Table of Contents
The GCN3 GPU is a model that simulates a GPU at the ISA level, as opposed to the intermediate language level. This page will give you a general overview of how to use this model, the software stack the model uses, and provide resources that detail the model and how it is implemented.
Currently, the GCN3 GPU model in gem5 is only supported on the develop branch. Thus, you should use the develop branch when running the GCN3 model. We expect this support to be integrated into the gem5-v21.0 release.
The gem5 repository comes with a dockerfile located in
util/dockerfiles/gcn-gpu/. This dockerfile contains the drivers and libraries needed to run the GPU model. A pre-built version of the docker image is hosted at
The gem5-resources repository also comes with a sample application (square) that can be used to verify that the model runs correctly.
The docker image can either be built or pulled from gcr.io
To build the docker image from source:
# Working directory: gem5/util/dockerfiles/gcn-gpu docker build -t <image_name> .
To pull the pre-built docker image:
docker pull gcr.io/gem5-test/gcn-gpu
You can also put
gcr.io/gem5-test/gcn-gpu as the image in the docker run command without pulling beforehand and it will be pulled automatically.
The following command assumes the gem5 directory is a subdirectory of your current directory
docker run --rm -v $PWD/gem5:/gem5 -w /gem5 <image_name> scons -sQ -j$(nproc) build/GCN3_X86/gem5.opt
The following command assumes the gem5-resources directory is a subdirectory of your current directory
docker run --rm -v $PWD/gem5-resources:$PWD/gem5-resources -w $PWD/gem5-resources/src/square <image_name> make gfx8-apu
The following command assumes that gem5 and gem5-resources are subdirectories of your current directory
docker run --rm -v $PWD/gem5:/gem5 -v $PWD/gem5-resources:/gem5-resources \ -w /gem5 <image_name> \ build/GCN3_X86/gem5.opt configs/example/apu_se.py -n2 \ --benchmark-root=/gem5-resources/src/square/bin \ -c square.o
The GCN3 model was designed with enough fidelity to not require an emulated runtime. Instead, the GCN3 model uses the Radeon Open Compute platform (ROCm). ROCm is an open platform from AMD that implements Heterogeneous Systems Architecture (HSA) principles. More information about the HSA standard can be found on the HSA Foundation's website. More information about ROCm can be found on the ROCm website
The model currently only works with system-call emulation (SE) mode, therefore all kernel level driver functionality is modeled entirely within the SE mode layer of gem5. In particular, the emulated GPU driver supports the necessary
ioctl() commands it receives from the userspace code. The source for the emulated GPU driver can be found in:
The GPU compute driver:
The HSA device driver:
The HSA driver code models the basic functionality for an HSA agent, which is any device that can be targeted by the HSA runtime and accepts Architected Query Language (AQL) packets. AQL packets are a standard format for all HSA agents, and are used primarily to initiate kernel launches on the GPU. The base
HSADriver class holds a pointer to the HSA packet processor for the device, and defines the interface for any HSA device. An HSA agent does not have to be a GPU, it could be a generic accelerator, CPU, NIC, etc.
GPUComputeDriver derives from
HSADriver and is a device-specific implementation of an
HSADriver. It provides the implementation for GPU-specific
src/dev/hsa/kfd_ioctl.h header must match the
kfd_ioctl.h header that comes with ROCt. The emulated driver relies on that file to interpret the
ioctl() codes the thunk uses.
The GCN3 model supports ROCm version 1.6
The following ROCm components are required:
The following additional components are used to build and run machine learning programs:
For information about installing these components locally, the commands in the GCN3 dockerfile (
util/dockerfiles/gcn-gpu/) can be followed on an Ubuntu 16 machine.
Describes the GCN3 model
Covers information about the GPU architecture, GCN3 ISA and HW-SW interfaces in gem5. Also provides an introduction to ROCm.
Contains further documentation about the ROCm stack, as well as programming guides for using ROCm.