dgQuEST

Source code is now available. Build/run guidelines and experimental setup descriptions are still incomplete and we are working in progress.

Dependencies

• NVIDIA CUDA Toolkit (tested on CUDA 11.1, other versions are not tested)
• An MPI implementation (tested on MPICH 4.0.1 and OpenMPI 4.1.2, other versions are not tested)

Experimental Setup

The evaluation is done in the following environment

• MPI: MPICH 4.0.1
• OS: Ubuntu 20.04 LTS (Linux kernel: 5.4.0-77-generic)
• CPU: Intel(R) Xeon(R) CPU E5-2680 v4 @ 2.40GHz
• GPU: NVIDIA Tesla V100 32GB
• InfiniBand NIC: ConnectX-3 40 Gb/sec (4X FDR10)

Other similar environments should also work but it's not ensured since we have not tested them.

How to Build

Clone the repo and build with CMake. Do not forget to clone submodules by adding --recursive option to git clone, or use git submodule init followed by git submodule update.

git clone https://github.com/fty1777/dgQuEST.git
cd dgQuEST
git submodule init
git submodule update

mkdir build
cd build
cmake .. -DCMAKE_CUDA_COMPILER=<path/to/nvcc> -DCMAKE_CUDA_ARCHITECTURES=<arch, e.g. 70 for V100>
make -j

To change the user program including the quantum circuit, append -DUSER_PROGRAM=<path/to/user/program> option to cmake command or modify the CMakeLists.txt. After executing cmake, you can also modify the generated CMakeCache.txt in build directory.

How to Run

First, execute the sampler once to run the benchmarks for the time predictor:

cd build
./sampler

To change the sampled range of the nubmer of qubits, edit sampler/main.cpp. The sampled data are stored in file predict_data.txt in text format. The name should not be changed because the simulator will use the file with this fixed filename.

Before running the simulator, a config file is required to be created. The config file must contains 2 to 3 integers, which are the minK, maxK and designatedK, respectively. The minK and maxK determined the searching range of the analytical model in our paper. The designatedK is not necessary. If designatedK is given, the simulator will run with this given K instead of the optimum one predicted by the model. If designatedK is not given, the simulator will use the K selected by the prediction model. After creating the config file, run the simulator:

echo <configs> > QuEST.conf # or `cp <DGQUEST_ROOT>/configs/QuEST.conf .` to create the configuration file
./test

Code structure

Our work is mainly in /src/cpp directory, in which:

• QuEST_backend_distributed.cpp contains the implementation of the hybrid memory management and main logic of page-table-based memory management, pipelined communication, etc.
• QuEST_gpu_kernel.cu contains the calculation part including fused kernel, reordering, etc.
• time_predictor.c/time_predictor.h contains the analytical prediction model, the interface is a TimePredictor class.
• DAG.cpp contains the DAG-based gate aggregation mechanism and the heuristic DFS algorithm in our paper.