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Update tutorial to align with the new Aerial release (25-3)

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Reem Bahsoun
2026-01-30 12:13:01 +01:00
parent de0d100d67
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doc/Aerial_FAPI_Split_Tutorial.md
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@@ -1,4 +1,4 @@
# OAI 7.2 Fronthaul Interface 5G SA Tutorial
# OAI - Aerial FAPI Split Tutorial
**Table of Contents**
@@ -14,9 +14,12 @@ The hardware on which we have tried this tutorial:
| Dell PowerEdge R750 (Dual Intel Xeon Gold 6336Y CPU @ 2.4G, 24C/48T (185W), 512GB RDIMM, 3200MT/s) |Ubuntu 22.04.3 LTS (5.15.0-72-lowlatency)| NVIDIA Converged Accelerator A100X (24.39.2048) |
| Supermicro Grace Hopper MGX ARS-111GL-NHR (Neoverse-V2, 3.4GHz, 72C/72T, 576GB LPDDR5) | Ubuntu 22.04.5 LTS (6.5.0-1019-nvidia-64k) |NVIDIA BlueField3 (32.41.1000)|
**NOTE**:
- These are not minimum hardware requirements. This is the configuration of our servers. The NIC card should support hardware PTP time stamping.
- Starting from tag [2025.w13](https://gitlab.eurecom.fr/oai/openairinterface5g/-/tree/2025.w13?ref_type=tags) of OAI, we are only testing with the Grace Hopper server.
**Note**:
- These are not minimum hardware requirements. This is the configuration of our
servers. The NIC card should support hardware PTP time stamping.
- Starting from tag
[2025.w13](https://gitlab.eurecom.fr/oai/openairinterface5g/-/tree/2025.w13?ref_type=tags)
of OAI, we are only testing with the Grace Hopper server.
PTP enabled switches and grandmaster clock we have tested with:
@@ -54,10 +57,10 @@ The UEs that have been tested and confirmed working with Aerial are the followin
To set up the L1 and install the components manually refer to this [instructions page](https://docs.nvidia.com/aerial/cuda-accelerated-ran/index.html).
**Note**:
- As of wk36, the L1 must be compiled with the following CMake flag: `-DSCF_FAPI_10_04_SRS=ON` , this is due to the usage
of the FAPI 10.04 version of the SRS PDU, and RX_Beamforming PDU.
- To configure the Gigabyte server please refer to these [instructions](https://gitlab.eurecom.fr/oai/openairinterface5g/-/blob/2025.w13/doc/Aerial_FAPI_Split_Tutorial.md)
- The last release to support the Gigabyte server is **Aerial CUDA-Accelerated RAN 24-1**.
- To configure the Gigabyte server please refer to these
[instructions](https://gitlab.eurecom.fr/oai/openairinterface5g/-/blob/2025.w13/doc/Aerial_FAPI_Split_Tutorial.md)
- The last release to support the Gigabyte server is **Aerial CUDA-Accelerated
RAN 24-1**.
#### CPU allocation
@@ -67,21 +70,43 @@ of the FAPI 10.04 version of the SRS PDU, and RX_Beamforming PDU.
**Grace Hopper MGX ARS-111GL-NHR**
| Applicative Threads | Allocated CPUs |
|------------------------|----------------|
| PTP & PHC2SYS Services | 41 |
| OAI `nr-softmodem` | 13-16 |
| Applicative Threads | Allocated CPUs |
|--------------------------------------|--------------------------------|
| `workers_ul` | 4, 5 |
| `workers_dl` | 6, 7, 8 |
| `timer_thread_config` | 9 |
| `message_thread_config` | 9 |
| `pcap_shm_caching_cpu_core` | 10 |
| `pcap_file_saving_cpu_core` | 10 |
| `dpdk_thread` | 10 |
| `ul_pcap_capture_thread_cpu_affinity`| 10 |
| `pcap_logger_thread_cpu_affinity` | 10 |
| `h2d_copy_thread_cpu_affinity` | 11 |
| `fh_stats_dump_cpu_core` | -1 |
| `pdump_client_thread` | -1 |
| `debug_worker` | -1 |
| `prometheus_thread` | -1 |
| | |
| OAI `nr-softmodem` | 13,14,15,16 |
### PTP configuration
**Note**:
- `-1` indicates that no explicit CPU pinning is applied and the thread
is scheduled by the Linux kernel.
