多样化自适应批量搜索：在多GPU上求解QUBO问题的框架
Diverse Adaptive Bulk Search: a Framework for Solving QUBO Problems on Multiple GPUs
AI总结：
本文提出DABS框架，一种基于遗传算法的多GPU QUBO求解器，通过多种搜索算法、遗传操作和解池的多样化策略，自动选择有效方法，在八块NVIDIA A100 GPU上成功找到三类QUBO问题的最优解。
摘要：
二次无约束二元优化（QUBO）是一种组合优化问题，旨在找到一个最优的二元解向量，该向量最小化由向量中二元变量的二次公式定义的能量值。由于许多NP难问题可以归约为QUBO问题，因此大量研究致力于开发在各种计算平台上运行的QUBO求解器，如量子设备、ASIC、FPGA、GPU和光纤。本文提出了一个名为多样化自适应批量搜索（DABS）的框架，该框架有潜力找到多种类型QUBO问题的最优解。我们的DABS求解器采用基于遗传算法的搜索算法，具有三种多样化策略：多种搜索算法、多种遗传操作和多个解池。在求解器执行过程中，成功找到良好解的搜索算法和遗传操作会被自动选择以获得更好的解。此外，搜索算法在不同解池之间遍历以找到良好解。我们已将DABS求解器实现为在多个GPU上运行。使用八块NVIDIA A100 GPU进行的实验评估证实，我们的DABS求解器成功找到了三类QUBO问题的最优解或潜在最优解。
英文摘要：
Quadratic Unconstrained Binary Optimization (QUBO) is a combinatorial optimization to find an optimal binary solution vector that minimizes the energy value defined by a quadratic formula of binary variables in the vector. As many NP-hard problems can be reduced to QUBO problems, considerable research has gone into developing QUBO solvers running on various computing platforms such as quantum devices, ASICs, FPGAs, GPUs, and optical fibers. This paper presents a framework called Diverse Adaptive Bulk Search (DABS), which has the potential to find optimal solutions of many types of QUBO problems. Our DABS solver employs a genetic algorithm-based search algorithm featuring three diverse strategies: multiple search algorithms, multiple genetic operations, and multiple solution pools. During the execution of the solver, search algorithms and genetic operations that succeeded in finding good solutions are automatically selected to obtain better solutions. Moreover, search algorithms traverse between different solution pools to find good solutions. We have implemented our DABS solver to run on multiple GPUs. Experimental evaluations using eight NVIDIA A100 GPUs confirm that our DABS solver succeeds in finding optimal or potentially optimal solutions for three types of QUBO problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
