OpenMP5 offload boosts quantum-inspired optimization across GPUs
Evaluation of portability and performance of an OpenMP5 offloaded Quantum-Inspired Evolutionary Optimization Across the GPU Ecosystem
Performance
Summary
Optimization algorithms inspired by quantum computing can solve complex problems by evolving solutions step-by-step. The authors tested whether a single OpenMP5 code could run efficiently on different high-end GPUs and CPUs without needing separate versions for each hardware type. They found that the GPU versions ran tens to over a hundred times faster than a single CPU core, though some tasks still work better on the CPU. This shows it is possible to write one code that performs well across major GPU platforms in real-world optimization.
What this means in practice
- •For high performance computing engineers: Use a single OpenMP5 program to accelerate quantum-inspired optimization on various GPUs, simplifying deployment across servers, cloud setups, and leadership-class accelerators.
- •For cloud platform operators: Provide optimized GPU resources capable of running a unified code for quantum-inspired metaheuristics to support efficient large-scale optimization workloads.
Authors
Kasturi Venkata Srikanth, Ashish Singh, Ferdin Sagai Don Bosco, Aman Mittal, Abhishek Singh, Aditya Singh, Abhishek Chopra
Abstract
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.