Learning Algorithms for Automatic Data Structure Design

Modern embedded systems are now capable of executing complex and demanding applications that are often based on large data structures. The design of the critical data structures of the application affects the performance and the memory requirements of the whole system. Dynamic Data Structure Refinement methodology provides optimizations, mainly in list and array data structures, which are based on the application's features and access patterns. In this work, we extend various aspects of the methodology: First, we integrate radix tree optimizations. Then, we provide a set of platform-aware data structure implementations, for performing optimizations based on the hardware features. The extended methodology is evaluated using a wide set of synthetic and real-world benchmarks, in which we achieved performance and memory trade-offs up to 29.6%. Additionally, Pareto optimal data structure implementations that were not available by the previous methodology, are identified with the extended one.

VECFEM is a black-box solver for the solution of a large class of nonlinear functional equations by finite element methods. It uses very robust solution methods for the linear FEM problem to compute reliably the Newton-Raphson correction and the error indicator. Kernel algorithms are conjugate gradient methods (CG) for the solution of the linear system. In this paper we present the optimal data structures on parallel computers for the matrix-vector multiplication, which is the key operation in the CG iteration, the principles of the element distribution onto the processors and the mounting of the global matrix over all processors as transformation of optimal data structures. VECFEM is portably implemented for message passing systems. Two examples with unstructured and structured grids will show the efficiency of the data structures.

Mesoscopic Traffic Simulation is an important tool in traffic analysis and traffic management support. The balance between traffic modeling details and performance has made Mesoscopic Traffic Simulation one of the key solutions for traffic controllers and policy makers. Mesoscopic traffic simulators offer acceptable speed in simulating normal traffic. However, when traffic prediction and optimization for large scale networks come into context, the performance of mesoscopic traffic simulators is unsatisfactory in optimizing a massive number of control parameters for a much longer prediction horizon. This issue again emphasizes the need to further speed up mesoscopic traffic simulation. This paper proposes a comprehensive framework to massively speed up mesoscopic traffic simulation using GPU without compromising its correctness and realistic modeling property. It also gives an in-depth analysis into the trade-off between simulation correctness and performance speedup. By combining the power of GPU with optimal design and data structures, mesoscopic traffic simulation is able to speed up to more than 6 times compared to original CPU implementation.

As the main content of the current research and discussion in the field of computer science, genetic algorithm has been applied more and more deeply in the field of architecture. Scholars from various countries have mastered a large number of application cases and theoretical knowledge. In the development of social economy and technology, the optimization design level of urban building structure is higher and higher. Optimization innovation based on genetic algorithm can effectively improve the speed of calculation and convergence performance. Therefore, on the basis of understanding the research status of genetic algorithm structure optimization design, this paper puts forward a structure optimization program with adaptive genetic algorithm as the core. The final experimental results show that this design can further improve the performance of the optimal design of the building structure, and meet the requirements of technical innovation in the field of modern architecture.

Aiming at the problems such as low efficiency of initial structure optimization design of traditional refractive optical system and overreliance on experience in structure selection. In this paper, an initial structure automatic optimization design method of refractive optical system based on deep learning is proposed. The structural characteristic data of the reference lens in the optical lens library are learned through supervised training. Unsupervised training model based on ray tracing is constructed to improve the generalization ability of deep neural network model. Through the network model generated by training, the optical system structure parameters including real glass are output, and the automatic optimal design of the initial structure of the refractive optical system is realized. The design results show that the initial structure spot radius of optical system in full field and full spectrum optimized by network model are close to the reference lens. The initial structure of the optical system can be designed according to different focal length requirements. The success rate of one million initial structures designed in this paper is greater than 96.403%, which indicates that the network model has good generalization ability. The method proposed in this paper contributes to automatically generate the initial structure of the refractive optical system rapidly and provides a new solution for the optimization of complex optical system.

Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems

Let $G=(V,E)$ be any undirected graph on $V$ vertices and $E$ edges. A path $\textbf{P}$ between any two vertices $u,v\in V$ is said to be $t$-approximate shortest path if its length is at most $t$ times the length of the shortest path between $u$ and $v$. We consider the problem of building a compact data structure for a given graph $G$ which is capable of answering the following query for any $u,v,z\in V$ and $t>1$. \centerline{\em report $t$-approximate shortest path between $u$ and $v$ when vertex $z$ fails} We present data structures for the single source as well all-pairs versions of this problem. Our data structures guarantee optimal query time. Most impressive feature of our data structures is that their size {\em nearly} match the size of their best static counterparts.