- core 10 is a `low_priority_core` and it is shared by all low-priority threads.
1. You need to install the `linuxptp` debian package. It will install both ptp4l and phc2sys.
#### PTP configuration
1. Install the `linuxptp` debian package. It will install both
ptp4l and phc2sys.
```bash
#Ubuntu
sudo apt install linuxptp -y
```
Once installed you can use this configuration file for ptp4l (`/etc/ptp4l.conf`). Here the clock domain is 24 so you can adjust it according to your PTP GM clock domain
Once installed you can use this configuration file for ptp4l
(`/etc/ptp4l.conf`). Here the clock domain is 24 so you can adjust it according
to your PTP GM clock domain
```
[global]
@@ -94,25 +119,27 @@ tx_timestamp_timeout 30
network_transport L2
```
The service of ptp4l (`/lib/systemd/system/ptp4l.service`) should be configured
as below:
The service of ptp4l (`/lib/systemd/system/ptp4l.service`) should be configured as below:
```
[Unit]
Description=Precision Time Protocol (PTP) service
[Unit] Description=Precision Time Protocol (PTP) service
Documentation=man:ptp4l
[Service]
Restart=always
RestartSec=5s
Type=simple
ExecStart=taskset -c 41 /usr/sbin/ptp4l -f /etc/ptp.conf
ExecStart=/usr/sbin/ptp4l -f /etc/ptp.conf
[Install]
WantedBy=multi-user.target
```
and service of phc2sys (`/lib/systemd/system/phc2sys.service`) should be configured as below:
and service of phc2sys (`/lib/systemd/system/phc2sys.service`) should be
configured as below:
```
[Unit]
Description=Synchronize system clock or PTP hardware clock (PHC)
@@ -131,10 +158,25 @@ ExecStart=/usr/sbin/phc2sys -a -r -n 24
WantedBy=multi-user.target
```
**Note**: As of release 25-3 there's no need to allocate a core for PTP.
## Prepare the L1
- Follow these
[instructions](https://github.com/NVIDIA/aerial-cuda-accelerated-ran) to
clone the `aerial-cuda-accelerated-ran` repository, pull and build the cuBB
image.
- The CMake flags we use to compile the L1 are:
- `-DSCF_FAPI_10_04_SRS=ON` this flag must be used as of OAI tag `2025.w36`
and this is due to the usage of the FAPI 10.04 version of the SRS PDU, and
RX_Beamforming PDU.
- `-DENABLE_CONFORMANCE_TM_PDSCH_PDCCH=OFF` this option should only be
enabled when doing conformance testing with testmac.
## Build OAI gNB
If it's not already cloned, the first step is to clone OAI repository
```bash
git clone https://gitlab.eurecom.fr/oai/openairinterface5g.git ~/openairinterface5g
cd ~/openairinterface5g/
@@ -142,65 +184,42 @@ cd ~/openairinterface5g/
### Get nvIPC sources from the L1 container
The library used for communication between L1 and L2 components is called nvIPC, and is developed by NVIDIA. It is not open-source and can't be freely distributed.
In order to achieve this communication, we need to obtain the nvIPC source files from the L1 container (cuBB) and place it in the gNB project directory `~/openairinterface5g`.
This allows us to build and install this library when building the L2 docker container.
Check whether your L1 container is running:
```bash
~$ docker container ls -a
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
a9681a0c4a10 14dca2002237 "/opt/nvidia/nvidia_…" 3 months ago Exited (137) 10 days ago cuBB
```
If it is not running, you may start it and logging into the container by running the following:
```bash
~$ docker start cuBB
cuBB
~$ docker exec -it cuBB bash
aerial@c_aerial_aerial:/opt/nvidia/cuBB#
```
After logging into the container, you need to pack the nvIPC sources and copy them to the host ( the command creates a `tar.gz` file with the following name format: `nvipc_src.YYYY.MM.DD.tar.gz`)
```bash
~$ docker exec -it cuBB bash
aerial@c_aerial_aerial:/opt/nvidia/cuBB# cd cuPHY-CP/gt_common_libs
aerial@c_aerial_aerial:/opt/nvidia/cuBB/cuPHY-CP/gt_common_libs#./pack_nvipc.sh
nvipc_src.YYYY.MM.DD/
...
