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The Atlas multi-GPU quantum circuit simulator.

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// repository documentation

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# Atlas: High-performance GPU-based Quantum Circuit Simulator

## Installation

### Prerequisites
* cuQuantum (requires compute capability 7.0+)
* OpenMPI/MPICH
* cmake >= 3.18
* NCCL
* CUDA

### Build Atlas

#### 1. Set related environment variables in `config/config.linux`
We support two modes for simulation in Atlas. First one is distributed GPU-based simulation (`USE_LEGION=OFF`). The other one is CPU-offload enabled simulation (`USE_LEGION=ON`), which support simulating more qubits on a single machine. Note that the second mode hasn't been tested for multi-node execution.

In addition, please also replace all hard-coded paths (starting with `/global/homes/m/mingkuan`) with your home directory.

#### 2. Install the HiGHS solver in Quartz
```shell
cd deps/quartz/external/HiGHS
mkdir build
cd build
cmake ..
make -j 12
```
#### 3. Build and Install
```shell
cd ../../../../..  # cd $TORQUE_HOME
mkdir build
cd build
# module load nccl  # You may need this on Perlmutter
bash ../config/config.linux
make -j 12
```

## Run the Simulation
### Perlmutter

#### Sbatch
There are some `sbatch` scripts for running simulation using Atlas in `scripts/perlmutter/bench`. Run them with:
```
sbatch xxx.sh
```

#### Interactive mode:
* Allocate nodes
```
salloc --nodes 2 -q regular --time 00:20:00 --constraint gpu --gpus-per-node 4 --account=YOUR_ACCOUNT
```
* Load modules

```
module load nccl
module load cudatoolkit
conda activate qs
export PATH=$PATH:$HiGHS_HOME/build/bin
export MPICH_GPU_SUPPORT_ENABLED=1
```

* Distributed GPU-based simulation launched with `srun`, for example:

```
srun -u \
     --ntasks="$(( SLURM_JOB_NUM_NODES ))" \
     --ntasks-per-node=1\ 
     $TORQUE_HOME/build/examples/mpi-based/simulate --import-circuit qft --n 31 --local 28 --device 4 --use-ilp
```

### For CPU-offload simulation:

1. Create a Python 3.8 environment with PuLP:
```shell
conda create --name pulp python=3.8
conda activate pulp
pip install pulp
```

2. Make sure the `setenv("PYTHONPATH", ...)` in `examples/legion-based/test_sim_legion.cc` is pointing to the correct location.

3. Build and run in interactive mode:
```shell
cd build
make -j 12
cd ../scripts/perlmutter/bench
salloc --nodes 1 -q regular --time 00:30:00 --constraint gpu --gpus-per-node 4 --account=YOUR_ACCOUNT
bash offload.sh  # takes around 25 minutes
```

### AWS

#### For CPU-offload simulation:
To run the scalability test on an AWS p3.8xlarge instance:
```
cd $TORQUE_HOME/build/examples/legion-based/`

./test -ll:gpu NUM_GPU -ll:fsize F_SIZE -ll:zsize Z_SIZE --local-qubits LOCAL_QUBITS_NUM --all-qubits ALL_QUBITS_NUM
```
* `-ll:gpu`: set the number of GPUs we have for the simulation.

* `-ll:fsize`: total gpu memory we have (e.g., 15000)

* `-ll:zsize`: zero-copy DRAM size (e.g., 100000)

* `--local-qubits`: the number of local qubits (for a 16G GPU, 28 local qubits at most)

* `--all-qubits`: the number of all the qubits.

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