We construct nonblocking networks that are efficient not only as regards their cost and delay, but also as regards the time and space required to control them. In this paper we present the first simultaneous “weakly optimal” solutions for the explicit construction of nonblocking networks, the design of algorithms and data-structures. “Weakly optimal” is in the sense that all measures of complexity (size and depth of the network, time for the algorithm, space for the data-structure, and number of processor-time product) are within one or more logarithmic factors of their smallest possible values. In fact, we construct a scheme in which networks withn inputs andn outputs have sizeO(n(logn)2) and depthO(logn), and we present deterministic and randomized on-line parallel algorithms to establish and abolish routes dynamically in these networks. In particular, the deterministic algorithm usesO((logn)5) steps to process any number of transactions in parallel (with one processor per transaction), maintaining a data structure that useO(n(logn)2) words.

High level data structures are a cornerstone of modern programming and at the same time stand in the way of compiler optimizations. In order to reason about user- or library-defined data structures compilers need to be extensible. Common mechanisms to extend compilers fall into two categories. Frontend macros, staging or partial evaluation systems can be used to programmatically remove abstraction and specialize programs before they enter the compiler. Alternatively, some compilers allow extending the internal workings by adding new transformation passes at different points in the compile chain or adding new intermediate representation (IR) types. None of these mechanisms alone is sufficient to handle the challenges posed by high level data structures. This paper shows a novel way to combine them to yield benefits that are greater than the sum of the parts.Instead of using staging merely as a front end, we implement internal compiler passes using staging as well. These internal passes delegate back to program execution to construct the transformed IR. Staging is known to simplify program generation, and in the same way it can simplify program transformation. Defining a transformation as a staged IR interpreter is simpler than implementing a low-level IR to IR transformer. With custom IR nodes, many optimizations that are expressed as rewritings from IR nodes to staged program fragments can be combined into a single pass, mitigating phase ordering problems. Speculative rewriting can preserve optimistic assumptions around loops.We demonstrate several powerful program optimizations using this architecture that are particularly geared towards data structures: a novel loop fusion and deforestation algorithm, array of struct to struct of array conversion, object flattening and code generation for heterogeneous parallel devices. We validate our approach using several non trivial case studies that exhibit order of magnitude speedups in experiments.

A range of attacks on network components, such as algorithmic denial-of-service attacks and cryptanalysis via timing attacks, are enabled by data structures for which an adversary can predict the durations of operations that he will induce on the data structure. In this paper we introduce the problem of designing data structures that confound an adversary attempting to predict the timing of future operations he induces, even if he has adaptive and exclusive access to the data structure and the timings of past operations. We also design a data structure for implementing a set (supporting membership query, insertion, and deletion) that exhibits timing unpredictability and that retains its efficiency despite adversarial attacks. To demonstrate these advantages, we develop a framework by which an adversary tracks a probability distribution on the data structure’s state based on the timings it emitted, and infers invocations to meet his attack goals.KeywordsBinary Search TreeAverage EntropyAbstract Data TypeLinked ListUnpredictable TimingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Justice-centered approaches to equitable computer science (CS) education\nframe CS learning as a means for advancing peace, antiracism, and social\njustice rather than war, empire, and corporations. However, most research in\njustice-centered approaches in CS education focus on K-12 learning\nenvironments. In this position paper, we review justice-centered approaches to\nCS education, problematize the lack of justice-centered approaches to CS in\nhigher education in particular, and describe a justice-centered approach for\nundergraduate Data Structures and Algorithms. Our approach emphasizes three\ncomponents: (1) ethics: critiques the sociopolitical values of data structure\nand algorithm design as well as the underlying logics of dominant computing\nculture; (2) identity: draws on culturally responsive-sustaining pedagogies to\nemphasize student identity as rooted in resistance to the dominant computing\nculture; and (3) political vision: ensures the rightful presence of political\nstruggles by reauthoring rights to frame CS learning as a force for social\njustice. Through a case study of this Critical Comparative Data Structures and\nAlgorithms pedagogy, we argue that justice-centered approaches to higher CS\neducation can help all computing students not only learn about the ethical\nimplications of nominally technical concepts, but also develop greater respect\nfor diverse epistemologies, cultures, and experiences surrounding computing\nthat are essential to creating the socially-just worlds we need.\n

