Running the cuPHY SDK

Aerial SDK 23-4

The SDK provides the cuPHY library and several examples that link with the library.

Prerequisites

The following instructions assume the system configuration and cuBB SDK installation are done. If not, see the cuBB Install Guide to complete the installation or upgrade process.

After powering on the system, use the following commands to verify that the GPU and NIC are in the correct state:

Copy
Copied!
            

# Verify GPU is detected and CUDA driver version matches the release manifest. $ nvidia-smi

Verify that the NIC is in the correct state on the host (this is only required to run cuBB end-to-end):

Copy
Copied!
            

# Verify CX6-DX NIC is detected. $ sudo lshw -c network -businfo Bus info Device Class Description ======================================================= pci@0000:05:00.0 eno1 network I210 Gigabit Network Connection pci@0000:06:00.0 enp6s0 network I210 Gigabit Network Connection pci@0000:b5:00.0 ens6f0 network MT2892 Family [ConnectX-6 Dx] pci@0000:b5:00.1 ens6f1 network MT2892 Family [ConnectX-6 Dx] # Verify the link state is right. Assuming NIC port 0 is connected. $ sudo mlxlink -d b5:00.0 Operational Info ---------------- State : Active Physical state : LinkUp Speed : 100G Width : 4x FEC : Standard RS-FEC - RS(528,514) Loopback Mode : No Loopback Auto Negotiation : ON Supported Info -------------- Enabled Link Speed (Ext.) : 0x000007f2 (100G_2X,100G_4X,50G_1X,50G_2X,40G,25G,10G,1G) Supported Cable Speed (Ext.) : 0x000002f2 (100G_4X,50G_2X,40G,25G,10G,1G) Troubleshooting Info -------------------- Status Opcode : 0 Group Opcode : N/A Recommendation : No issue was observed.


Set Up the Host Environment

Set up the environment on both servers. You must run aerial-init.sh once each time the system reboots. Refer to the “System Initialization Script” section in the cuBB Installation Guide for more details.

Copy
Copied!
            

$ sudo ./aerial-init.sh


Launch the cuBB Container

Use the following command to launch the cuBB container:

Copy
Copied!
            

$ sudo docker exec -it cuBB /bin/bash


Build cuPHY SDK in the Container

Build cuPHY in the cuBB container using the following commands:

Copy
Copied!
            

$ cd /opt/nvidia/cuBB/cuPHY $ mkdir build $ mkdir install $ cd build $ cmake .. -DCMAKE_INSTALL_PREFIX=../install $ make -j $(nproc --all)

cuPHY is, by default, built in Release mode. The option BUILD_DOCS=ON is also enabled by default to allow the make to generate the Doxygen documentation for the cuPHY library API. To disable this option, pass -DBUILD_DOCS=OFF to the CMake command line. The output directory is cuPHY/install/docs.

To put the built cuPHY headers and libraries into an installation directory so that other applications using the cuPHY library can compile and link with cuPHY, use the commands from the current build directory:

Copy
Copied!
            

$ make install

This creates the include and lib directories under the cuPHY/install directory.

This section describes how to run the cuPHY SDK standalone example programs. They read test vector data files as input. Refer to the supported test vector configurations list in the cuBB Release Notes for what test vectors to use for all the different configurations. Do not use old test vectors from previous cuBB releases with the example programs of this release.

Generating Test Vectors using Matlab 5GModel

Run this Matlab command:

Copy
Copied!
            

cd('nr_matlab'); startup; [nTC, errCnt] = runRegression({'TestVector'}, {'allChannels'}, 'compact', [0, 1] );

All the cuPHY test vectors are generated and stored under nr_matlab/GPU_test_input.

Instructions for Testing cuPHY Channels Manually

PUSCH

Test Vectors

Match test vector name with PUSCH_gNB_CUPHY.h5

How to Run

  • Streams mode: cuPHY/build/examples/pusch_rx_multi_pipe/cuphy_ex_pusch_rx_multi_pipe -i ~/<tv_name>.h5

  • Graphs mode: cuPHY/build/examples/pusch_rx_multi_pipe/cuphy_ex_pusch_rx_multi_pipe -i ~/<tv_name>.h5 -m 1

Expected Outcome

Test 1 (CRC test KPI): All test cases must have zero CRC errors (only CRC errors, not correct ones, are reported when the channel is run).

PUCCH

Test Vectors

Match test vector name with PUCCH_F*_gNB_CUPHY.h5

How to Run

PUCCH format 0/1/2/3: cuPHY/build/examples/pucch_rx_pipeline/cuphy_ex_pucch_rx_pipeline -i <tv_name>

Expected Outcome

  • cuphy_ex_pucch_Fx_receiver checks if the test vector includes PFx UCI first.

  • If the test-vector UCI format is not expected, it displays “No PFx UCI received”.

pucch_outcome_1.png

  • If the test-vector UCI format is expected, it compares UCI output.xzsd.

pucch_outcome_2.png


PRACH

Test Vectors

Match test vector name with PRACH_gNB_CUPHY.h5

How to Run

cuPHY/build/examples/prach_receiver_multi_cell/prach_receiver_multi_cell -i <tv_name> -r <num_iteration> -k

Expected Outcome

  • prach_receiver_multi_cell compares against the reference measurements in the test vector.

