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  • Un virus biologique utilisé à bon escient pourrait augmenter par dix la capacité des batteries, selon des chercheurs américains

    Un virus biologique utilisé à bon escient pourrait augmenter par dix la capacité des batteries Selon des chercheurs américains Les virus sont connus pour leur capacité de duplication et de destruction. Des chercheurs estiment avoir trouvé le moyen d'exploiter à bon escient leur capacité d'auto-renouvellement. En particulier le virus de la mosaïque du tabac (TMV) qui s'attaquent habituellement aux plantes, en particulier le tabac et les tomates. Ces scientifiques américains, qui travaillent sur la façon d'enrober des petites cellules de virus sur des matériaux conducteurs, ont constaté lors d'un test d'incorporation d'une nanostructure sur une batterie...

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  • Talend : seulement 5 ans et déjà tout d'un grand, le spécialiste français de l'intégration de données affiche une croissance de 100% par an

    Talend : 5 ans et déjà tout d'un grand L'entreprise française d'intégration de données affiche une croissance de 100% par an Il est bien loin le temps où lorsque l'on tapait Talend dans Google, le moteur de recherche renvoyait une proposition de correction orthographique pour « talent ». Pas si loin que cela, en fait. Car si l'entreprise française spécialisée dans les outils open-sources d'intégration de données n'est plus stricto-sensu une « start-up », elle est encore très jeune. De passage à Paris, le PDG de Talend ? Bertrand Diard ? le rappelait lors de son intervention au Talend Connect. « Quand on présente nos résultats, on nous dit souvent &q...

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  • Samsung rejette les accusations d'exploiter des mineurs en Chine par ses fournisseurs et assoit sa domination en Europe de l'Ouest

    Samsung rejette les accusations d'exploiter de mineurs en Chine Par ses fournisseurs et il assoit sa domination en Europe de l'Ouest Le mois dernier, China Labor Watch avait reporté des violations des lois du travail chinois quant à l'emploi des enfants de moins de 16 ans. Cette organisation non gouvernementale de défense des droits des travailleurs en Chine a mené trois enquêtes durant les mois de juin et juillet 2012 au sein de l'usine HEG Electronics Co. [IMG]http://idelways.developpez.com/news/images/samsung.jpg[/IMG] HEG Electronics Co est l'entreprise de sous-traitance pour Samsung en Chine. L'usine lui produit des téléphones, des lecteurs DVD, des ...

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  • Plug&Work DeLux : une soirée de Gala et de recrutement au Luxembourg le 8 novembre prochain, un évènement organisé par Moovijob

    Plug&Work DeLux : une soirée de Gala et de recrutement au Luxembourg Le 8 novembre prochain, un évènement organisé par Moovijob Après le succès de ses soirées Plug&Work en France, Moovijob a décidé de débarquer au Luxembourg le 8 novembre prochain au Château de Septfontaines de 19H00 à 22H00 avec la soirée Plug&Work Delux. La soirée Plug&Work DeLux sera l'occasion d'aller à la rencontre de près de 30 entreprises luxembourgeoises et d'une centaine de recruteurs offrant des jobs dans les secteurs de la Finance et de l' IT, lors d'un cocktail dînatoire. La soirée Plug&Work DeLux repose sur un concept intéressant : la clé USB et le badge. ...

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  • Google ajoute Do Not Track à Chrome, la fonction de protection de la vie privée ne sera pas activée par défaut

    Google ajoute Do Not Track à Chrome la fonction de protection de la vie privée ne sera pas activée par défaut Google a annoncé l'ajout de la fonction Do Not Track au navigateur Chrome. Do Not Track (DNT) est une mesure de sécurisation de la vie privée des internautes en envoyant via HTTP une entête particulière aux annonceurs, qui informe ceux-ci que l'utilisateur ne souhaite pas que son activité en ligne soit tracée pour de la publicité ciblée. La fonctionnalité sera disponible au sein du navigateur de Google avant la fin de l'année et peut déjà être testée dans la version de Chrome qui est téléchargeable sur le canal « Canary ». L'option Do Not Track pourra être ac...

