1,720,966 research outputs found
Contributions sur les techniques de computation approximée et comment les mesurer
No full textLa Computation Approximée est basée dans l'idée que des améliorations significatives de l'utilisation du processeur, de l'énergie et de la mémoire peuvent être réalisées, lorsque de faibles niveaux d'imprécision peuvent être tolérés. C'est un concept intéressant, car le manque de ressources est un problème constant dans presque tous les domaines de l'informatique. Des grands superordinateurs qui traitent les big data d'aujourd'hui sur les réseaux sociaux, aux petits systèmes embarqués à contrainte énergétique, il y a toujours le besoin d'optimiser la consommation de ressources. La Computation Approximée propose une alternative à cette rareté, introduisant la précision comme une autre ressource qui peut à son tour être échangée par la performance, la consommation d'énergie ou l'espace de stockage. La première partie de cette thèse propose deux contributions au domaine de l'informatique approximative: Aproximate Loop Unrolling : optimisation du compilateur qui exploite la nature approximative des données de séries chronologiques et de signaux pour réduire les temps d'exécution et la consommation d'énergie des boucles qui le traitent. Nos expériences ont montré que l'optimisation augmente considérablement les performances et l'efficacité énergétique des boucles optimisées (150% - 200%) tout en préservant la précision à des niveaux acceptables. Primer: le premier algorithme de compression avec perte pour les instructions de l'assembleur, qui profite des zones de pardon des programmes pour obtenir un taux de compression qui surpasse techniques utilisées actuellement jusqu'à 10%. L'objectif principal de la Computation Approximée est d'améliorer l'utilisation de ressources, telles que la performance ou l'énergie. Par conséquent, beaucoup d'efforts sont consacrés à l'observation du bénéfice réel obtenu en exploitant une technique donnée à l'étude. L'une des ressources qui a toujours été difficile à mesurer avec précision, est le temps d'exécution. Ainsi, la deuxième partie de cette thèse propose l'outil suivant : AutoJMH : un outil pour créer automatiquement des microbenchmarks de performance en Java. Microbenchmarks fournissent l'évaluation la plus précis de la performance. Cependant, nécessitant beaucoup d'expertise, il subsiste un métier de quelques ingénieurs de performance. L'outil permet (grâce à l'automatisation) l'adoption de microbenchmark par des non-experts. Nos résultats montrent que les microbencharks générés, correspondent à la qualité des manuscrites par des experts en performance. Aussi ils surpassent ceux écrits par des développeurs professionnels dans Java sans expérience en microbenchmarking.Approximate Computing is based on the idea that significant improvements in CPU, energy and memory usage can be achieved when small levels of inaccuracy can be tolerated. This is an attractive concept, since the lack of resources is a constant problem in almost all computer science domains. From large super-computers processing today’s social media big data, to small, energy-constraint embedded systems, there is always the need to optimize the consumption of some scarce resource. Approximate Computing proposes an alternative to this scarcity, introducing accuracy as yet another resource that can be in turn traded by performance, energy consumption or storage space. The first part of this thesis proposes the following two contributions to the field of Approximate Computing :Approximate Loop Unrolling: a compiler optimization that exploits the approximative nature of signal and time series data to decrease execution times and energy consumption of loops processing it. Our experiments showed that the optimization increases considerably the performance and energy efficiency of the optimized loops (150% - 200%) while preserving accuracy to acceptable levels. Primer: the first ever lossy compression algorithm for assembler instructions, which profits from programs’ forgiving zones to obtain a compression ratio that outperforms the current state-of-the-art up to a 10%. The main goal of Approximate Computing is to improve the usage of resources such as performance or energy. Therefore, a fair deal of effort is dedicated to observe the actual benefit obtained by exploiting a given technique under study. One of the resources that have been historically challenging to accurately measure is execution time. Hence, the second part of this thesis proposes the following tool : AutoJMH: a tool to automatically create performance microbenchmarks in Java. Microbenchmarks provide the finest grain performance assessment. Yet, requiring a great deal of expertise, they remain a craft of a few performance engineers. The tool allows (thanks to automation) the adoption of microbenchmark by non-experts. Our results shows that the generated microbencharks match the quality of payloads handwritten by performance experts and outperforms those written by professional Java developers without experience in microbenchmarking
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Approximate Loop Unrolling
No full textWe introduce Approximate Unrolling, a loop optimization that reduces execution time and energy consumption, exploiting the existence of code regions that can endure some degree of approximation while still producing acceptable results. This work focuses on a specific kind of forgiving region: counted loops that map a given functions over the elements of an array. Approximate Unrolling transforms loops in a similar way Loop Unrolling does. However, unlike its exact counterpart, our optimization does not unroll loops by adding exact copies of the loop's body. Instead, it adds interpolations. We describe our experimental implementation of Approximate Unrolling in the Server (C2) Compiler of the Open-JDK Hotspot JVM. The choice to implement our technique directly in the compiler reduced Phase Order problems and transformation overhead. It also proved that our technique could actually improve the performance of a production-ready compiler. Using our modified version of the compiler , we perform several experiments showing that Approximate Unrolling is able reduce execution time and energy consumption of the generated code by a factor of 50% with minimal accuracy losses
Automatic Microbenchmark Generation to Prevent Dead Code Elimination and Constant Folding
No full textInternational audienceMicrobenchmarking consists of evaluating, in isolation, the performance of small code segments that play a critical role in large applications. The accuracy of a microbenchmark depends on two critical tasks: wrap the code segment into a payload that faithfully recreates the execution conditions that occur in the large application; build a scaffold that runs the payload a large number of times to get a statistical estimate of the execution time. While recent frameworks such as the Java Microbenchmark Harness (JMH) take care of the scaffold challenge, developers have very limited support to build a correct payload. In this work, we focus on the automatic generation of pay-loads, starting from a code segment selected in a large application. In particular, we aim at preventing two of the most common mistakes made in microbenchmarks: dead code elimination and constant folding. Since a microbench-mark is such a small program, if not designed carefully, it will be " over-optimized " by the JIT and result in distorted time measures. Our technique hence automatically extracts the segment into a compilable payload and generates additional code to prevent the risks of " over-optimization ". The whole approach is embedded in a tool called AutoJMH, which generates payloads for JMH scaffolds. We validate the capabilities AutoJMH, showing that the tool is able to process a large percentage of segments in real programs. We also show that AutoJMH can match the quality of payloads handwritten by performance experts and outperform those written by professional Java developers without experience un microbenchmarking
Images of Code: Lossy Compression for Native Instructions
No full textDevelopers can use lossy compression on images and many other artifacts to reduce size and improve network transfer times. Native program instructions, however, are typically not considered candidates for lossy compression since arbitrary losses in instructions may dramatically affect program output. In this paper we show that lossy compression of compiled native instructions is possible in certain circumstances. We demonstrate that the instructions sequence of a program can be lossily translated into a separate but equivalent program with instruction-wise differences, which still produces the same output. We contribute the novel insight that it is possible to exploit such instruction differences to design lossy compression schemes for native code. We support this idea with sound and unsound program transformations that improve performance of compression techniques such as Run-Length (RLE), Huffman and LZ77. We also show that large areas of code can endure tampered instructions with no impact on the output, a result consistent with previous works from various communities.QC 20180221</p
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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