  • Measured values are displayed and if they are within tolerance the message is displayed:

========> Test PASS

PDSCH

Test Vectors

Match test vector name with PDSCH_gNB_CUPHY.h5

How to Run

  • PDSCH in non-AAS mode, streams: cuPHY/build/examples/pdsch_tx/cuphy_ex_pdsch_tx ~/<tv_name>.h5 2 0 0

  • PDSCH in non-AAS mode, graphs: cuPHY/build/examples/pdsch_tx/cuphy_ex_pdsch_tx ~/<tv_name>.h5 2 0 1

Expected Outcome

Test 1 (correctness against reference model): Channel reports correct match with reference model

PDCCH

Test Vectors

Match test vector name with PDCCH_gNB_CUPHY.h5

How to Run

  • Streams mode: cuPHY/build/examples/pdcch/embed_pdcch_tf_signal -i ~/<tv_name>.h5 -m 0

  • Graphs mode: cuPHY/build/examples/pdcch/embed_pdcch_tf_signal -i ~/<tv_name>.h5 -m 1

Expected Outcome

Test 1 (correctness against reference model): Test PASS

SSB

Test Vectors

Match test vector name with SSB_gNB_CUPHY.h5

How to Run

  • Streams mode: cuPHY/build/examples/ss/testSS -i ~/<tv_name>.h5 -m 0

  • Graphs mode: cuPHY/build/examples/ss/testSS -i ~/<tv_name>.h5 -m 1

Expected Outcome

Test 1 (correctness against reference model): Test PASS

CSI-RS

Test Vectors

Match test vector name with CSIRS_gNB_CUPHY.h5

How to Run

  • Streams mode: cuPHY/build/examples/csi_rs/nzp_csi_rs_test -i <tv_name> -m 0

  • Graphs mode: cuPHY/build/examples/csi_rs/nzp_csi_rs_test -i <tv_name> -m 1

Expected Outcome

Test 1 (correctness against reference model): Test PASS

Instructions for LDPC Performance Test

The ldpc_perf_collect.py Python script from the cuPHY repository can be used to perform error rate tests for the cuPHY LDPC decoder. There are test input files defined for Z = [64, 128, 256, 384], BG = [1,2]. The current tests check whether the block error rate (BLER, also sometimes referred to as Frame Error Rate or FER) is less than 0.1.

From the build directory, the following commands run the tests:

Copy
Copied!
            

../util/ldpc/ldpc_perf_collect.py --mode test -i ../util/ldpc/test/ldpc_decode_BG1_Z64_BLER0.1.txt  -f -w 800 -P ../util/ldpc/ldpc_perf_collect.py --mode test -i ../util/ldpc/test/ldpc_decode_BG1_Z128_BLER0.1.txt -f -w 800 -P ../util/ldpc/ldpc_perf_collect.py --mode test -i ../util/ldpc/test/ldpc_decode_BG1_Z256_BLER0.1.txt -f -w 800 -P ../util/ldpc/ldpc_perf_collect.py --mode test -i ../util/ldpc/test/ldpc_decode_BG1_Z384_BLER0.1.txt -f -w 800 -P ../util/ldpc/ldpc_perf_collect.py --mode test -i ../util/ldpc/test/ldpc_decode_BG2_Z64_BLER0.1.txt  -f -w 800 -P ../util/ldpc/ldpc_perf_collect.py --mode test -i ../util/ldpc/test/ldpc_decode_BG2_Z128_BLER0.1.txt -f -w 800 -P ../util/ldpc/ldpc_perf_collect.py --mode test -i ../util/ldpc/test/ldpc_decode_BG2_Z256_BLER0.1.txt -f -w 800 -P ../util/ldpc/ldpc_perf_collect.py --mode test -i ../util/ldpc/test/ldpc_decode_BG2_Z384_BLER0.1.txt -f -w 800 -P

Each test input file contains multiple tests for different code rates, as specified by the number of parity nodes.

Within the util/perf folder, there is a set of Python scripts to automate and simplify performance measurements aimed at characterizing the cell capacity of cuPHY in isolation (that is, without I/O to and from NIC or layer 2).

The scripts offer the possibility of measuring the following:

Workload latency with peak cells, for the following use cases:

  • F01 (20MHz cells, 4 layers downlink and 1 layer uplink, numerology index equal to 0) with concurrent reference workloads PDSCH and PUSCH;

  • F14 (100MHz cells, 16 layers downlink and 8 layers uplink, numerology index equal to 1) with reference concurrent workloads PDSCH and PUSCH.

Capacity with peak + average cells, for the following use cases:

  • F01 (20MHz cells, 4 layers downlink and 1 layer uplink, numerology index equal to 0) with concurrent reference workloads PDSCH and PUSCH.

  • F14 (100MHz cells, 16 layers downlink and 8 layers uplink, numerology index equal to 1) with concurrent reference workloads PDSCH and PUSCH.

In all cases, Aerial SDK offers the possibility of measuring the latency of all workloads (PDSCH and PUSCH) including:

  • Dynamic and heterogeneous traffic (meaning that each cell is stimulated with different test vectors and every slot sees a different allocation of the test vectors to the considered cells)

  • Specific traffic models

Further details on preparing the test vectors and on using the scripts to conduct the measurements can be found in cuPHY/util/perf/performance.md.

Previous Generating TV and Launch Pattern Files
Next Running cuBB End-to-End
© Copyright 2022-2023, NVIDIA.. Last updated on Apr 20, 2024.