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  • Apture : "le glossaire du Web" racheté par Google, afin d'inclure ses services de recherche contextuelle au navigateur Chrome

    Apture : "le glossaire du Web" racheté par Google Pour inclure ses services de recherche contextuelle au navigateur Chrome Après 13 ans de développement dans son coeur de métier, Google a encore des choses à apprendre sur la recherche en ligne, et n'hésite pas à sortir son portefeuille dans ce but. L'entreprise vient de signer l'accord d'acquisition d'Apture, une start-up qui se proclame le « glossaire du Web ». Cette dernière enrichit depuis 2007 des millions de pages avec des contenus complémentaires, issus de la recherche contextuelle et consultables en pop-up sans quitter la page. [IMG]http://idelways.developpez.com/news/images/apture.png[/IMG]

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  • Comment l'analyse statique moderne peut-elle faciliter la vie des développeurs ? Découvrez la solution de Coverity, utilisée par le CERN

    Webinar : comment l'analyse statique de dernière génération peut-elle faciliter la vie des développeurs ? Découvrez la solution de Coverity, utilisée par le CERN Dans un monde où un bug mineur peut avoir des effets dévastateurs, les outils d'analyse statique d'ancienne génération s'avèrent souvent incapables de détecter les vrais défauts de code, difficiles à identifier. Mais des outils modernes d'analyse statique de code existent, permettent de détecter ces défauts critiques et potentiellement dommageables tôt dans le cycle de développement, permettant ainsi de réduire les coûts, les délais et les risques liés aux erreurs logicielles. Coverity Static Analysis est l'une de...

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  • Avez-vous confiance dans la confidentialité du Cloud ? Google fait certifier la confidentialité des Google Docs par des tiers indépendants

    Avez-vous confiance dans la confidentialité du Cloud ? Google fait certifier la confidentialité des Google Docs par des organismes indépendants Google, Amazon ou Salesforce.com se livrent bataille dans le Cloud. Mais ces prestataires ont un point commun : la question de la confidentialité est toujours plus ou moins esquivée. Le constat est certainement moins vrai pour Microsoft qui d'une part propose un Cloud hybride (et donc permet de garder la main en local sur ses données sensibles) et d'autre part qui a admis qu'en cas de demande de la part de la justice américaine, ses data-centers seraient ouverts au FBI.

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  • Les ayants droit hantés par Megaupload ? Des demandes de suppression pleuvent encore sur Google pour des services disparus

    Les ayants droit hantés par Megaupload, Demonoid et BTjunkie ? Des demandes de suppression pleuvent encore sur Google pour des services qui n'existent plus [IMG]http://idelways.developpez.com/news/images/fbi.jpg[/IMG] Google reçoit quotidiennement une quantité importante de demandes de suppression de liens vers Megaupload, Demonoid et BTjunkie et autres services de partage mis hors service depuis déjà un moment ! Ces demandes proviennent des plus grandes maisons de disques ainsi que des compagnies et associations antipiratage, qui envoient des rapports DMCA (une loi américaine contre le copyright) sans même vérifier si ces liens existent toujours. ...

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  • Les pirates exploiteraient déjà la faille de Windows XP divulguée par un employé de Google, qui se d

    Mise à jour du 16/06/10 Les pirates exploiteraient déjà la faille de Windows XP Divulguée par un employé de Google, qui se défend des accusations de Microsoft Tavis Ormandy, l'employé de Google qui a mis à jour une faille dans le Centre d'Aide et de Support de Windows XP et qui a publié un exploit montrant comment il était possible d'en tirer profit, se défend d'avoir eu un comportement irresponsable. Il affirme dans un Tweet que, contrairement aux dires de Microsoft, il n'a pas laissé 5 jours mais deux mois à l'éditeur de Windows (60 jours) pour colmater la faille. Ce serait donc l'inertie de Redmond qui l'aurait décid...

