hipRAND is a RAND marshalling library, with multiple supported backends. It sits between the application and the backend RAND library, marshalling inputs into the backend and results back to the application. hipRAND exports an interface that does not require the client to change, regardless of the chosen backend. Currently, hipRAND supports either rocRAND or cuRAND.
Information about the library API and other topics can be found in the hipRAND Documentation.
Run the steps below to build documentation locally.
cd docs
pip3 install -r .sphinx/requirements.txt
python3 -m sphinx -T -E -b html -d _build/doctrees -D language=en . _build/html
cd ../python/hiprand
python setup.py build_sphinx
Download pre-built packages from ROCm's package servers, or by clicking the github releases tab and manually downloading, which could be newer. Release notes are available for each release on the releases tab.
sudo apt update && sudo apt install hiprand
- CMake (3.16 or later)
- For AMD GPUs:
- AMD ROCm platform (5.0.0 or later)
- rocRAND library
- For NVIDIA GPUs:
- CUDA Toolkit
- cuRAND library
The root of this repository has a helper bash script install
to build and install hipRAND on Ubuntu with a single command. It does not take a lot of options and hard-codes configuration that can be specified through invoking cmake directly, but it's a great way to get started quickly and can serve as an example of how to build/install. A few commands in the script need sudo access, so it may prompt you for a password.
./install -h
-- shows help./install -id
-- build library, build dependencies and install (-d flag only needs to be passed once on a system)
If you use a distro other than Ubuntu, or would like more control over the build process, the hipRAND build wiki has helpful information on how to configure cmake and manually build.
A list of exported functions from hipRAND can be found on the wiki
The hipRAND interface is compatible with rocRAND and cuRAND-v2 APIs. Porting a CUDA application which originally calls the cuRAND API to an application calling hipRAND API should be relatively straightforward. For example, creating a generator is
hiprandStatus_t
hiprandCreateGenerator(
hiprandGenerator_t* generator,
hiprandRngType_t rng_type
)
Here is one example of generating a log-normally distributed float from a generator (these functions are templated for all generators).
__device__ double
hiprand_log_normal_double(
hiprandStateSobol64_t* state,
double mean,
double stddev
)