Quantum computers are increasingly being integrated into classical computing environments, particularly within supercomputing and data centers, where they serve as accelerators for solving a wide range of specific problems. Previous research has indicated that quantum computers have the potential to resolve optimization problems with theoretically lower computational complexity than classical algorithms. However, in practical scenarios, even minor instances of these problems often require substantial computational resources, enhanced quality quantum hardware, and the results may not always be optimal. In this paper, we highlight the importance of hybrid systems, combining quantum and classical computations to address growing problem sizes and computational demands. We focus on optimization problems using hardware and software-enhanced quantum computers operational within a novel quantum-classical hybrid setup, incorporating GPUs and photonic quantum computers. The study specifically addresses combinatorial optimization problems, such as the Max-Cut and the Job Shop Scheduling Problem, in addition to selected machine learning classification use cases. Notably, the quantum algorithm in Max-Cut optimization outperformed complete solution searches, especially for larger problem instances. For the Job-Shop Scheduling Problem, we made a significant advancement by successfully solving substantially larger instances compared to our previous previous work. Furthermore, selected hybrid neural networks incorporating quantum layers showed improved stability, though without a clear quality advantage over classical models. The paper also highlights the rapid progress and technological achievements in both hardware and software used in near-term photonic quantum computers, suggesting a promising future for quantum-classical hybrid systems in useful applications.

Tactical deconfliction problem involves resolving conflicts between aircraft to ensure safety while maintaining efficient trajectories. Several techniques exist to safely adjust aircraft parameters such as speed, heading angle, or flight level, with many relying on mixed-integer linear or nonlinear programming. These techniques, however, often encounter challenges in real-world applications due to computational complexity and scalability issues. This paper proposes a new quantum approach that applies the Quantum Approximate Optimization Algorithm (QAOA) and the Quantum Alternating Operator Ansatz (QAOAnsatz) to address the aircraft deconfliction problem. We present a formula for designing quantum Hamiltonians capable of handling a broad range of discretized maneuvers, with the aim of minimizing changes to original flight schedules while safely resolving conflicts. Our experiments show that a higher number of aircraft poses fewer challenges than a larger number of maneuvers. Additionally, we benchmark the newest IBM quantum processor and show that it successfully solves four out of five instances considered. Finally, we demonstrate that incorporating hard constraints into the mixer Hamiltonian makes QAOAnsatz superior to QAOA. These findings suggest quantum algorithms could be a valuable algorithmic candidate for addressing complex optimization problems in various domains, with implications for enhancing operational efficiency and safety in aviation and other sectors.

The Job Shop Scheduling Problem (JSSP) has always been considered as one of the most complex and industry essential scheduling problems. Optimizing the makespan of a given schedule generally involves using dedicated algorithms, local search strategies, or metaheuristics. These approaches, however, heavily rely on classical computational power, which is bounded by the physical limits of microcontrollers and power issues. Inspired by the promising results achieved for Quantum Annealing (QA) based approaches to solve JSSP instances, we propose a new approach that uses gate-model quantum architecture as an alternative to QA. We find that we can make use of the time-indexed JSSP instance representation to build a cost Hamiltonian, which can be embedded into Quantum Approximate Optimization Algorithm (QAOA) to find an optimal solution to a basic JSSP instance. We demonstrate the use of QAOA to solve the JSSP, and we evaluate its efficiency and accuracy for this problem from experimental results, as there is an increased urgency to demonstrate the applicability of quantum optimization algorithms. We also find that optimal variational parameters form patterns that can facilitate computation in bigger quantum circuits. Additionally, we compare the obtained noiseless simulation results of gate-model quantum cir-cuits demonstrating the relationship between two evaluation criteria -makespan and energy. Finally, we analyze and present the overall performance of our approach with the increasing deadline and simulated depth of QAOA circuits.(c) 2023 Poznan Supercomputing and Networking Center IBCH PAS. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Quantum computing is a rapidly developing technology that, in theory, can solve complex computational problems practically intractable for classical computers. Although the technology offers promising breakthroughs, it is only in the early stages of development, and various quantum computer architectures are emerging. One such new development is the photonic quantum computer. Since the work on discrete optimization using different quantum computer architectures is well studied, in this paper, we experiment with solving a toy instance of the Job-Shop Scheduling problem using a hybrid learning algorithm on a photonic quantum computer simulator. The promising results, combined with some highly desirable properties of photonic quantum computers, show that this new architecture is worth considering for further development and investment in the quantum technology landscape.

