State Vector Simulators¶

CPU¶

The qpp-cpu backend backend provides a state vector simulator based on the CPU-only, OpenMP threaded Q++ library. This backend is good for basic testing and experimentation with just a few qubits, but performs poorly for all but the smallest simulation and is the default target when running on CPU-only systems.

To execute a program on the qpp-cpu target even if a GPU-accelerated backend is available,

use the following commands:

Python

python3 program.py [...] --target qpp-cpu

The target can also be defined in the application code by calling

cudaq.set_target('qpp-cpu')

If a target is set in the application code, this target will override the --target command line flag given during program invocation.

C++

nvq++ --target qpp-cpu program.cpp [...] -o program.x
./program.x

Single-GPU¶

The nvidia backend provides a state vector simulator accelerated with - the cuStateVec library. The cuStateVec documentation provides a detailed explanation for how the simulations are performed on the GPU.

The nvidia target supports multiple configurable options including specification of floating point precision.

To execute a program on the nvidia backend, use the following commands:

Python

Single Precision (Default):

python3 program.py [...] --target nvidia --target-option fp32

Double Precision:

python3 program.py [...] --target nvidia --target-option fp64

The target can also be defined in the application code by calling

cudaq.set_target('nvidia', option = 'fp64')

If a target is set in the application code, this target will override the --target command line flag given during program invocation.

C++

Single Precision (Default):

nvq++ --target nvidia --target-option fp32 program.cpp [...] -o program.x
./program.x

Double Precision (Default):

nvq++ --target nvidia --target-option fp64 program.cpp [...] -o program.x
./program.x

Note

This backend requires an NVIDIA GPU and CUDA runtime libraries. If you do not have these dependencies installed, you may encounter an error stating Invalid simulator requested. See the section Dependencies and Compatibility for more information about how to install dependencies.

In the single-GPU mode, the nvidia backend provides the following environment variable options. Any environment variables must be set prior to setting the target. It is worth drawing attention to gate fusion, a powerful tool for improving simulation performance which is discussed in greater detail here.

**Environment variable options supported in single-GPU mode**¶
Option	Value	Description
`CUDAQ_FUSION_MAX_QUBITS`	positive integer	The max number of qubits used for gate fusion. The default value is `4`.
`CUDAQ_FUSION_DIAGONAL_GATE_MAX_QUBITS`	integer greater than or equal to -1	The max number of qubits used for diagonal gate fusion. The default value is set to `-1` and the fusion size will be automatically adjusted for the better performance. If 0, the gate fusion for diagonal gates is disabled.
`CUDAQ_FUSION_NUM_HOST_THREADS`	positive integer	Number of CPU threads used for circuit processing. The default value is `8`.
`CUDAQ_MAX_CPU_MEMORY_GB`	non-negative integer, or `NONE`	CPU memory size (in GB) allowed for state-vector migration. `NONE` means unlimited (up to physical memory constraints). Default is 0GB (disabled, variable is not set to any value).
`CUDAQ_MAX_GPU_MEMORY_GB`	positive integer, or `NONE`	GPU memory (in GB) allowed for on-device state-vector allocation. As the state-vector size exceeds this limit, host memory will be utilized for migration. `NONE` means unlimited (up to physical memory constraints). This is the default.

Deprecated since version 0.8: The nvidia-fp64 targets, which is equivalent setting the fp64 option on the nvidia target, is deprecated and will be removed in a future release.

Multi-node multi-GPU¶

The nvidia backend also provides a state vector simulator accelerated with the cuStateVec library with support for Multi-Node, Multi-GPU distribution of the state vector.

This backend is necessary to scale applications that require a state vector that cannot fit on a single GPU memory.

The multi-node multi-GPU simulator expects to run within an MPI context. To execute a program on the multi-node multi-GPU NVIDIA target, use the following commands (adjust the value of the -np flag as needed to reflect available GPU resources on your system):

See the Divisive Clustering application to see how this backend can be used in practice.

Python

Double precision simulation:

mpiexec -np 2 python3 program.py [...] --target nvidia --target-option fp64,mgpu

Single precision simulation:

mpiexec -np 2 python3 program.py [...] --target nvidia --target-option fp32,mgpu

Note

If you installed CUDA-Q via pip, you will need to install the necessary MPI dependencies separately; please follow the instructions for installing dependencies in the Project Description.

In addition to using MPI in the simulator, you can use it in your application code by installing mpi4py, and invoking the program with the command

mpiexec -np 2 python3 -m mpi4py program.py [...] --target nvidia --target-option fp64,mgpu

The target can also be defined in the application code by calling

cudaq.set_target('nvidia', option='mgpu,fp64')

If a target is set in the application code, this target will override the --target command line flag given during program invocation.

Note

The order of the option settings are interchangeable. For example, cudaq.set_target('nvidia', option='mgpu,fp64') is equivalent to cudaq.set_target('nvidia', option='fp64,mgpu').
The nvidia target has single-precision as the default setting. Thus, using option='mgpu' implies that option='mgpu,fp32'.

