Current Status

For supporting OpenCL, or hardware level GeneralPurposeGPU computing. We are at the planning stage, and redesigning the interfaces in gallium to support compute, like TGSI. It is expected that we will support AMD (ATI) Evergreen (r800 - HD5xxx), and hopefully Nvidia cards too.

	cpu (llvmpipe)	nv50	nvc0	r700	Evergreen/NI	Cayman	SI
gallium hw interface	N/N	MOSTLY	MOSTLY	3D	MOSTLY	MOSTLY	MOSTLY
handling GPU buffers	TODO	TODO	TODO	TODO	DONE	DONE	DONE
execute TGSI compute shader	TODO	TODO	TODO	TODO	N/A	N/A	N/A
execute LLVM-IR compute shader	TODO	TODO	TODO	TODO	DONE	DONE	DONE
performance profiling	TODO	TODO	TODO	TODO	TODO	TODO	TODO
global address space	TODO	TODO	TODO	TODO	DONE	DONE	DONE
local address space	TODO	TODO	TODO	TODO	TODO	TODO	TODO
private address space	TODO	TODO	TODO	TODO	WIP	WIP	TODO
constant adress space	TODO	TODO	TODO	TODO	WIP	WIP	TODO
local sync	TODO	TODO	TODO	TODO	TODO	TODO	TODO
global sync	TODO	TODO	TODO	N/A	TODO	TODO	TODO
local atomics	TODO	TODO	TODO	N/A	TODO	TODO	TODO
global atomics	TODO	TODO	TODO	N/A	TODO	TODO	TODO
2D image read	TODO	TODO	TODO	TODO	TODO	TODO	TODO
3D image read	TODO	TODO	TODO	N/A	TODO	TODO	TODO
2D image write	TODO	TODO	TODO	TODO	TODO	TODO	TODO
3D image write	TODO	TODO	TODO	N/A	TODO	TODO	TODO
accurate4 arithmetics	TODO	TODO	TODO	TODO	MOSTLY7	WIP	WIP
OpenCL5 1.0	TODO	TODO	TODO	N/A6	WIP	WIP	WIP
OpenCL 1.1	TODO	TODO	TODO	N/A	WIP	WIP	WIP
OpenGL interoperability	TODO	TODO	TODO	TODO	TODO	TODO	TODO

¹ Proof of concept test code for testing and experimenting with hardware compute support

² Hardware specific binary code

⁴ OpenCL defines some level expected accuracy. Some hardware doesn't support it, so we need software emulation

⁵ OpenCL front-end is a separate project, we aim to support all features needed by the front-end to implement the standard

⁶ Only partial OpenCL support is possible, support through vertex shaders.

⁷ Mostly reliable for float and integer types. char, short, long, and double types need more testing.

r600g

Supported Hardware

• All Evergreen and Northern Islands GPU familes are currently supported. If you are unsure what family your GPU is, you can use this chart: http://www.x.o/wiki/RadeonFeature#Decoder_ring_for_engineering_vs_marketing_names to figure it out.
• There are still a few bugs on Cayman GPUs, that may prevent some compute features from working as well as they do on pre-Cayman GPUS.

Supported Linux Kernel Versions

• r600g compute is known to work with stable Linux Kernel versions >= 3.1. Versions older than 3.1 may work, but have not been tested.

How to Install

Getting the source code

• Current Development version:

  • LLVM / Clang:

      git clone http://llvm.org/git/llvm.git
      cd llvm/tools
      git clone http://llvm.org/git/clang.git
    

  • libclc:

      git clone git://people.freedesktop.org/~tstellar/libclc
    

  • Mesa:

      git clone git://anongit.freedesktop.org/mesa/mesa
    

• Stable Version (NOTE: Some distros provide packages for the stable versions of Mesa (9.1) and LLVM (3.2) that include compute support for r600g):

  • LLVM / Clang:

      git clone git://people.freedesktop.org/~tstellar/llvm
      cd llvm/tools
      wget http://llvm.org/releases/3.2/clang-3.2.src.tar.gz
      tar -xzf clang-3.2.src.targ.gz
      mv clang-3.2-src clang
    

  • libclc:

      git clone git://people.freedesktop.org/~tstellar/libclc
      git checkout llvm-3.2
    

  • Mesa:

      wget ftp://ftp.freedesktop.org/pub/mesa/9.1.1/MesaLib-9.1.1.tar.gz
      tar -xzf MesaLib-9.1.1.tar.gz
    

Building

1. LLVM / Clang:

    cd llvm/
    ./configure --enable-experimental-targets=R600 --enable-targets=x86 --enable-shared
    make -j3 && make install
   

   • LLVM builds all supported targets by default, so adding --enable-targets=x86 will speed up the build time. NOTE: Mesa requires the X86 target to be built.
   • LLVM will try to use clang as its compiler by default. This may not work for you in all cases (e.g. you have an old version of clang installed). You can force llvm to use gcc by appending CC=gcc CXX=g++ to the configure arguments.

2. libclc:

    git clone git://people.freedesktop.org/~tstellar/libclc
    cd libclc/
    ./configure.py
    make
    make install
   

   • When buiding libclc you may see this warning: is not a recognized processor for this target (ignoring processor) Don't worry, this warning is harmless.

3. Mesa:

    cd mesa/
    ./autogen.sh --with-dri-drivers="" --with-gallium-drivers=r600 --enable-opencl
    make -j3
    make install
   

Testing

• OpenCL examples that mostly work with clover and r600g can be found here.
• Piglit: Use the all_cl.tests test profile.

Troubleshooting

• If see this error message: radeon: Failed to get PCI ID, error number -13, make sure you have permissions to access the device (usually /dev/dri/card0), and get the latest version of mesa from git. Prior to this commit: 044de40cb0c6af54d99252f55145972780362afa, you would have seen this error message when running compute programs and X at the same time.
• If you get the error message "cannot find stddef.h" when you try to run a compute program, this means that clang can't find its builtin include files. The solution for this is to make sure that clang and llvm are both installed to the same $(LIBDIR). Clover expects the clang builtin includes to be in $(LLVM_LIBDIR)/clang/$(LLVM_VERSION)/

Todo

R600ToDo#Compute