---------------------------------------------
Pack nvipc source code finished:
/opt/nvidia/cuBB/cuPHY-CP/gt_common_libs/nvipc_src.YYYY.MM.DD.tar.gz
aerial@c_aerial_aerial:/opt/nvidia/cuBB/cuPHY-CP/gt_common_libs# sudo cp nvipc_src.yyyy.mm.dd.tar.gz /opt/cuBB/share/
aerial@c_aerial_aerial:/opt/nvidia/cuBB/cuPHY-CP/gt_common_libs# exit
```
The file should now be present in the `~/openairinterface5g/ci-scripts/yaml_files/sa_gnb_aerial/` directory, from where it is moved into `~/openairinterface5g`
```bash
~$ mv ~/openairinterface5g/ci-scripts/yaml_files/sa_gnb_aerial/nvipc_src.YYYY.MM.DD.tar.gz ~/openairinterface5g/
```
Alternatively, after running `./pack_nvipc.sh` , you can exit the container and copy `nvipc_src.YYYY.MM.DD.tar.gz` using `docker cp`
The library used for communication between L1 and L2 components is called
nvIPC, and is developed by NVIDIA. In order to achieve this communication, we
need to obtain the nvIPC source files from the L1 container (cuBB) and place it
in the gNB project directory `~/openairinterface5g`. This allows us to build
and install this library when building the L2 docker image.
```bash
~$ docker exec -it cuBB bash
aerial@c_aerial_aerial:/opt/nvidia/cuBB# cd cuPHY-CP/gt_common_libs
aerial@c_aerial_aerial:/opt/nvidia/cuBB/cuPHY-CP/gt_common_libs#./pack_nvipc.sh
nvipc_src.YYYY.MM.DD/
...
---------------------------------------------
Pack nvipc source code finished:
/opt/nvidia/cuBB/cuPHY-CP/gt_common_libs/nvipc_src.YYYY.MM.DD.tar.gz
aerial@c_aerial_aerial:/opt/nvidia/cuBB/cuPHY-CP/gt_common_libs# exit
~$ docker cp nv-cubb:/opt/nvidia/cuBB/cuPHY-CP/gt_common_libs/nvipc_src.YYYY.MM.DD.tar.gz ~/openairinterface5g
# Start interactive development container
./cuPHY-CP/container/run_aerial.sh
# Pack the nvIPC sources and copy them to the host ( the command creates a `tar.gz` file with the following name format: `nvipc_src.YYYY.MM.DD.tar.gz`)
aerial@c_aerial_oaicicd:/opt/nvidia/cuBB# cd cuPHY-CP/gt_common_libs
aerial@c_aerial_oaicicd:/opt/nvidia/cuBB/cuPHY-CP/gt_common_libs#./pack_nvipc.sh
nvipc_src.YYYY.MM.DD/ ... --------------------------------------------- Pack
nvipc source code finished:/opt/nvidia/cuBB/cuPHY-CP/gt_common_libs/nvipc_src.YYYY.MM.DD.tar.gz
```
*Important note:* For using docker cp, make sure to copy the entire name of the created nvipc_src tar.gz file.
Keep the interactive development container open and in another terminal use
`docker cp` to copy the library
```bash
docker cp c_aerial_oaicicd:/opt/nvidia/cuBB/cuPHY-CP/gt_common_libs/nvipc_src.YYYY.MM.DD.tar.gz ~/openairinterface/
```
*Important note:* For using docker cp, make sure to
copy the entire name of the created nvipc_src tar.gz file.
You can now exit the interactive development container.
With the nvIPC sources in the project directory, the L2 docker image can be built.
With the nvIPC sources in the project directory, the docker image can be built.
