cuPQC

Pre-requisite

To utilize cuPQC the user needs the following:

• CUDA Toolkit 12.4 or newer

• Supported CUDA compiler

  cuPQC benchmarking video: https://youtu.be/mdnXKroR1wo?si=yiTMTUy1MIzl20_7

These two steps will be done using the following commands:

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/12.6.3/local_installers/cuda-repo-ubuntu2204-12-6-local_12.6.3-560.35.05-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu2204-12-6-local_12.6.3-560.35.05-1_amd64.deb
sudo cp /var/cuda-repo-ubuntu2204-12-6-local/cuda-*-keyring.gpg /usr/share/keyrings/
sudo apt-get update
sudo apt-get -y install cuda-toolkit-12-6

post cudakit installation steps:

export PATH=/usr/local/cuda-12.6/bin${PATH:+:${PATH}}

export LD_LIBRARY_PATH=/usr/local/cuda-12.6/lib64\
                         ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Make these changes permananet by adding them in the bashrc file

Check nvcc:

nvcc --version

the output should be like this:

master@master:~/slum_dawg$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2024 NVIDIA Corporation
Built on Tue_Oct_29_23:50:19_PDT_2024
Cuda compilation tools, release 12.6, V12.6.85
Build cuda_12.6.r12.6/compiler.35059454_0

• Supported host compiler (C++17 required)

By default present in g++ and gcc 11(default in ubuntu 22)

• (Optionally) CMake (version 3.20 or greater)

• x86_64 CPU

• A NVIDIA GPU with one the following architectures: 70, 75, 80, 86, 89, 90

In this we used rtx a4000. Recommended: H100

Build

Firstly clone this repo

Then go to the example directory

cd cuPQC/examples/

Now either run the make command directly

make

or either run the whole command manually

nvcc -dlto -arch=native -std=c++17 -O3  -L../lib/ -lcupqc -lcuhash -o <binary_name> <file_name.cu>  -I../include/ -I../include/cupqc

for eg:

nvcc -dlto -arch=native -std=c++17 -O3  -L../lib/ -lcupqc  -o v4_max_bench v4_max_bench.cu  -I../include/ -I../include/cupqc

Make sure to adjust the arch as per your GPU's compute capability. Check it here

Note: if programs dont run then delete the binary and build again using any of the given above two commands

Benchmarking (these tests were done on Nvidia GH200 and files are configured according to that)

For ML-KEM 512 Benchmarking:

nvcc -dlto -arch=native -std=c++17 -O3  -L../lib/ -lcupqc  -o v4_max_bench v4_max_bench.cu  -I../include/ -I../include/cupqc
./v4_max_bench

For ML-KEM 512 we achieved:

• Keygen: ~20 million ops/sec
• Encapsulation: ~18.5 million ops/sec
• Decapsulation: ~18 million ops/sec

For ML-KEM 768: ./mlkem768_bench

For ML-KEM 1024: ./mlkem1024_bench

For ML-DSA 44: ./bench_mldsa

For ML-DSA 65: ./mldsa65_bench

For ML-KEM 87: ./mldsa87_bench