Towards Scalable Quantum Circuit Simulation via RDMA

Chia Hsin Hsu, Chuan Chi Wang, Nai Wei Hsu, Chia Heng Tu, Shih Hao Hung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The trend towards optimization of quantum circuit simulators (QCS) has been driven by the rapid development of quantum computing, as there is a growing interest in creating high-efficiency QCS for quantum computing systems and applications. Despite this growth, the existing classical simulators pose challenges in simulating large quantum operations due to the exponential growth of memory and computation resources required. To overcome these issues, some studies have explored the use of non-volatile memory, such as solid-state disks (SSDs), to store a large amount of state vector while maintaining cost efficiency. However, such highly frequent read-and-write operations in large quantum simulations can quickly deplete the lifespan of SSDs, leading to overestimated practical efficiency and environmental concerns. Alternatively, Remote Direct Memory Access (RDMA) can be used to expand memory capacity, allowing computers in a network to access remote memories with low latency and high bandwidth. Therefore, we propose using one-sided RDMA operations along with a series of optimizations to expand memory capacity and efficiently maximize the utilization of unused memory assets. The experimental results demonstrate that our approaches can be practically scaled up without any lifespan consumption. The proposed optimization can get a 3.0x speedup compared to the local memory approach and a maximum speedup of 2.0x compared to the naive RDMA-based approach, as observed in our benchmark test.

Original languageEnglish
Title of host publication2023 Research in Adaptive and Convergent Systems RACS 2023
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9798400702280
DOIs
Publication statusPublished - 2023 Aug 6
Event2023 Research in Adaptive and Convergent Systems, RACS 2023 - Gdansk, Poland
Duration: 2023 Aug 62023 Aug 10

Publication series

Name2023 Research in Adaptive and Convergent Systems RACS 2023

Conference

Conference2023 Research in Adaptive and Convergent Systems, RACS 2023
Country/TerritoryPoland
CityGdansk
Period23-08-0623-08-10

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • General Computer Science

Fingerprint

Dive into the research topics of 'Towards Scalable Quantum Circuit Simulation via RDMA'. Together they form a unique fingerprint.

Cite this