### Building OAI gNB docker image
In order to build the target image (`oai-gnb-aerial`), first you should build a common shared image (`ran-base`)
In order to build the target image (`oai-gnb-aerial`), first you should build a
common shared image (`ran-base`)
```bash
~$ cd ~/openairinterface5g/
~/openairinterface5g$ docker build . -f docker/Dockerfile.base.ubuntu --tag ran-base:latest
@@ -210,52 +229,113 @@ In order to build the target image (`oai-gnb-aerial`), first you should build a
## Running the setup
In order to use Docker Compose to automatically start and stop the setup, we first need to create a pre-built image of L1 after compiling the L1 software and making the necessary adjustments to its configuration files.
The process of preparing L1 is covered in NVIDIA's documentation and falls outside the scope of this document.
### Prepare the L1 image
After preparing the L1 software, the container needs to be committed to create an image where L1 is ready for execution, which can later be referenced by a `docker-compose.yaml` file.
*Note:* The default L1 configuration file is `cuphycontroller_P5G_FXN_GH.yaml`, located in `/opt/nvidia/cuBB/cuPHY-CP/cuphycontroller/config/`.
In this file the RU MAC address needs to be specified before commiting the image.
```bash
~$ docker commit nv-cubb cubb-build:25-2.1
~$ docker image ls
..
cubb-build 25-2.1 824156e0334c 2 weeks ago 23.9GB
-..
```
### Adapt the OAI-gNB configuration file to your system/workspace
Edit the sample OAI gNB configuration file and check following parameters:
Edit the [OAI gNB configuration file](https://gitlab.eurecom.fr/oai/openairinterface5g/-/blob/develop/ci-scripts/conf_files/gnb-vnf.sa.band78.273prb.aerial.conf?ref_type=heads)
and check the following parameters:
* `gNBs` section
* The PLMN section shall match the one defined in the AMF
* To calculate the `absoluteFrequencySSB` and `dl_absoluteFrequencyPointA`,
please follow these
[instructions](https://gitlab.eurecom.fr/oai/openairinterface5g/-/blob/develop/doc/gNB_frequency_setup.md)
* `amf_ip_address` shall be the correct AMF IP address in your system
* `GNB_IPV4_ADDRESS_FOR_NG_AMF` shall match your DU N2 interface IP address
* `GNB_IPV4_ADDRESS_FOR_NGU` shall match your DU N3 interface IP address
The default amf_ip_address:ipv4 value is 192.168.70.132, when installing the CN5G following [this tutorial](https://gitlab.eurecom.fr/oai/openairinterface5g/-/blob/develop/doc/NR_SA_Tutorial_OAI_CN5G.md)
Both 'GNB_IPV4_ADDRESS_FOR_NG_AMF' and 'GNB_IPV4_ADDRESS_FOR_NGU' need to be set to the IP address of the NIC referenced previously.
The default amf_ip_address:ipv4 value is 192.168.70.132, when installing the
CN5G following [this
tutorial](https://gitlab.eurecom.fr/oai/openairinterface5g/-/blob/develop/doc/NR_SA_Tutorial_OAI_CN5G.md)
Both `GNB_IPV4_ADDRESS_FOR_NG_AMF` and `GNB_IPV4_ADDRESS_FOR_NGU` need to be
set to the IP address of the NIC referenced previously.
**Note**: If the Core Network is running on the same server, 3 cores should be
allocated to it. 2 for the UPF and 1 for the all the remaining services as shown
below.
### Running docker compose
#### Aerial L1 entrypoint script
The `aerial_l1_entrypoint` script is used by the L1 container to start the L1 software and is called in the Docker Compose file.
It begins by setting up environment variables, restarting NVIDIA MPS, and finally running `cuphycontroller_scf`.
```patch
diff --git a/docker-compose/docker-compose-basic-nrf.yaml b/docker-compose/docker-compose-basic-nrf.yaml
index 7fbefc9..d5ef964 100644
--- a/docker-compose/docker-compose-basic-nrf.yaml
+++ b/docker-compose/docker-compose-basic-nrf.yaml
@@ -28,6 +28,7 @@ services:
- 8080/tcp
volumes:
- ./conf/basic_nrf_config.yaml:/openair-udr/etc/config.yaml
+ cpuset: "15"
environment:
- TZ=Europe/Paris
depends_on:
```
The L1 software is executed with an argument that specifies which configuration file to use. If not modified, the default argument is set to `P5G_FXN_GH`.
