Load balancing is a crucial aspect of high-performance computing (HPC) systems that allows for the equitable distribution of computational tasks across available processors. As we move towards exascale computing, effective load balancing becomes even more critical to leverage the full potential of HPC systems.
To address this challenge, a recent study by the Department of Physics, Durham University, School of Computing, Newcastle University, and Institute for Multiscale Thermofluids, School of Engineering, The University of Edinburgh explores the application of quantum annealing to load balance two paradigmatic algorithms in high performance computing – adaptive mesh refinement and smoothed particle hydrodynamics.
Introduction to this study
In the study, the researchers found that quantum annealing outperforms classical methods such as the round-robin protocol in a grid-based context, but lacks a decisive advantage over more advanced methods such as steepest descent or simulated annealing. However, for the more complex particle formulation, approached as a multi-objective optimization, quantum annealing solutions are demonstrably Pareto dominant to state-of-the-art classical methods across both objectives.
Challenges
The primary obstacle to scalability is found to be limited coupling on current quantum annealing hardware. Despite this limitation, the results indicate a noteworthy advancement in solution quality, which can have a significant impact on effective CPU usage.
The study delves into the potential of quantum computing to address the challenges of load balancing, with a particular focus on the quantum annealing (QA) approach for each of the two cases described above. QA is particularly suited for finding the ground state of an Ising problem, which is essentially analogous to finding the optimal solution to many binary combinatorial optimization problems of interest.
Quantum Annealing Approach
Quantum annealing is a computational technique that leverages quantum effects to solve optimization problems. It’s a specialized form of quantum computing designed specifically to find the global minimum of a given objective function, which represents the solution to an optimization problem. The process is inspired by classical annealing, a technique used in metallurgy to achieve low-energy states in physical systems.
In quantum annealing, a system of qubits (quantum bits) is initialized to represent a problem’s configuration. These qubits interact with each other and with an external environment, typically through a magnetic field, to evolve towards the optimal solution of the problem. The evolution is guided by a quantum Hamiltonian, which is gradually transformed into the problem Hamiltonian encoding the optimization problem. The system’s evolution ideally leads to a state representing the optimal solution, found by minimizing the energy of the system.
Quantum annealing hardware offers several potential benefits:
Parallelism: Quantum annealing systems can explore multiple potential solutions simultaneously due to quantum superposition, potentially leading to faster exploration of the solution space compared to classical optimization methods.
Tunneling: Quantum annealing exploits quantum tunneling phenomena, allowing the system to traverse energy barriers that would be insurmountable for classical systems. This enables the exploration of a broader solution space.
Low Energy Consumption: Quantum annealing hardware, if properly designed and operated, can offer energy-efficient solutions for certain optimization problems, particularly those that are challenging for classical computers.
Solving Combinatorial Optimization Problems: Quantum annealing excels at solving combinatorial optimization problems, where the goal is to find the best solution among a vast number of possible combinations. This makes it potentially valuable for various real-world applications such as logistics, finance, and materials science.
Adiabaticity: Ideally, quantum annealing is an adiabatic process, meaning the system remains in its ground state throughout the evolution, guaranteeing the solution’s optimality under certain conditions.
However, it’s important to note that current quantum annealing hardware, such as D-Wave’s systems, still faces several challenges, including qubit coherence times, gate errors, and limited connectivity between qubits. As a result, quantum annealing is not universally superior to classical optimization methods and is most effective for certain classes of problems. Ongoing research and development aim to address these challenges and expand the capabilities of quantum annealing hardware.
Some proposals
The study proposes integrating quantum annealers with classical HPC systems, mirroring the synergy between GPUs and CPUs and aligning well with the trend towards diversifying and optimizing computational resources in HPC environments.
Complete study
To read the full study, please refer to the attached PDF file.
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