Third-party Solvers Research Articles

Constraint Programming Approach to Coverage-Path Planning for Autonomous Multi-UAV Infrastructure Inspection

This article presents a constraint modeling approach to global coverage-path planning for linear-infrastructure inspection using multiple autonomous UAVs. The problem is mathematically formulated as a variant of the Min–Max K-Chinese Postman Problem (MM K-CPP) with multi-weight edges. A high-level constraint programming language is used to model the problem, which enables model execution with different third-party solvers. The optimal solutions are obtained in a reasonable time for most of the tested instances and different numbers of vehicles involved in the inspection. For some graphs with multi-weight edges, a time limit is applied, as the problem is NP-hard and the computation time increases exponentially. Despite that, the final total inspection cost proved to be lower when compared with the solution obtained for the unrestricted MM K-CPP with single-weight edges. This model can be applied to plan coverage paths for linear-infrastructure inspection, resulting in a minimal total inspection time for relatively simple graphs that resemble real transmission networks. For more extensive graphs, it is possible to obtain valid solutions in a reasonable time, but optimality cannot be guaranteed. For future improvements, further optimization could be considered, or different models could be developed, possibly involving artificial neural networks.

New Search Method for Sbox-Related Impossible Differentials

The impossible differential attack is one of the most fundamental tools of cryptanalysis and has been successfully applied to a large variety of block ciphers. In a typical impossible differential attack, the foundation and first step is to construct an impossible differential. Nowadays, two kinds of most commonly used approaches in impossible differential construction are the matrix-based and tool-aided automatic search methods. In this paper, we proposed a new search method combining an early-abort strategy with the guess-and-determine technique to find longer impossible differentials. Compared with the previous matrix-based search methods, ours has taken the details of Sbox into consideration, while compared with the tool-aided methods, ours is independent of a third-party solver and is applicable to ciphers with large (≥8 bits) Sboxes, which could be tough work for tool-aided methods to cover. Therefore, more accurate results could be obtained. To prove the effectiveness of our method, it is applied to CSA and FOX64. For CSA, 23/24/25-round impossible differentials were found for CSA-BC. This improved the longest impossible differential distinguisher so far by 1/2/3 rounds. And 25-round key recovery attacks are performed against CSA-BC and CSA, which are the longest impossible differential attack by now. For FOX64, we proved that an impossible differential for its round function always implies an impossible differential for 4-round FOX64, and new types of impossible differentials were able to be found. Our method has its own advantage in dealing with large Sboxes, and it may be a possible direction to search for Sbox-related impossible differentials.

Large-scale periodic scheduling in time-sensitive networks

The real-time and safety-critical demands posed in a variety of industrial systems can now be handled on the Ethernet architecture by virtue of the IEEE Time-Sensitive Networking task group. In particular, deterministic nature and timeliness guarantees can be achieved by time-triggered communication schedules that are enabled by the IEEE 802.1Qbv standard. However, generating such a schedule is a computationally challenging task due to several harsh constraints that must be satisfied. This problem is usually transformed into some existing formalism, e.g., Satisfiability Modulo Theories, and solved using a general third-party solver, which has the drawback of limited scalability. Except for some one-pass heuristics, such as the earliest deadline first or as-soon-as-possible scheduling, the current literature does not provide any algorithm tailored to this specific problem of scheduling time-triggered traffic in time-sensitive networks. In this paper, we develop an efficient algorithm that does not use any third-party solver and is based on the search in the space of partial schedules while being guided by the discovered conflicts. Our algorithm calculates schedules for problem instances consisting of 2000 network nodes and more than 10000 flows.

Implementing and Evaluating an Heterogeneous, Scalable, Tridiagonal Linear System Solver with OpenCL to Target FPGAs, GPUs, and CPUs

Solving diagonally dominant tridiagonal linear systems is a common problem in scientific high-performance computing (HPC). Furthermore, it is becoming more commonplace for HPC platforms to utilise a heterogeneous combination of computing devices. Whilst it is desirable to design faster implementations of parallel linear system solvers, power consumption concerns are increasing in priority. This work presents the oclspkt routine. The oclspkt routine is a heterogeneous OpenCL implementation of the truncated SPIKE algorithm that can use FPGAs, GPUs, and CPUs to concurrently accelerate the solving of diagonally dominant tridiagonal linear systems. The routine is designed to solve tridiagonal systems of any size and can dynamically allocate optimised workloads to each accelerator in a heterogeneous environment depending on the accelerator’s compute performance. The truncated SPIKE FPGA solver is developed first for optimising OpenCL device kernel performance, global memory bandwidth, and interleaved host to device memory transactions. The FPGA OpenCL kernel code is then refactored and optimised to best exploit the underlying architecture of the CPU and GPU. An optimised TDMA OpenCL kernel is also developed to act as a serial baseline performance comparison for the parallel truncated SPIKE kernel since no FPGA tridiagonal solver capable of solving large tridiagonal systems was available at the time of development. The individual GPU, CPU, and FPGA solvers of the oclspkt routine are 110%, 150%, and 170% faster, respectively, than comparable device-optimised third-party solvers and applicable baselines. Assessing heterogeneous combinations of compute devices, the GPU + FPGA combination is found to have the best compute performance and the FPGA-only configuration is found to have the best overall estimated energy efficiency.

