Year: 2024
Type: preprint
Abstract: This paper presents techniques for theoretically and practically efficient and scalable Schrödinger-style quantum circuit simulation. Our approach partitions a quantum circuit into a hierarchy of subcircuits and simulates the subcircuits on multi-node GPUs, exploiting available data parallelism while minimizing communication costs. To minimize communication costs, we formulate an Integer Linear Program that rewards simulation of "nearby" gates on "nearby" GPUs. To maximize throughput, we use a dynamic programming algorithm to compute the subcircuit simulated by each kernel at a GPU. We realize these techniques in Atlas, a distributed, multi-GPU quantum circuit simulator. Our evaluation on a variety of quantum circuits shows that Atlas outperforms state-of-the-art GPU-based simulators by more than 2$\times$ on average and is able to run larger circuits via offloading to DRAM, outperforming other large-circuit simulators by two orders of magnitude.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: CNS: Medium: Ground up Adaptive Learning System for Heterogeneous Environments, G1079818582, G1143651532, G122673668, SHF: Small: Solving the Parallel Functional Programming Challenge, G1677143136, G1863003419, G419881185, G4501827968, G498139845, G5076365615, CAREER: An Automated End-to-end Machine Learning System, Collaborative Research: PPoSS: LARGE: Unifying Software and Hardware to Achieve Performant and Scalable Frictionless Parallelism in the Heterogeneous Future, G7354628648, RINGS: Object-Oriented Video Analytics for Next-Generation Mobile Environments, Collaborative Research: SHF: Medium: Responsive Parallelism for Interactive Applications: Theory and Practice, SHF: Medium: Algorithmic lambda-Calculus for the Design, Analysis, and Implementation of Parallel Algorithms
OpenAlex ID: W4402503290