混合量子-高性能计算中间件系统用于自适应资源、工作负载和任务管理
Hybrid Quantum-HPC Middleware Systems for Adaptive Resource, Workload and Task Management
AI总结：
本文提出混合量子-高性能计算系统中自适应资源、工作负载和任务管理的中间件方法，通过四层架构、执行动机集、Pilot-Quantum框架和Q-Dreamer工具包实现应用感知调度和性能建模。
摘要：
混合量子-经典应用由于基础设施和工作负载的异构性和动态性，对资源管理提出了重大挑战。量子-高性能计算环境将量子处理单元（QPUs）与多种经典资源（CPUs、GPUs）结合，而应用涵盖从紧密耦合执行到松散耦合任务并行的耦合模式，具有变化的资源需求。传统高性能计算调度器缺乏对应用语义的可见性，无法在运行时响应资源可用性的波动。本文提出了一种基于中间件的方法，用于混合量子-高性能计算系统中的自适应资源、工作负载和任务管理。我们做出了四方面的贡献：（i）一个概念性的四层中间件架构，将管理分解到工作流、工作负载、任务和资源层面，从而在异构量子-高性能计算资源上实现应用感知调度；（ii）一组执行动机集，捕捉混合应用的交互和耦合特征，实现为量子微型应用，用于系统化的工作负载特征化；（iii）Pilot-Quantum，一个基于Pilot抽象的中间件框架，实现了后期绑定和动态资源分配，适应于运行时的资源和工作负载动态；（iv）Q-Dreamer，一个性能建模工具包，提供可重用的组件用于有指导的工作负载划分，包括一个电路切割优化器，能够分析性地推导出最优的划分策略。在异构HPC平台（Perlmutter、NVIDIA DGX配备H100/B200 GPU）上的评估显示，能够高效地在CPU、GPU和QPUs之间进行多后端编排，适用于各种执行动机。Q-Dreamer预测最优电路切割配置的准确率高达82%。
英文摘要：
Hybrid quantum-classical applications pose significant resource management challenges due to heterogeneity and dynamism in both infrastructure and workloads. Quantum-HPC environments integrate quantum processing units (QPUs) with diverse classical resources (CPUs, GPUs), while applications span coupling patterns from tightly coupled execution to loosely coupled task parallelism with varying resource requirements. Traditional HPC schedulers lack visibility into application semantics and cannot respond to fluctuating resource availability at runtime. This paper presents a middleware-based approach for adaptive resource, workload, and task management in hybrid quantum-HPC systems. We make four contributions: (i) a conceptual four-layer middleware architecture that decomposes management across workflow, workload, task, and resource levels, enabling application-aware scheduling over heterogeneous quantum-HPC resources; (ii) a set of execution motifs capturing interaction and coupling characteristics of hybrid applications, realized as quantum mini-apps for systematic workload characterization; (iii) Pilot-Quantum, a middleware framework built on the pilot abstraction that enables late binding and dynamic resource allocation, adapting to resource and workload dynamics at runtime; and (iv) Q-Dreamer, a performance modeling toolkit providing reusable components for informed workload partitioning, including a circuit-cutting optimizer that analytically derives optimal partitioning strategies. Evaluation on heterogeneous HPC platforms (Perlmutter, NVIDIA DGX with H100/B200 GPUs) demonstrates efficient multi-backend orchestration across CPUs, GPUs, and QPUs for diverse execution motifs. Q-Dreamer predicts optimal circuit cutting configurations with up to 82% accuracy.
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