Types de donnée du langage C

Résumé

Les types de donnée du langage C définissent les caractéristiques de stockage et les opérations disponibles pour chaque valeur et chaque variable d'un code source en langage C. Les types fondamentaux du langage C sont conçus pour pouvoir correspondre aux types supportés par l'architecture de processeur cible. Le langage C possède une vingtaine de types fondamentaux pour représenter des nombres naturels, entiers et réels. Le langage offre une syntaxe pour construire des types d'adresse mémoire (pointeurs) vectoriels (tableaux) et composés (structures). En outre, la bibliothèque standard du C fournit des types supplémentaires qui permettent de s'abstraire des types liés aux caractéristiques techniques de l'ordinateur cible. Types standards Types principaux Le langage C fournit quatre spécificateurs arithmétiques de base char, int, float et double ainsi que leurs versions modifiés signed, unsigned, short et long. Le tableau suivant liste les combinaisons et la plag

Source officielle

https://fr.wikipedia.org/wiki/Types_de_donn%C3%A9e_du_langage_C

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Chargement

Integration of scientific and social networks

Ehsaneddin Asgari

In this paper, we address the problem of scientific-social network integration to find a matching relationship between members of these networks (i.e. The DBLP publication network and the Twitter social network). This task is a crucial step toward building a multi environment expert finding system that has recently attracted much attention in Information Retrieval community. In this paper, the problem of social and scientific network integration is divided into two sub problems. The first problem concerns finding those profiles in one network, which presumably have a corresponding profile in the other network and the second problem concerns the name disambiguation to find true matching profiles among some candidate profiles for matching. Utilizing several name similarity patterns and contextual properties of these networks, we design a focused crawler to find high probable matching pairs, then the problem of name disambiguation is reduced to predict the label of each candidate pair as either true or false matching. Because the labels of these candidate pairs are not independent, state-of-the-art classification methods such as logistic regression and decision tree, which classify each instance separately, are unsuitable for this task. By defining matching dependency graph, we propose a joint label prediction model to determine the label of all candidate pairs simultaneously. Two main types of dependencies among candidate pairs are considered for designing the joint label prediction model which are quite intuitive and general. Using the discriminative approaches, we utilize various feature sets to train our proposed classifiers. An extensive set of experiments have been conducted on six test collection collected from the DBLP and the Twitter networks to show the effectiveness of the proposed joint label prediction model.

Springer2014

Chargement

Holistic, Efficient, and Real-time Cleaning of Heterogeneous Data

Styliani Asimina Giannakopoulou

Data cleaning has become an indispensable part of data analysis due to the increasing amount of dirty data. Data scientists spend most of their time preparing dirty data before it can be used for data analysis. Existing solutions that attempt to automate the data cleaning procedure treat data cleaning as a separate offline process that takes place before analysis begins, while also focusing on a specific use-case. In addition, when the analysis involves complex non-relational data such as graphs, data cleaning becomes more challenging as it involves expensive operations. Therefore, offline, specialized cleaning tools exhibit long running times or fail to process large datasets. At the same time, applying data cleaning before analysis starts assumes a priori knowledge of the inconsistencies and the query workload, thereby requiring effort on understanding and cleaning data that is unnecessary for the analysis. Therefore, from a user's perspective, one is forced to use a different, potentially inefficient tool for each category of errors.In this thesis we aim for coverage and efficiency of data cleaning. We design and build data cleaning systems that employ high-level abstractions to (a) represent and optimize different cleaning operations for data of various formats, and (b) allow for real-time data cleaning that is relevant to data analysis.We introduce CleanM, a language that can express multiple types of cleaning operations. CleanM goes through a three-level translation process for optimization purposes; a different family of optimizations is applied in each abstraction level. Thus, CleanM can express complex data cleaning tasks, optimize them in a unified way, and deploy them in a scaleout fashion.To further reduce the data-to-insight time, we propose an approach that performs probabilistic repair of denial constraint violations on-demand, driven by the exploratory analysis that users perform. We introduce Daisy, a system that seamlessly integrates data cleaning into the analysis by relaxing query results. Daisy executes analytical query-workloads over dirty data by weaving cleaning operators into the query plan.To cover complex data such as graphs, we optimize the building block of data cleaning operations on graph data, that is subgraph matching. Subgraph matching constitutes the main bottleneck when cleaning graph data as it is an NP-complete problem. To optimize subgraph matching, we present a scale-up, radix-based algorithm that starts from an arbitrary partitioning of the graph and coordinates parallel pattern matching to eliminate redundant work among the workers. To address load imbalance, we employ a work stealing technique, specific to the subgraph matching problem. Worker threads steal work from straggler threads by using a heuristic that maximizes the work stolen, while at the same time preserves the order of evaluating candidate vertices.Overall, instead of using offline, specialised tools, this thesis designs abstractions that optimize different cleaning primitives over heterogeneous data, while also integrating data cleaning tasks seamlessly into data analysis. Specifically, we provide (a) a declarative high-level interface backed by an optimizable query calculus and (b) the required optimizations at the underlying data cleaning layers while also taking into consideration the exploratory analysis that users perform.

EPFL2021

Chargement

, ,

The advent of the quantum computer makes current public-key infrastructure insecure. Cryptography community is addressing this problem by designing, efficiently implementing, and evaluating novel public-key algorithms capable of withstanding quantum computational power. Governmental agencies, such as NIST, are promoting standardization of quantum-resistant algorithms that is expected to run for 7 years. Several modern applications must maintain permanent data secrecy; therefore, they ultimately require the use of quantum-resistant algorithms. Because algorithms are still under scrutiny for eventual standardization, the deployment of the hardware implementation of quantum-resistant algorithms is still in early stages. In this article, we propose a methodology to design programmable hardware accelerators for lattice-based algorithms, and we use the proposed methodology to implement flexible and energy efficient post-quantum cache-based accelerators for NewHope, Kyber, Dilithium, Key Consensus from Lattice (KCL), and R.EMBLEM submissions to the NIST standardization contest. To the best of our knowledge, we propose the first efficient domain-specific, programmable cache-based accelerators for lattice-based algorithms. We design a single accelerator for a common kernel among various schemes with different kernel sizes, i.e., loop count, and data types. This is in contrast to the traditional approach of designing one special purpose accelerators for each scheme. We validate our methodology by integrating our accelerators into an HLS-based SoC infrastructure based on the X86 processor and evaluate overall performance. Our experiments demonstrate the suitability of the approach and allow us to collect insightful information about the performance bottlenecks and the energy efficiency of the explored algorithms. Our results provide guidelines for hardware designers, highlighting the optimization points to address for achieving the highest energy minimization and performance increase. At the same time, our proposed design allows us to specify and execute new variants of lattice-based schemes with superior energy efficiency compared to the main application processor without changing the hardware acceleration platform. For example, we manage to reduce the energy consumption up to 2.1x and energy-delay product (EDP) up to 5.2x and improve the speedup up to 2.5x.

ASSOC COMPUTING MACHINERY2020

Chargement

Holistic, Efficient, and Real-time Cleaning of Heterogeneous Data

Styliani Asimina Giannakopoulou

EPFL2021

, ,

ASSOC COMPUTING MACHINERY2020