In this paper, we consider the problem of scheduling network tasks that have to be performed in a given computer network. Each network task consists of the transmission of data files between two nodes of the network and has to be performed in an a priori known time window in such a way that the energy consumed by the network equipment during the transmission of all the considered tasks is minimized. The entire mathematical model of this scheduling problem is proposed. Some results of computational experiments are also presented to show energy savings achieved through implementation of the proposed model in the interdata center network.

Energy and performance improvements in stencil computations are relevant for both application developers and data center administrators. They appear as the fundamental scheme in many large-scale scientific simulations and workloads. Many research efforts have focused on some estimation techniques of the energy usage of HPC systems based on specific characteristics of parallel applications. In case of stencils, we have previously concentrated on detailed estimations of energy consumption and the energy-aware distribution of stencil computations on heterogeneous processors. However, we have restricted our comprehensive studies to a single heterogeneous computing node only. In this paper, we show how scheduling and optimization techniques can be applied for energy and performance improvements of stencil computations on multi-node HPC systems using different network topologies. We formulate a scheduling model together with a new Tabu Search algorithm, called Task Movement (TM), taking into account the communication hierarchies, to minimize the overall energy usage and the execution time of stencil computations. Experimental studies show that this algorithm solves the considered problem more efficiently comparing to other, simpler heuristics. We present computational experiments for a reference 7 point stencil computation pattern on three commonly used low-diameter network topologies: Fat-tree, Dragonfly, and Torus. According to our studies, the most promising multi-node HPC architecture for stencil computations is based on the Torus network concept. Finally, we argue that the proposed scheduling model and TM algorithm can be easily adopted within existing high-level parallel execution environments for stencils automatic performance tuning.

Validation, verification, and uncertainty quantification (VVUQ) of simulation workflows are essential for building trust in simulation results, and their increased use in decision‐making processes. The EasyVVUQ Python library is designed to facilitate implementation of advanced VVUQ techniques in new or existing workflows, with a particular focus on high‐performance computing, middleware agnosticism, and multiscale modeling. Here, the application of EasyVVUQ to five very diverse application areas is demonstrated: materials properties, ocean circulation modeling, fusion reactors, forced human migration, and urban air quality prediction.

The amount of big data collected during human–computer interactions requires natural language processing (NLP) applications to be executed efficiently, especially in parallel computing environments. Scalability and performance are critical in many NLP applications such as search engines or web indexers. However, there is a lack of mathematical models helping users to design and apply scheduling theory for NLP approaches. Moreover, many researchers and software architects reported various difficulties related to common NLP benchmarks. Therefore, this paper aims to introduce and demonstrate how to apply a scheduling model for a class of keyword extraction approaches. Additionally, we propose methods for the overall performance evaluation of different algorithms, which are based on processing time and correctness (quality) of answers. Finally, we present a set of experiments performed in different computing environments together with obtained results that can be used as reference benchmarks for further research in the field.

We consider a problem of scheduling independent preemptable jobs on parallel identical machines to minimize a makespan. The processing rate of a job depends on a temporal allocation of limited power. Moreover, we assume that energy consumption is also constrained. Thus power/energy is treated as a doubly-constrained resource. In consequence the problem is to find a sequence of jobs on machines and simultaneously to determine an allocation of power to jobs that lead to a feasible schedule of minimal length. A complex non-linear mathematical programming problem has to be solved in a general case. In this paper we propose a solution procedure that utilizes an observation that usually only one limit for doubly-constrained resource is active in an optimal schedule.

