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There has been growing interest in applying the DEM (discrete element method) to study the charging and burden distribution in a BF (blast furnace). In practice, the real particles in a BF are non-spherical. However, spherical particles have mostly been used in previous DEM investigations. Furthermore, various particle models have been developed to describe non-spherical particles. However, the effects of using different particle models on particle behavior in a BF are still unclear. Therefore, a comparative study of how the particle shape model impacts the burden charging in a BF was conducted. Specifically, the DEM using a multi-sphere model, polyhedral model, and super-ellipsoid model was first established. Then, experiments and DEM simulations of the charging and burden distribution of non-spherical quartz sand particles in a lab-scale bell-less top BF were performed. The results indicated that the number of sub-spheres, the principle of creating the particle for multi-spheres, the number of planes for polyhedrons, and the shape indices for super-ellipsoids could all affect the accuracy and efficiency. Moreover, applying the super-ellipsoid model and multi-sphere model could achieve reasonable accuracy and efficiency, with the highest simulation accuracy for the polyhedral model but at the cost of a rather heavy computational burden.

A novel approach to generation of tiled code for arbitrarily nested loops is presented. It is derived via a combination of the polyhedral and iteration space slicing frameworks. Instead of program transformations represented by a set of affine functions, one for each statement, it uses the transitive closure of a loop nest dependence graph to carry out corrections of original rectangular tiles so that all dependences of the original loop nest are preserved under the lexicographic order of target tiles. Parallel tiled code can be generated on the basis of valid serial tiled code by means of applying affine transformations or transitive closure using on input an inter-tile dependence graph whose vertices are represented by target tiles while edges connect dependent target tiles. We demonstrate how a relation describing such a graph can be formed. The main merit of the presented approach in comparison with the well-known ones is that it does not require full permutability of loops to generate both serial and parallel tiled codes; this increases the scope of loop nests to be tiled.

In a recent work, full convexity has been proposed as an alternative definition of digital convexity. It solves many problems related to its usual definitions, for instance: Fully convex sets are digitally convex in the usual sense, but are also connected and simply connected. However, full convexity is not a monotone property; hence, intersections of fully convex sets may be neither fully convex nor connected. This defect might forbid digital polyhedral models with fully convex faces and edges. This can be detrimental since classical standard and naive planes are fully convex. In this paper, we study several methods that builds a fully convex set from a digital set. One is particularly appealing and is based on an iterative process: This envelope operator solves in arbitrary dimension the problem of extending a digital set into a fully convex set, while leaving fully convex sets invariant. This extension naturally leads to digital polyhedra whose cells are fully convex. Then a relative envelope operator is proposed, which can be used to force digital planarity of fully convex sets. We provide experiments showing that our method produces coherent polyhedral models for any polyhedron in arbitrary dimension. Finally we study how we can speed up full convexity checks and envelope operations, with a worst-case complexity lowered by a factor 2 d in Z d .

This paper presents a model-based approach for reconstructing 3D polyhedral building models from aerial images. The proposed approach exploits some geometric and photometric properties resulting from the perspective projection of planar structures. Data are provided by calibrated aerial images. The novelty of the approach lies in its featurelessness and in its use of direct optimization based on image rawbrightness. The proposed framework avoids feature extraction and matching. The 3D polyhedral model is directly estimated by optimizing an objective function that combines an image-based dissimilarity measure and a gradient score over several aerial images. The optimization process is carried out by the Differential Evolution algorithm. The proposed approach is intended to provide more accurate 3D reconstruction than feature-based approaches. Fast 3D model rectification and updating can take advantage of the proposed method. Several results and evaluations of performance from real and synthetic images show the feasibility and robustness of the proposed approach.

This paper proposes a high-type control loop design method for LQR-LMI based on Lyapunov and polyhedral model theory. The high-type control loop design problem is simplified into a convex constraint problem, which achieves superior tracking performance. In this framework, the input amplitude of the control signal, the poles of the closed-loop system, the suppression of external interference and the perturbation of internal parameters are considered, and the linear matrix inequality (LMI) method is effectively used to solve the problems. In this paper, the polyhedral model control theory is introduced to characterize the uncertainty of the system for the change of model parameters of the controlled plant. Aiming at the problem of external disturbance suppression, the H2/H∞ control method is introduced into the system. These control methods provide the basis for the design of the high-type control loop. Compared with the simulation results of other optimization algorithms, the effectiveness and superiority of the controller parameter tuning rules in the proposed high-type control loop are verified.

