发表机构
BosonQ Psi Corporation（BosonQ Psi 公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文评估单一OpenMP5 QIEO源代码在多种GPU上的可移植性与性能，通过基因级卸载在V100、A100、MI300X上分别实现90倍、136倍、155倍于单核CPU的加速，并发现DetermineElite更适合主机执行。
AI 中文摘要
量子启发进化优化（QIEO）是一类新型的基于种群的元启发式优化算法，它将设计变量表示为一组量子比特，并通过旋转量子比特的振幅对来搜索连续的多维景观。每一代都将这些振幅朝向一个单一精英（即该代最佳个体）旋转。每代的计算成本为$O(N_p N_g)$，其中$N_p$为染色体数，$N_g$为基因（决策变量）数。此类求解器的生产使用很少局限于单一机器类别。原型通常在实验室服务器上运行，然后转移到租用的云工作站进行更复杂的活动。最大的问题则保留给领导级加速器。本文探讨是否一个单一的OpenMP 5 QIEO源代码（通过\ exttt{\#pragma omp target}卸载）能在上述每种环境中成为可行的生产路径。我们报告了针对同一源代码的多核Intel CPU基线，对0/1背包问题进行的三个独立活动。该研究包含约3000次运行，涵盖不同的染色体和基因数量，并在NVIDIA Tesla V100 SXM2、NVIDIA A100 80GB和AMD Instinct MI300X GPU上使用染色体级和基因级卸载策略进行评估。部署特有的细微差别，如Volta的常量内存悬崖、Ampere的L2持久性和\ exttt{this http URL}、CDNA 3的Infinity Cache和XCD占用率，均已处理以确保这些平台的高性能。结果显示，基因并行卸载在V100、A100和MI300X上相对于单CPU核心分别实现了90倍、136倍和155倍的几何平均加速，相对于72个主机线程分别实现了12倍、17倍和16.6倍的加速。此外，DetermineElite（即$O(N_p)$选择代最佳染色体的过程）被发现更适合在主机上执行而非设备上。
英文摘要
Quantum-inspired evolutionary optimization (QIEO) is a new class of population-based metaheuristic optimization algorithms which represents design variables as a set of qubits and searches a continuous, multi-dimensional landscape through rotation of the qubit's amplitude pair. Every generation rotates those amplitudes toward a single elite, which corresponds to that generation's best. The per-generation cost scales as $O(N_p N_g)$ for $N_p$ chromosomes and $N_g$ genes (decision variables). Production use of such solvers is rarely confined to a single machine class. Prototypes are run on laboratory servers- before moving to rented cloud workstations for more involved campaigns. The largest problems are reserved for leadership-class accelerators. This paper asks whether a \emph{single} OpenMP~5 source of QIEO, offloaded with \texttt{\#pragma omp target}, is a viable production path in each of those settings. We report three independent, campaigns of the 0/1 knapsack problem against a same-source multi-core Intel CPU baseline. The study comprises approximately 3,000 runs spanning varying chromosome and gene counts, evaluated using both chromosome-level and gene-level offload strategies on the NVIDIA Tesla V100 SXM2, NVIDIA A100 80GB, and AMD Instinct MI300X GPUs. Deployment-specific nuances such as Volta's constant-memory cliffs, Ampere's L2 persistence and \texttt{cp.async}, CDNA~3's Infinity Cache and XCD occupancy are addressed to ensure high performance of these platforms. Results reveal gene-parallel offload achieved geometric-mean speedups of 90$\times$, 136$\times$, and 155$\times$ over a single CPU core on the V100, A100, and MI300X, respectively, and 12$\times$, 17$\times$, and 16.6$\times$ over 72 host threads. Furthermore DetermineElite, the $O(N_p)$ selection of the generation-best chromosome, is found to be better suited to the host than to the device.