### Aerial L1 entrypoint script
After building the gNB image, and preparing the configuration file, the setup can be run with the following command:
The `aerial_l1_entrypoint` script is used by the L1 container to start the L1
software and is mounted in the Docker Compose file. It begins by setting up
environment variables, restarting NVIDIA MPS, and finally running
`cuphycontroller_scf`.
The L1 software is executed with an argument that specifies which configuration
file to use. If not modified, the default argument is set to `P5G_WNC_GH`.
### Aerial L1 configuration
```bash
cd ~/aerial-cuda-accelerated-ran/cuPHY-CP/cuphycontroller/config/
```
Choose the configuration file that matches your setup and make the necessary
changes. We currently use `cuphycontroller_P5G_WNC_GH.yaml`.
Make sure that the following parameters are modified:
```
dst_mac_addr:
nic:
vlan:
```
### Docker compose
We recommend the user to apply the patch below for `docker-compose.yaml`.
```patch
diff --git a/ci-scripts/yaml_files/sa_gnb_aerial/docker-compose.yaml b/ci-scripts/yaml_files/sa_gnb_aerial/docker-compose.yaml
index 985fe9a6a3..0774d826ac 100644
--- a/ci-scripts/yaml_files/sa_gnb_aerial/docker-compose.yaml
+++ b/ci-scripts/yaml_files/sa_gnb_aerial/docker-compose.yaml
@@ -15,10 +15,10 @@ services:
stdin_open: true
tty: true
volumes:
+ - ~/aerial-cuda-accelerated-ran:/opt/nvidia/cuBB/
- /lib/modules:/lib/modules
- /dev/hugepages:/dev/hugepages
- /usr/src:/usr/src
- - ./cuphycontroller_P5G_WNC_GH.yaml:/opt/nvidia/cuBB/cuPHY-CP/cuphycontroller/config/cuphycontroller_P5G_WNC_GH.yaml
- ./aerial_l1_entrypoint.sh:/opt/nvidia/cuBB/aerial_l1_entrypoint.sh
- /var/log/aerial:/var/log/aerial
- ../../../cmake_targets/share:/opt/cuBB/share
```
By applying this change, it would easier for the user to modify the L1 code or
configuration files directly from the `aerial-cuda-accelerated-ran` repository.
`ci-scripts/yaml_files/sa_gnb_aerial/docker-compose.yaml` is tailored for our
CI.
We copy the `aerial-cuda-accelerated-ran` repository inside the cuBB
docker image using `docker cp` and commit the changes using `docker commit`.
We mount the `cuphycontroller_P5G_WNC_GH.yaml` in the `nv-cubb` volumes so
that it would override some parameters depending on the setup being run on CI.
After building the gNB image, and preparing the configuration file, the setup
can be run with the following command:
```bash
cd ci-scripts/yaml_files/sa_gnb_aerial/
docker compose up -d
```
This will start both containers, beginning with `nv-cubb`, and `oai-gnb-aerial` will start only after it is ready.
This will start both containers, beginning with `nv-cubb`, and
`oai-gnb-aerial` will start only after it is ready.
The logs can be followed using these commands:
@@ -263,8 +343,13 @@ The logs can be followed using these commands:
docker logs -f oai-gnb-aerial
docker logs -f nv-cubb
```
#### Running with multiple L2s
One L1 instance can support multiple L2 instances. See also the [aerial documentation](https://developer.nvidia.com/docs/gputelecom/aerial-sdk/text/cubb_quickstart/running_cubb-end-to-end.html#run-multiple-l2-instances-with-single-l1-instance) for more details.
One L1 instance can support multiple L2 instances. See also
the [aerial
documentation](https://docs.nvidia.com/aerial/cuda-accelerated-ran/latest/quickstart_guide/running_cubb-end-to-end.html#run-multiple-l2-instances-with-single-l1-instance)
for more details.
In OAI the shared memory prefix must be configured in the configuration file.
@@ -274,11 +359,12 @@ In OAI the shared memory prefix must be configured in the configuration file.
```
### Stopping the setup
#### Stopping the setup
Run the following command to stop and remove both containers, leaving the
system ready to be restarted later:
Run the following command to stop and remove both containers, leaving the system ready to be restarted later:
```bash
cd ci-scripts/yaml_files/sa_gnb_aerial/
docker compose down
```