arXiv:2611.16285 · v1

Scaling Pêlagos: Multi-GPU Orchestration for Relay Belief-Propagation Decoding

首次提交 2026-11-12 12:36

版本更新 2026-11-12 12:36

雷达观测 2026-11-13 14:16

摘要

Fault-tolerant quantum computing requires quantum error-correction (QEC) decoders that achieve low logical error rates under real-time operating constraints. These constraints motivate heuristic decoding algorithms whose error-correction performance must be evaluated through large-scale simulation. High-throughput decoding accelerates this evaluation and the exploration of decoder parameters. Using multiple graphics processing units (GPUs) is a natural way to scale these simulations, but naively distributing syndrome batches introduces host-side preprocessing and output-collection overheads that limit scaling. To address these limitations, we extend the Pêlagos relay belief-propagation (Relay-BP) decoder with process-isolated orchestration, parallel host preprocessing, and shared-memory output. We evaluate both one-gross ($[[144,12,12]]$) and two-gross ($[[288,12,18]]$) codes in a multi-GPU environment. Specifically, for the one-gross circuit at $R=50$, eight AMD Instinct MI300X GPUs and eight AMD Instinct MI355X GPUs achieve optimized end-to-end speedups of $5.68\times$ and $6.23\times$, respectively. For the two-gross circuit, the corresponding speedups are $6.92\times$ and $7.01\times$. When throughput is calculated using GPU decoding time, the corresponding two-gross speedups are $7.58\times$ and $7.45\times$, indicating near-linear GPU scaling. We deploy Pêlagos on Lux, Oak Ridge National Laboratory's AMD Instinct MI355X-based supercomputer, as a pathfinding application for high-throughput QEC simulation. This deployment provides a concrete quantum-computing use case for the AI + HPC infrastructure supporting the Genesis Mission's scientific-discovery goals. These end-to-end measurements demonstrate a practical path to accelerating large-scale QEC simulation through efficient multi-GPU decoding.