website: Update GPU model page to remove GCN3 references (#87)

This change is part of a series of changes across repos to move
exclusively to Vega (gem5 discussion #306). This is intended to be
released whenever that change happens. Tentatively using v23.1.

- Removes GCN3 references and replaces with generic "AMD GPU"
- Change gcr.io to ghcr.io
- Change gcn-gpu docker tag to v23-1
- Added mentions of full system mode support
- Checked that all the links still work

_data/documentation.yml

@@ -187,8 +187,8 @@ docs:
     - title: GPU Models
       id: gpu_models
       subitems:
-        - page: GCN3
-          url: /documentation/general_docs/gpu_models/GCN3
+        - page: VEGA
+          url: /documentation/general_docs/gpu_models/vega
 
     - title: M5ops
       id: m5ops

_pages/documentation/general_docs/gpu_models/gcn3.md → _pages/documentation/general_docs/gpu_models/vega.md

@@ -1,44 +1,44 @@
 ---
 layout: documentation
-title: GCN3 GPU model
+title: AMD VEGA GPU model
 doc: gem5 documentation
 parent: gpu_models
-permalink: /documentation/general_docs/gpu_models/GCN3
+permalink: /documentation/general_docs/gpu_models/vega
 ---
 
-# **GCN3**
+# **AMD VEGA GPU model**
 
 Table of Contents
 
 1. [Using the model](#Using-the-model)
 2. [ROCm](#ROCm)
 3. [Documentation and Tutorials](#Documentation-and-Tutorials)
 
-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.
+The AMD VEGA GPU is a model that simulates a GPU at the VEGA 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.
 
 ## **Using the model**
 
-Currently, the GCN3 GPU model in gem5 is supported on the stable and develop branch.
+Currently, the AMD VEGA GPU model in gem5 is supported on the stable and develop branch.
 
-The [gem5 repository](https://github.com/gem5/gem5) 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 `gcr.io/gem5-test/gcn-gpu:v22-0`.
+The [gem5 repository](https://github.com/gem5/gem5) 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 `ghcr.io/gem5-test/gcn-gpu:v23-1`.
 
 The [gem5-resources repository](https://github.com/gem5/gem5-resources/) also comes with a number of sample applications that can be used to verify that the model runs correctly.  We recommend users start with [square](https://resources.gem5.org/resources/square), as it is a simple, heavily tested application that should run relatively quickly.
 
 #### Using the image
-The docker image can either be built or pulled from gcr.io.
+The docker image can either be built or pulled from ghcr.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 (Note the `v22-0` tag, to get the correct
+To pull the pre-built docker image (Note the `v23-1` tag, to get the correct
 image for this release):
 ```
-docker pull gcr.io/gem5-test/gcn-gpu:v22-0
+docker pull ghcr.io/gem5-test/gcn-gpu:v23-1
 ```
-You can also put `gcr.io/gem5-test/gcn-gpu:v22-0` as the image in the docker run command without pulling beforehand and it will be pulled automatically.
+You can also put `ghcr.io/gem5-test/gcn-gpu:v23-1` as the image in the docker run command without pulling beforehand and it will be pulled automatically.
 
 #### Building gem5 using the image
 See square in [gem5 resources](https://github.com/gem5/gem5-resources/tree/stable/src/gpu/square/) for an example of how to build gem5 in the docker.  Note: these directions assume you are pulling the latest image automatically.
@@ -48,10 +48,12 @@ See [gem5 resources](https://github.com/gem5/gem5-resources/tree/stable/src/gpu/
 
 ## **ROCm**
 
-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)](http://www.hsafoundation.com/) 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](https://rocmdocs.amd.com/en/latest/)
+The AMD VEGA GPU model was designed with enough fidelity to not require an emulated runtime. Instead, the model uses the Radeon Open Compute platform (ROCm). ROCm is an open platform from AMD that implements [Heterogeneous Systems Architecture (HSA)](http://www.hsafoundation.com/) 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](https://rocmdocs.amd.com/en/latest/)
 
 #### Simulation support for ROCm
-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 model currently works with system-call emulation (SE) mode and full-system (FS) mode.
+
+In SE mode, 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: `src/gpu-compute/gpu_compute_driver.[hh|cc]`
 
@@ -63,10 +65,12 @@ The `GPUComputeDriver` derives from `HSADriver` and is a device-specific impleme
 
 The `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.
 
+In FS mode, the real amdgpu Linux driver is used and installed as you would on a real machine. The source for the driver can instead be found in the [ROCK-Kernel-Driver](https://github.com/RadeonOpenCompute/ROCK-Kernel-Driver) repository.
+
 #### ROCm toolchain and software stack
-The GCN3 model supports ROCm version 4.0.
+The AMD VEGA GPU model supports ROCm versions up to 5.4 in FS mode and 4.0 in SE mode.
 
-The following ROCm components are required:
+The following ROCm components are required in SE mode:
 * [Heterogeneous Compute Compiler (HCC)](https://github.com/RadeonOpenCompute/hcc)
 * [Radeon Open Compute runtime (ROCr)](https://github.com/RadeonOpenCompute/ROCR-Runtime)
 * [Radeon Open Compute thunk (ROCt)](https://github.com/RadeonOpenCompute/ROCT-Thunk-Interface)
@@ -77,22 +81,26 @@ The following additional components are used to build and run machine learning p
 * [rocBLAS](https://github.com/ROCmSoftwarePlatform/rocBLAS/)
 * [MIOpen](https://github.com/ROCmSoftwarePlatform/MIOpen/)
 * [rocm-cmake](https://github.com/RadeonOpenCompute/rocm-cmake/)
+* [PyTorch](https://pytorch.org/) (FS mode only)
+* [Tensorflow](https://www.tensorflow.org/) - specifically the tensorflow-rocm python package (FS mode only)
 
 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.
 
 ## **Documentation and Tutorials**
 
-#### GCN3 Model
-Describes the GCN3 model
+Note that the VEGA ISA is a newer, superset ISA derived from GCN3. Therefore, the contents of the following papers, tutorials, and documentation apply to VEGA as well.
+
+#### GPU Model
+Describes the gem5 GPU model with the GCN3 ISA (at the time of writing). VEGA is a newer, superset ISA derived from GCN3. Therefore the contents of the following papers)
 * [HPCA 2018](https://ieeexplore.ieee.org/document/8327041)
 
 #### gem5 GCN3 ISCA tutorial
 Covers information about the GPU architecture, GCN3 ISA and HW-SW interfaces in gem5. Also provides an introduction to ROCm.
 * [gem5 GCN3 ISCA webpage](http://www.gem5.org/events/isca-2018)
 * [gem5 GCN3 ISCA slides](http://old.gem5.org/wiki/images/1/19/AMD_gem5_APU_simulator_isca_2018_gem5_wiki.pdf)
 
-#### GCN3 ISA
-* [GCN3 ISA](https://gpuopen.com/documentation/amd-isa-documentation/)
+#### VEGA ISA
+* [VEGA ISA](https://gpuopen.com/documentation/amd-isa-documentation/)
 
 #### ROCm Documentation
 Contains further documentation about the ROCm stack, as well as programming guides for using ROCm.