December 18, 2021
Conference Paper

SV-Sim: Scalable PGAS-based State Vector Simulation of Quantum Circuits

Abstract

High-performance quantum circuit simulation in a classic HPC is still imperative in the NISQ era. Observing that the major obstacle of scalable state-vector quantum simulation arises from the massively fine-grained irregular data-exchange with remote nodes, in this paper we present SV-Sim to apply the emerging PGAS-based communication models (i.e., direct peer access for intra-node CPUs/GPUs and SHMEM for inter-node CPU/GPU clusters) for efficient scalable quantum circuit simulation. Through an orchestrated device functional pointer design, SV-Sim is able to abstract the quantum gate sets across various heterogeneous backends, including IBM/Intel/AMD CPUs, NVIDIA /AMD GPUs, and Intel MIC, in a unified framework, but still asserting outstanding performance and tractable interface to higher-level quantum programming environments, such as IBM Qiskit, Microsoft Q\# and Google Cirq. Circumventing the disability of polymorphism in GPUs and leveraging the device-initiated one-sided communication, SV-Sim can process dynamically synthesized quantum circuit in a single GPU/CPU kernel without the need of expensive JIT or runtime branching, significantly improving the performance and simplifying the programming complexity for the emerging variational quantum algorithms. Evaluations on NVIDIA A100-DGX-1, V100-DGX-2, AMD MI100, ALCF Theta, and OLCF Summit HPCs show that SV-Sim can delivery scalable performance on various state-of-the-art HPC platforms, offering a useful tool for quantum algorithm validation and verification.

Published: December 18, 2021

Citation

Li A., B. Fang, C.E. Granade, G. Prawiroatmodjo, B. Heim, M. Roetteler, and S. Krishnamoorthy. 2021. SV-Sim: Scalable PGAS-based State Vector Simulation of Quantum Circuits. In Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2021), November 14-19, 2021, Virtual, Online, Art. No. 97. New York, New York:Association for Computing Machinery. PNNL-SA-161181. doi:10.1145/3458817.3476169