0;
}
@ -104,16 +104,20 @@ The command line for compilation is similar to what you would use for C++.
.. code-block:: console
$ clang++ -o axpy -I<CUDA install path>/samples/common/inc -L<CUDA install path>/<lib64 or lib> axpy.cu -lcudart_static -lcuda -ldl -lrt -pthread
$ 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
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``.
``<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 <https://developer.nvidia.com/cuda-gpus>`_. 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``.
Optimizations
=============