Purpose This study aims to structure a model for integrating social value into strategic management based on identifying the critical success factors (CSF) for such integration in the investigated companies. Design/methodology/approach This research was based on the actor–network theory. Through a rigorous approach to the case study methodology in a two-stage process lasting 21 months, we carried out this study. Findings Companies that use the polyhedral social accounting model in their strategic management processes do so without a reference model. We identified CSF for integrating social value, which was incorporated into a protocol model based on stakeholder theory and the use of social accounting. Practical implications Practitioners can use the proposed model to maintain the alignment of strategic performance and purpose. Using social accounting based on indicators and financial proxies allows managers to incorporate social value into strategic management in terms of financial value. Social implications The institutional demand for social information is based on the growing sensitivity of companies. Aligning social values with business strategies contributes to social sustainability. Originality/value This study focuses on an unresearched emerging phenomenon. Since the first approach to stakeholder theory, the development of a stakeholder-oriented strategy has faced the lack of a stakeholder accounting system. The polyhedral model of social accounting could help overcome this problem as it provides information that allows a novel and innovative method to make a stakeholder-oriented strategy effective.

In this article, we analyze the program codes generated automatically using three advanced optimizers: Pluto, Traco, and Dapt, which are specifically tailored for the NPDP benchmark set. This benchmark set comprises ten program loops, predominantly from the field of bioinformatics. The codes exemplify dynamic programming, a challenging task for well-known tools used in program loop optimization. Given the intricacy involved, we opted for three automatic compilers based on the polyhedral model and various loop-tiling strategies. During our evaluation of the code’s performance, we meticulously considered locality and concurrency to accurately estimate time and energy efficiency. Notably, we dedicated significant attention to the latest Dapt compiler, which applies space–time loop tiling to generate highly efficient code for the NPDP benchmark suite loops. By employing the aforementioned optimizers and conducting an in-depth analysis, we aim to demonstrate the effectiveness and potential of automatic transformation techniques in enhancing the performance and energy efficiency of dynamic programming codes.

Tiling is a crucial program transformation, adjusting the ops-to-bytes balance of codes to improve locality. Like parallelism, it can be applied at multiple levels. Allowing tile sizes to be symbolic parameters at compile time has many benefits, including efficient autotuning, and run-time adaptability to system variations. For polyhedral programs, parametric tiling in its full generality is known to be non-linear, breaking the mathematical closure properties of the polyhedral model. Most compilation tools therefore either perform fixed size tiling, or apply parametric tiling in only the final, code generation step. We introduce monoparametric tiling, a restricted parametric tiling transformation. We show that, despite being parametric, it retains the closure properties of the polyhedral model. We first prove that applying monoparametric partitioning (i) to a polyhedron yields a union of polyhedra with modulo conditions, and (ii) to an affine function produces a piecewise-affine function with modulo conditions. We then use these properties to show how to tile an entire polyhedral program. Our monoparametric tiling is general enough to handle tiles with arbitrary tile shapes that can tesselate the iteration space (e.g., hexagonal, trapezoidal, etc). This enables a wide range of polyhedral analyses and transformations to be applied.

The need for parallel task execution has been steadily growing in recent years since manufacturers mainly improve processor performance by increasing the number of installed cores instead of scaling the processor’s frequency. To make use of this potential, an essential technique to increase the parallelism of a program is to parallelize loops. Several automatic loop nest parallelizers have been developed in the past such as PluTo. The main restriction of these tools is that the loops must be statically analyzable which, among other things, disallows function calls within the loops. In this article, we present a seemingly simple extension to the C programming language which marks functions without side-effects. These functions can then basically be ignored when the automatic parallelizer checks the parallelizability of loops. We integrated the approach into the GCC compiler toolchain and evaluated it by running several real-world applications. Our experiments show that the C extension helps to identify additional parallelization opportunities and, thus, to significantly increase the performance of applications.

In many cases, applications are not optimized for the hardware on which they run. Several reasons contribute to this unsatisfying situation, such as legacy code, commercial code distributed in binary form, or deployment on compute farms. In fact, backward compatibility of ISA guarantees only the functionality, not the best exploitation of the hardware. In this work, we focus on maximizing the CPU efficiency for the SIMD extensions. The first contribution was originally published in the International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation, SAMOS XV, July 2015, Agios Konstantinos, Greece. It is a binary-to-binary optimization framework where loops vectorized for an older version of the processor SIMD extension are automatically converted to a newer one. It is a lightweight mechanism that does not include a vectorizer, but instead leverages what a static vectorizer previously did. We show that many loops compiled for x86 SSE can be dynamically converted to the more recent and more powerful AVX; as well as, how correctness is maintained with regards to challenges such as data dependencies and reductions. We obtain speedups in line with those of a native compiler targeting AVX. The second contribution is the runtime vectorization of loops in binary codes that were not originally vectorized. For this purpose, we use open source frameworks that we have tuned and integrated to (1) dynamically lift the x86 binary into the Intermediate Representation form of the LLVM compiler, (2) abstract hot loops in the polyhedral model, (3) use the power of this mathematical framework to vectorize them, and (4) finally compile them back into executable form using the LLVM Just-In-Time compiler. In most cases, the obtained speedups are close to the number of elements that can be simultaneously processed by the SIMD unit. The re-vectorizer and auto-vectorizer are implemented inside a dynamic optimization platform; it is completely transparent to the user, does not require any rewriting of the binaries, and operates during program execution.