Description and evaluation of the Community Ice Sheet Model (CISM) v2.1

Abstract. We describe and evaluate version 2.1 of the Community Ice Sheet Model (CISM). CISM is a parallel, 3-D thermomechanical model, written mainly in Fortran, that solves equations for the momentum balance and the thickness and temperature evolution of ice sheets. CISM's velocity solver incorporates a hierarchy of Stokes flow approximations, including shallow-shelf, depth-integrated higher order, and 3-D higher order. CISM also includes a suite of test cases, links to third-party solver libraries, and parameterizations of physical processes such as basal sliding, iceberg calving, and sub-ice-shelf melting. The model has been verified for standard test problems, including the Ice Sheet Model Intercomparison Project for Higher-Order Models (ISMIP-HOM) experiments, and has participated in the initMIP-Greenland initialization experiment. In multimillennial simulations with modern climate forcing on a 4 km grid, CISM reaches a steady state that is broadly consistent with observed flow patterns of the Greenland ice sheet. CISM has been integrated into version 2.0 of the Community Earth System Model, where it is being used for Greenland simulations under past, present, and future climates. The code is open-source with extensive documentation and remains under active development.

Pomodoro: A Novel Toolkit for Dynamic (MultiObjective) Optimization, and Model Based Control and Estimation

This paper presents the software package Pomodoro which includes a collection of algorithms and tools for dynamic optimization. It also introduces the framework for multiobjective problems, model predictive control and state estimation. Pomodoro is implemented in Python and utilizes CasADi as a backbone to formulate the problem. It uses orthogonal collocation technique to solve the dynamic optimization problems and efficient third-party solvers are employed to solve the resulting nonlinear programs. The design of the software and its main modules are discussed and the user-friendliness of the software is demonstrated with the help of tutorial examples for each problem class. Lastly, the advantages and limitations of this software are discussed.

Flexible job shop scheduling with lot streaming and sublot size optimisation

Models and optimisation approaches are developed for a flexible job shop scheduling problem with lot streaming and lot sizing of the variable sublots. A two-stage optimisation procedure is proposed. First, the makespan value is minimised with the smallest sublots defined for the problem instance. This makes it possible to shorten the makespan significantly, because each sublot is transferred separately to the next operation of a job. In the second stage, the sizes of the sublots are maximised without increasing the obtained makespan value. In this way, the quantity of sublots and transport activities is limited together with the related manufacturing cost. Two objectives are defined for the second stage. The first one is the maximisation of the sum of the sublot sizes of all operations, the second one is the maximisation of the number of the operations which do not need to be split at all. Mixed-integer linear programming, constraint programming and graph-based models are implemented for the problem. Two optimisation approaches are developed and compared in computational experiments for each stage and objective, one approach is based on a third-party solver, and the second one on an independent own implementation, namely a tabu search and a greedy constructive heuristic.

A matheuristic based on large neighborhood search for the vehicle routing problem with cross-docking

The vehicle routing problem with cross-docking (VRPCD) consists in defining a set of routes that satisfy transportation requests between a set of pickup points and a set of delivery points. The vehicles bring goods from pickup locations to a cross-docking platform, where the items may be consolidated for efficient delivery. In this paper we propose a new solution methodology for this problem. It is based on large neighborhood search and periodically solving a set partitioning and matching problem with third-party solvers. Our method improves the best known solution in 19 of 35 instances from the literature.

A unified approach to adapt scientific visualization systems to third-party solvers

This paper presents a new unified approach to adapt scientific visualization systems to third-party solvers implemented on different software and hardware platforms. This approach allows building multiplatform visualization systems, enables automatic conversion of input and output data from any solver into a rendering-compatible format, and provides real-time generation of high-quality images. The automated adaptation of visualization systems to third-party solvers is based on ontological engineering methods. Multiplatform portability is provided by the automatic generation of a graphical user interface (GUI) for each particular operating system and by preprocessing the data to be rendered by using heuristic-based tools, which ensures compatibility with different hardware and software platforms, including desktop computers and mobile devices. In addition, an original anti-aliasing algorithm is proposed to ensure high quality of resulting images. Based on the proposed approach, a multiplatform scientific visualization system called SciVi is developed, which is successfully used for solving various real-world scientific visualization problems from different application domains.

