gpuocelot

NOTE: this fork is not actively maintained anymore.

About

This is a fork of the gpuocelot code to make it work on a modern system, in combination with the CUDA library for Julia.

As far as the functionality and tests in CUDA.jl go, this is a drop-in replacement for the official CUDA driver.

Short overview of changes compared to upstream:

• Fix compilation on a modern system (yyparse hack)
• Support for Clang
• Add/extend/fix certain API calls (cuLaunchKernel, cuCtxDestroy, cuLaunch, cuDeviceGetName)
• Small bugfixes

Not that this fork happened before gpuocelot was available on GitHub, so there might be some differences between both code bases.

Requirements

• Scons
• CUDA toolkit <= 5.0 (you only need the toolkit, not the actual driver)
• Appropriate GCC for the CUDA toolkit you picked
• LLVM 3.5
• Boost

Compilation

Make sure you do a recursive check-out, so the hydrazine submodule is checked out too.

Example compilation:

cd $(CHECKOUT_DIR)
CUDA_BIN_PATH=/opt/cuda-5.0/bin CUDA_LIB_PATH=/opt/cuda-5.0/lib CUDA_INC_PATH=/opt/cuda-5.0/include \
CC=clang CXX=clang++ python2 build.py \
    --install -p $(PREFIX) -j$(JOBS)

Note: if your main gcc binary is not compatible with your CUDA toolkit version (for example, CUDA 5.0 requires gcc <= 4.6), you will need to edit ocelot/scripts/build_environment.py and change the two nvcc invocations to include -ccbin=gcc-4.6 (or something similar).

Note: due to restrictions of the build system, make sure you only use absolute paths when passing information through environment variables, and always build with --install.

Note: LLVM needs to be at version 3.5. If your distribution provides some other version, install and compile LLVM from source and point the build system to that installation's llvm-config binary by setting the LLVM_CONFIG environment variable before invoking build.py.

Usage

Compile and install gpuocelot into a directory you can load libraries from. Next, you either rename libocelot.so to libcuda.so, or you use something which knows about gpuocelot (like CUDA.jl does). After that, you can use the available symbols just as it were the official NVIDIA implementation.