Performance Models for Split-execution Computing Systems
Abstract
Split-execution computing leverages the capabilities of multiple computational models to solve problems, but splitting program execution across different computational models incurs costs associated with the translation between domains. We analyze the performance of a split-execution computing system developed from conventional and quantum processing units (QPUs) by using behavioral models that track resource usage. We focus on asymmetric processing models built using conventional CPUs and a family of special-purpose QPUs that employ quantum computing principles. Our performance models account for the translation of a classical optimization problem into the physical representation required by the quantum processor while also accounting for hardware limitations and conventional processor speed and memory. We conclude that the bottleneck in this split-execution computing system lies at the quantum-classical interface and that the primary time cost is independent of quantum processor behavior.
Cite
@article{arxiv.1607.01084,
title = {Performance Models for Split-execution Computing Systems},
author = {Travis S. Humble and Alexander J. McCaskey and Jonathan Schrock and Hadayat Seddiqi and Keith A. Britt and Neena Imam},
journal= {arXiv preprint arXiv:1607.01084},
year = {2016}
}
Comments
Presented at 18th Workshop on Advances in Parallel and Distributed Computational Models [APDCM2016] on 23 May 2016; 10 pages