Using Scientific Visualization Tools to Bridge the Talent Gap

Abstract In this paper the use of adaptive scientific visualization tools in education, including in the area of high performance computing education is proposed in order to help students understand in depth the nature of particular scientific problems and to help them to learn parallel computing approaches to solving these problems. The proposed approach may help to bridge the talent gap in natural and computational sciences, since high quality visualization can help to uncover hidden regularities in the data with which the researchers and students work and can lead to new level of understanding how the data can be partitioned and processed in parallel. A multiplatform client-server scientific visualization system is presented that can be easily integrated with third-party solvers from any field of science. This system can be used as a visual aid and a collaboration tool in high performance computing education.

Parallel GMRES with a multiplicative Schwarz preconditioner

This paper presents a robust hybrid solver for linear systems that combines a Krylov subspace method as accelerator with a Schwarz-based preconditioner. This preconditioner uses an explicit formulation associated to one iteration of the multiplicative Schwarz method. The Newtonbasis GMRES, which aim at expressing a good data parallelism between subdomains is used as accelerator. In the first part of this paper, we present the pipeline parallelism that is obtained when the multiplicative Schwarz preconditioner is used to build the Krylov basis for the GMRES method. This is referred as the first level of parallelism. In the second part, we introduce a second level of parallelism inside the subdomains. For Schwarz-based preconditioners, the number of subdomains are keeped small to provide a robust solver. Therefore, the linear systems associated to subdomains are solved efficiently with this approach. Numerical experiments are performed on several problems to demonstrate the benefits of using these two levels of parallelism in the solver, mainly in terms of numerical robustness and global efficiency. Cet article présente un solveur hybride robuste pour des systèmes linéaires. Ce solveur parallèle construit un préconditionneur de type Schwarz pour accélerer une méthode basée sur les sous-espaces de Krylov. Le préconditionneur est défini à partir d’une formulation explicite correspondant à une itération de Schwarz multiplicatif. Dans le but de réduire les communications et les dépendences entre les sous-domaines, nous utilisons la version de GMRES qui dissocie la construction de la base de Krylov et son orthogonalisation. Nous présentons dans un premier temps le parallélisme qui est obtenu lorsque ce préconditionneur Schwarz multiplicatif est utilisé dans la construction de la base de Krylov. C’est le premier niveau de parallélisme. Dans la deuxième partie de ce travail, nous introduisons un deuxième niveau de parallélisme à l’intérieur de chaque sous-domaine. Pour des décompositions de domaines avec recouvrement, le nombre de sous-domaines doit rester faible pour fournir un solveur robuste. De ce fait, les systèmes linéaires associés aux sous-domaines sont résolus de manière efficace avec ce deuxième niveau de parallélisme. Plusieurs tests numériques sont présentés à la fin du document pour valider l’efficacité de cette approche.

ECLiPSe – From LP to CLP

AbstractECLiPSe is a Prolog-based programming system, aimed at the development and deployment of constraint programming applications. It is also used for teaching most aspects of combinatorial problem solving, for example, problem modelling, constraint programming, mathematical programming and search techniques. It uses an extended Prolog as its high-level modelling and control language, complemented by several constraint solver libraries, interfaces to third-party solvers, an integrated development environment and interfaces for embedding into host environments. This paper discusses language extensions, implementation aspects, components, and tools that we consider relevant on the way from Logic Programming to Constraint Logic Programming.

Scheduling Commercial Videotapes in Broadcast Television

This paper, motivated by the experiences of a major U.S.-based broadcast television network, presents algorithms and heuristics to schedule commercial videotapes. Major advertisers purchase several slots to air commercials during a given time period on a broadcast network. We study the problem of scheduling advertiser's commercials in the slots it purchased when the same commercial is to be aired multiple times. Under such a situation, the advertisers typically want the airings of a commercial to be as evenly spaced as possible. Thus, our objective is to schedule a set of commercials in a set of available slots such that multiple airings of the same commercial are as evenly spaced as possible. A natural formulation of this problem is a mixed-integer program that can be solved using third-party solvers. We also develop a branch-and-bound algorithm based on a problem-specific bounding scheme. Both approaches fail to solve larger problem instances within a reasonable time frame. We present an alternative mixed-integer program that lends itself to an efficient solution. For solving even larger problems, we present multiple heuristics.