C++

Double precision simulation:

nvq++ --target nvidia  --target-option mgpu,fp64 program.cpp [...] -o program.x
mpiexec -np 2 ./program.x

Single precision simulation:

nvq++ --target nvidia  --target-option mgpu,fp32 program.cpp [...] -o program.x
mpiexec -np 2 ./program.x

Note

This backend requires an NVIDIA GPU, CUDA runtime libraries, as well as an MPI installation. If you do not have these dependencies installed, you may encounter either an error stating invalid simulator requested (missing CUDA libraries), or an error along the lines of failed to launch kernel (missing MPI installation). See the section Dependencies and Compatibility for more information about how to install dependencies.

The number of processes and nodes should be always power-of-2.

Host-device state vector migration is also supported in the multi-node multi-GPU configuration.

In addition to those environment variable options supported in the single-GPU mode, the nvidia backend provides the following environment variable options particularly for the multi-node multi-GPU configuration. Any environment variables must be set prior to setting the target.

**Additional environment variable options for multi-node multi-GPU mode**¶
Option	Value	Description
`CUDAQ_MGPU_LIB_MPI`	string	The shared library name for inter-process communication. The default value is `libmpi.so`.
`CUDAQ_MGPU_COMM_PLUGIN_TYPE`	`AUTO`, `EXTERNAL`, `OpenMPI`, or `MPICH`	Selecting `cuStateVec` `CommPlugin` for inter-process communication. The default is `AUTO`. If `EXTERNAL` is selected, `CUDAQ_MGPU_LIB_MPI` should point to an implementation of `cuStateVec` `CommPlugin` interface.
`CUDAQ_MGPU_NQUBITS_THRESH`	positive integer	The qubit count threshold where state vector distribution is activated. Below this threshold, simulation is performed as independent (non-distributed) tasks across all MPI processes for optimal performance. Default is 25.
`CUDAQ_MGPU_FUSE`	positive integer	The max number of qubits used for gate fusion. The default value is `6` if there are more than one MPI processes or `4` otherwise.
`CUDAQ_MGPU_P2P_DEVICE_BITS`	positive integer	Specify the number of GPUs that can communicate by using GPUDirect P2P. Default value is 0 (P2P communication is disabled).
`CUDAQ_GPU_FABRIC`	`MNNVL`, `NVL`, or `NONE`	Automatically set the number of P2P device bits based on the total number of processes when multi-node NVLink (`MNNVL`) is selected; or the number of processes per node when NVLink (`NVL`) is selected; or disable P2P (with `NONE`).
`CUDAQ_GLOBAL_INDEX_BITS`	comma-separated list of positive integers	Specify the inter-node network structure (faster to slower). For example, assuming a 8 nodes, 4 GPUs/node simulation whereby network communication is faster, this `CUDAQ_GLOBAL_INDEX_BITS` environment variable can be set to `3,2`. The first `3` represents 8 nodes with fast communication and the second `2` represents 4 8-node groups in those total 32 nodes. Default is an empty list (no customization based on network structure of the cluster).
`CUDAQ_HOST_DEVICE_MIGRATION_LEVEL`	positive integer	Specify host-device memory migration w.r.t. the network structure. If provided, this setting determines the position to insert the number of migration index bits to the `CUDAQ_GLOBAL_INDEX_BITS` list. By default, if not set, the number of migration index bits (CPU-GPU data transfers) is appended to the end of the array of index bits (aka, state vector distribution scheme). This default behavior is optimized for systems with fast GPU-GPU interconnects (NVLink, InfiniBand, etc.)

Deprecated since version 0.8: The nvidia-mgpu backend, which is equivalent to the multi-node multi-GPU double-precision option (mgpu,fp64) of the nvidia is deprecated and will be removed in a future release.

The above configuration options of the nvidia backend can be tuned to reduce your simulation runtimes. One of the performance improvements is to fuse multiple gates together during runtime. For example, x(qubit0) and x(qubit1) can be fused together into a single 4x4 matrix operation on the state vector rather than 2 separate 2x2 matrix operations on the state vector. This fusion reduces memory bandwidth on the GPU because the state vector is transferred into and out of memory fewer times. By default, up to 4 gates are fused together for single-GPU simulations, and up to 6 gates are fused together for multi-GPU simulations. The number of gates fused can significantly affect performance of some circuits, so users can override the default fusion level by setting the setting CUDAQ_MGPU_FUSE environment variable to another integer value as shown below.

Python

CUDAQ_MGPU_FUSE=5 mpiexec -np 2 python3 program.py [...] --target nvidia --target-option mgpu,fp64

C++

nvq++ --target nvidia --target-option mgpu,fp64 program.cpp [...] -o program.x
CUDAQ_MGPU_FUSE=5 mpiexec -np 2 ./program.x