In many distributed computing systems, aspects related to security are getting more and more relevant. Security is ubiquitous and could not be treated as a separated problem or a challenge. In our opinion it should be considered in the context of resource management in distributed computing environments like Grids and Clouds, e.g. scheduled computations can be much delayed because of cyber-attacks, inefficient infrastructure or users valuable and sensitive data can be stolen even in the process of correct computation. To prevent such cases there is a need to introduce new evaluation metrics for resource management that will represent the level of security of computing resources and more broadly distributed computing infrastructures. In our approach, we have introduced a new metric called reputation, which simply determines the level of reliability of computing resources from the security perspective and could be taken into account during scheduling procedures. The new reputation metric is based on various relevant parameters regarding cyber-attacks (also energy attacks), administrative activities such as security updates, bug fixes and security patches. Moreover, we have conducted various computational experiments within the Grid Scheduling Simulator environment (GSSIM) inspired by real application scenarios. Finally, our experimental studies of new resource management approaches taking into account critical security aspects are also discussed in this paper.

In this paper, discrete-continuous project scheduling problems with preemptable activities are considered. In these problems, activities of a project simultaneously require discrete and continuous resources for their execution. The activities are preemptable, and the processing rate of each activity is a continuous, increasing function of the amount of a single continuous resource allotted to the activity at a time. The problem is to find a precedence- and discrete resource-feasible schedule and, simultaneously, continuous resource allocation that would minimize the project duration. Convex and concave processing rate functions are considered separately. We show that for convex functions the problem is simple, whereas for concave functions a special methodology has to be developed. We discuss the methodology for three cases of the problem: no discrete resource constraints, one discrete resource being a set of parallel, identical machines, and an arbitrary number of discrete resources. In each case we analyze separately independent and precedence- related activities. Some conclusions and directions for future research are given.

Performance of high-end supercomputers will reach the exascale through the advent of core counts in billions. However, in the upcoming exascale computing era it is important not only to focus on the performance, but also on scalability of fine-grained parallel applications, data locality and energy aware scheduling within the parallel code. In fact, parallel applications need to change even now by redesigning algorithms and data structures respectively to take advantage of the recent improvements in energy efficiency of heterogeneous computing hardware, including multicore processors and GPU accelerators. Over the next few years one of the biggest challenges for exascale will be the ability of parallel applications to fully exploit locality which will, in turn, be required to achieve expected performance and energy efficiency. Future highly parallel applications will have to deal with deep memory hierarchies taking into account energy cost in moving data off-chip. Therefore, they will have to apply new coordinated scheduling approaches to balance energy aware resource utilization and minimize work starvation during runtime. As new constraints and limits on memory bandwidth and energy will play a key role in high performance computing (HPC) in the future, more sophisticated and dynamic scheduling techniques will be needed and applied within the parallel code. In this paper we focus on an energy-aware distribution of the stencil workload on heterogeneous processors. Our analysis of energy and performance models focused on relevant class of stencil computations to explore the relationship between task scheduling algorithms and energy constraints. More precisely, we search for a schedule which minimizes the energy usage within a specified computation's deadline of the stencil workload on heterogeneous architectures. Since the problem is computationally intractable, we present an integer linear programming formulation for finding optimal schedules. As finding optimal schedules is time consuming we have developed four heuristics and tested them experimentally with respect to optimal solutions. In our work we focus on a single node configurations with heterogeneous processors. These configurations represent the state of the art multi- and many-core architectures.

In the paper a power-aware problem of scheduling preemptable jobs on parallel identical machines to minimize the schedule length is considered. Exact approaches utilizing the idea of grouping jobs are presented and compared from the viewpoint of the size of an appropriate non-linear programming problem.

In this chapter project scheduling under an additional continuous resource is considered. In particular, we deal with discrete-continuous project scheduling problems to minimize the project duration. These problems are characterized by the fact that activities of a project simultaneously require discrete and continuous resources for their execution. A class of the problems is considered, where the number of discrete resources is arbitrary, and there is one continuous, renewable resource, whose total amount available at a time is limited. Activities are nonpreemptable, and the processing rate of an activity is a continuous, increasing function of the amount of the continuous resource allotted to the activity at a time. Theoretical results for the cases of convex and concave processing rate functions of activities are presented, and the methodology developed for solving the problems with concave functions is described in detail. Some conclusions and final remarks are given.