This study aims to demonstrate the application of big data models in the evaluation of manufacturing competitiveness across different countries and economic entities. It involves analyzing algorithms and data structures, designing algorithms for evaluating manufacturing competitiveness suitable for different countries and economic entities, and providing algorithmic and theoretical support for building computer assessment models for manufacturing competitiveness in economic entities. The research adopts qualitative and quantitative methods, comparing and analyzing empirical data on manufacturing resource endowment and competitiveness in various countries, combining theoretical analysis and empirical data. By conducting a literature review and empirical research, relevant information and data are collected to establish the relationship between manufacturing resource endowment index and competitiveness. Through case studies and comparative research, this study explores the main factors affecting manufacturing competitiveness in different countries and their interrelationships. It confirms that the competitiveness of manufacturing industries in different countries is influenced by factors such as resource endowment. Further analysis quantifies the relationship between national manufacturing resource endowment and competitiveness, leading to the design of computer algorithms, data structures, and processing logic. The results demonstrate the significant role of resource endowment in manufacturing competitiveness, serving as a crucial basis for formulating comprehensive national strategies. The innovations of this study include: 1. Introducing the manufacturing resource endowment index as a key variable for evaluating competitiveness, enriching the content of competitiveness big data models. 2. Designing comprehensive data structures, algorithms, and processing logic for assessing manufacturing competitiveness from multiple perspectives. 3. In-depth analysis of the relationship and analytical methods between manufacturing strategies, resource endowment, and competitiveness, providing theoretical and model references for the design of computer big data operation platforms.

Fixtures are tools to locate and securely hold workpieces for manufacturing operations such as inspection and machining. This paper presents a simple and complete algorithm for automatic and optimal fixture design on 3-D workpieces represented by discrete points. A new performance index is proposed to simultaneously address three fundamental requirements: form-closure constraint, minimum locating errors of the workpiece, and even load distribution among the fixturing points. To find the optimal future, the algorithm combines a local optimization procedure with a recursive problem decomposition strategv. The local optimization procedure searches for a solution in the direction of minimizing the performance index. When the performance index reaches a local minimum, the algorithm decomposes the discrete point set into subsets, and then carries out new rounds of local search in the subsets. The algorithm ends when a locally optimal solution is obtained. Compared to the exhaustive search, this algorithm is more efficient; and compared to other heuristic methods, the proposed algorithm is complete. The efficiency of this algorithm is demonstrated by numerical examples.

During the design optimization of the excavator boom, there are many design variables and complicated processes. The original optimization methods mainly focused on the optimization of mathematical models, and they lacked consideration in the use of domain knowledge, design-specification knowledge, expert experience knowledge, and historical examples. In order to comprehensively utilize the domain knowledge and expert experience knowledge, this study uses the optimization process analysis, uses knowledge expression and coding processing technology to encode the boom structure, builds an optimal design coding system based on knowledge guidance, and realizes the automatic optimization design of the boom structure. In the process of constructing the knowledge-oriented optimization system, to realize the reuse of the knowledge of the boom structure design in the numerical optimization iteration, a knowledge processing flowchart of the boom structure design is constructed. The concept of “shape distance” is proposed to judge the similarity feature matrix of the boom structure coding. To evaluate whether the stress distribution is uniform, a fast prediction model based on stress characteristic regions is constructed. The research results show that, under the comprehensive consideration of the four working conditions, the knowledge-guided optimization of the boom structure can avoid the deformity in the optimization process, accelerate the calculation speed of the optimization model, and improve the optimization quality of the model.

The fast development of electric vehicles (EVs) has resulted in several topics of research in this area, such as the development of a charging pricing strategy, charging control, location of the charging station, and the structure within the charging station. This paper proposes the optimal design of the structure of an EV fast-charging station (EVFCS) connected with a renewable energy source and battery energy storage systems (BESS) by using metaheuristic algorithms. The optimal design of this structure aims to find the number and power of chargers. Moreover, the renewable energy source and BESS can reduce the impact on the grid, so these energy sources are considered as ones of the optimally-designed structure of EVFCS in this work. Thus, it is necessary to determine the optimal sizing of the renewable energy source, BESS, and the grid power connected to EVFCS. This optimal structure can improve the profitability of the station. To solve the optimization problem, three metaheuristic algorithms, including particle swarm optimization (PSO), Salp swarm algorithm (SSA), and arithmetic optimization algorithm (AOA), are adopted. These algorithms aim to find the optimal structure which maximizes the profit of the EVFCS determined by its net present value (NPV), and the results obtained from these algorithms were compared. The results demonstrate that all considered algorithms could find the feasible solutions of the optimal design of the EVFCS structure where PSO provided the best NPV, followed by AOA and SSA.