89 lines
3.1 KiB
Plaintext
89 lines
3.1 KiB
Plaintext
<span id="dask" />
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# Running with Threadpool and DASK
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Qiskit Aer runs simulation jobs on a single-worker Python multiprocessing ThreadPool executor so that all parallelization is handled by low-level OpenMP and CUDA code. However to customize job-level parallel execution of multiple circuits a user can specify a custom multiprocessing executor and control the splitting of circuits using the `executor` and `max_job_size` backend options. For large scale job parallelization on HPC clusters Qiskit Aer executors support the distributed Clients from the [DASK](http://dask.org).
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## Installation of DASK packages with Aer
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If you want to install dask client at the same time as Qiskit Aer, please add the `dask` extra as follows. This option installs Aer, dask, and distributed packages.
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```python
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pip install .[dask]
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```
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## Usage of executor
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To use Threadpool or DASK as an executor, you need to set `executor` and `max_job_size` by `set_options` function. If both `executor` (default None) and `max_job_size` (default None) are set, Aer splits the multiple circuits to some chunk of circuits and submits them to the executor. `max_job_size` can control the number of splitting circuits. When `max_job_size` is set to 1, multiple circuits are split into one circuit and distributed to the executor. If a user executes 60 circuits with the executor and `max_job_size=1`, Aer splits it as 60 jobs each of 1 circuit. If there are 60 circuits and `max_job_size=2`, Aer splits it as 30 jobs, each with 2 circuits.
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### Example: Threadpool execution
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```python
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import qiskit
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from concurrent.futures import ThreadPoolExecutor
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from qiskit_aer import AerSimulator
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from math import pi
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# Generate circuit
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circ = qiskit.QuantumCircuit(15, 15)
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circ.h(0)
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circ.cx(0, 1)
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circ.cx(1, 2)
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circ.p(pi/2, 2)
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circ.measure([0, 1, 2], [0, 1 ,2])
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circ2 = qiskit.QuantumCircuit(15, 15)
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circ2.h(0)
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circ2.cx(0, 1)
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circ2.cx(1, 2)
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circ2.p(pi/2, 2)
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circ2.measure([0, 1, 2], [0, 1 ,2])
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circ_list = [circ, circ2]
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qbackend = AerSimulator()
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# Set executor and max_job_size
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exc = ThreadPoolExecutor(max_workers=2)
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qbackend.set_options(executor=exc)
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qbackend.set_options(max_job_size=1)
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result = qbackend.run(circ_list).result()
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```
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### Example: Dask execution
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The Dask client uses `multiprocessing` so you need to guard it by an `if __name__ == "__main__":` block.
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```python
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import qiskit
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from qiskit_aer import AerSimulator
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from dask.distributed import LocalCluster, Client
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from math import pi
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def q_exec():
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# Generate circuits
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circ = qiskit.QuantumCircuit(15, 15)
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circ.h(0)
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circ.cx(0, 1)
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circ.cx(1, 2)
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circ.p(pi/2, 2)
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circ.measure([0, 1, 2], [0, 1 ,2])
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circ2 = qiskit.QuantumCircuit(15, 15)
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circ2.h(0)
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circ2.cx(0, 1)
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circ2.cx(1, 2)
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circ2.p(pi/2, 2)
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circ2.measure([0, 1, 2], [0, 1 ,2])
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circ_list = [circ, circ2]
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exc = Client(address=LocalCluster(n_workers=2, processes=True))
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# Set executor and max_job_size
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qbackend = AerSimulator()
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qbackend.set_options(executor=exc)
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qbackend.set_options(max_job_size=1)
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result = qbackend.run(circ_list).result()
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if __name__ == '__main__':
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q_exec()
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```
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