cuZK: An Efficient GPU Implemetation of zkSNARK

This library is an efficient GPU implemetation of zkSNARK. It contains source code of the paper cuZK: Accelerating Zero-Knowledge Proof with A Faster Parallel Multi-Scalar Multiplication Algorithm on GPUs published at TCHES 2023.

Note: The contributions of this fork are:

• improved reproducibility and scalability: using Docker for building, reduced memory footprint, tested various GPUs
• introduced profiling of time/energy performance

License

This library is licensed under the Apache License Version 2.0 and MIT licenses.

Requirements

To compile and run the code you only need a GPU with NVIDIA drivers installed; the CUDA Toolkit with compiling and runtime tools will be supplied by Docker.

The original experiments were accompolished in the following setup:

1. Ubuntu 20.04
2. CUDA 11.5
3. gcc 7.5.0
4. Nvidia V100 (32 GB)

Build / Develop

Select the appropriate NVIDIA CUDA image which contains building tools. Check the compatibility of installed NVIDIA drivers.

Start the container mounting the working directory with the github source code:

docker run -d \
   -it \
   --name nvidia-cuda \
   --runtime=nvidia \
   --mount type=bind,source=$(pwd),target=/home \
   --privileged \
   nvidia/cuda:11.7.1-devel-ubuntu20.04

Inside the running container, install git and libgmp3-dev:

apt-get update
apt-get install -y git libgmp3-dev

Within the cuZK/test directory, adjust the compilation scope in the headers of Makefile:

# cuZK/test/Makefile
all: msma msmb  # limit the compilation scope to selected files

and then run make (it will take a while!)

root@7816e1643c2a:/home/cuZK/test# make

NOTE: See also more on compute capability in the documentation.

NOTE: The original code reserves too much of RAM. This can be adjusted inside the function multi_init_params.

Profiling

Advanced profiling can be done with NVIDIA Management Library. The querying API should be used around the code piece of interest; the example below measures the energy consumption:

   nvmlInit();
   unsigned long long energy_start, energy_end, energy_elapsed;
   nvmlDeviceGetTotalEnergyConsumption(device, &energy_start);
   // code to profile ...
   nvmlDeviceGetTotalEnergyConsumption(device, &energy_end);
   energy_elapsed = energy_end - energy_start;
   nvmlShutdown();

The nvml library should be included in the source code with #include <nvml.h> and linked at compilation time with -l nvidia-ml option.

Performance depends on the clock frequency; the range of allowed frequencies can be checked with nvidia-smi -q -d SUPPORTED_CLOCKS and the memory/graphics clocks can be adjusted with nvidia-smi -ac $mem,$freq.

See the script profiling the MSM algorithm under a range of frequencies, and a sample Python script to process results.

Here are results obtained on Tesla V100-SXM2-16GB:

Run Original Code

To run a test of an MSM of 2^20 scale and EC points on the BLS12-381 curve, run:

## (It will take some time to run for the first time.)
./msmtestb 20 

To run a test of Groth protocol with 2^20 constraint scales and EC points on the BLS12-381 curve, run:

## (It will take some time to run for the first time.)
./testb 20

For EC points on the ALT_BN128 curve and MNT4, run:

## ALT_BN128
./msmtesta 20
./testb 20

## MNT4
./msmtestm 20
./testm 20

In addition, our BLS12-377 carve implementation has a Rust binding with the template from Sppark developed by Supranational LLC. To install the latest version of Rust, first install rustup. Once rustup is installed, install the Rust toolchain by invoking:

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
rustup install stable

To test BLS12-377 carve implementation.

cd test/BLS377
cargo bench

Original Rusults

Here are a selection of the results tested under NVIDIA V100 GPU card with BLS12-381 curve. More results can be found in the paper cuZK.

For MSM computation,

Scale	Bellperson	cuZK	Speedup
2^19	0.23 s	0.12	2.08x
2^20	0.41 s	0.19	2.18x
2^21	0.73 s	0.33	2.20x
2^22	1.30 s	0.58	2.25x
2^23	2.64 s	1.15	2.29x

For Groth's protocol,

Scale	Bellperson	cuZK	Speedup
2^19	2.62 s	0.98	2.67x
2^20	4.45 s	1.68	2.65x
2^21	7.96 s	2.76	2.88x
2^22	14.20 s	5.08	2.80x
2^23	29.13 s	9.91	2.94x