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  • Microsoft brevète la technologie de ses Microsoft Glass, les lunettes prochain objet grand public révolutionné par l'informatique ?

    Microsoft brevète des technologies pour des Microsoft Glass Les lunettes prochain objet grand public révolutionné par l'informatique ? Les Google Glass suscitent beaucoup d'intérêt de la part de la concurrence. Après Apple, c'est au tour de Microsoft de se lancer dans ce genre de projet. L'éditeur qui avait promis qu'il sortirait d'autres appareils que la Surface (et la Xbox 360) sous sa marque propre pourrait bien tenir parole avec des lunettes. C'est en tout cas ce que laisse entrevoir un brevet qu'il a déposé ce 22 novembre. [IMG]http://ftp-developpez.com/gordon-fowler/Microsoft%20Glass.jpg[/IMG] Microsoft Glasses te...

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  • Le moteur de recherche Baidu fait son entrée sur le marché des navigateurs mobiles, dominé par Apple et Google

    Le moteur de recherche Baidu fait son entrée sur le marché des navigateurs mobiles Dominé par Apple et Google Le moteur de recherche chinois Baidu lance son propre navigateur Web mobile dans le but de générer plus de bénéfices auprès des utilisateurs de smartphones, dont le nombre se chiffre désormais en centaines de millions d'utilisateurs en Chine. [IMG]http://idelways.developpez.com/news/images/baidu-logo.jpg[/IMG] Baidu a choisi de cibler les utilisateurs chinois et se place comme principal rival à Safari sur les appareils d'Apple et à Chrome sur Google Android. Il ne compte pas que sur la fierté nationale : selon la société Baidu, les tests de perform...

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  • Choose Your Boss : un tiers de recruteurs en plus par mois, le nouveau site d'emploi IT inspiré des sites de rencontres trouve son public

    Choose Your Boss : un nouveau site d'emplois IT qui s'inspire des sites de rencontres Plus de deux cent offres qualifiées de postes disponibles Choose Your Boss est un site original. Il s'inspire de Meetic et autres Attractive World pour mettre en relation développeurs et professionnels de l'IT d'une part et recruteurs d'autre part. Créé par Laurent Chollat-Namy, qui bénéficie d'une expérience professionnelle de 15 ans dans l'IT, celui-ci explique : « Nous avons longuement discuté avec des recruteurs et des informaticiens pour identifier leurs besoins et leurs pratiques. Cette réflexion nous a amenés à réinventer la mise en relation candidat / recruteur en nous inspirant des sites de r...

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  • iOS 6 "jailbreaké" moins d'une journée après son lancement par le groupe de hackers Dev-Team, l'iPhone 5 bientôt déverrouillé ?

    iOS 6 "jailbreaké" moins d'une journée après son lancement Par le groupe de hackers Dev-Team, l'iPhone 5 bientôt déverrouillé ? L'iPhone Dev-Team, un groupe de hackers indépendant spécialisé dans les produits Apple, a réussi à créer un Jailbreak (déverrouillage) pour l'iOS 6. Cette annonce est survenue moins d'une journée après la sortie de ce dernier. [IMG]http://idelways.developpez.com/news/images/jailbreak.jpg[/IMG] Sorti dans la lignée du célèbre outil redsn0w, le jailbreak fonctionne seulement pour l'iPhone 4, l'iPhone 3GS et la quatrième génération de l'iPod Touch, soit uniquement des dispositifs équipés de processeurs A4. L'iPhone 5 n'est ...

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  • Le réseau GSM cracké par deux chercheurs qui ont présenté un moyen de sniffer données et conversations d'un mobile

    Le réseau GSM cracké par deux chercheurs qui ont présenté un moyen de sniffer données et conversations d'un mobile pour moins de 100 euros Deux chercheurs allemands viennent de faire la démonstration d'un système de piratage du réseau GSM (technologie la plus utilisée dans le monde pour relier les téléphones mobiles entre eux -plus de 5 milliards d'appareils concernés selon les opérateurs-), qui permet également de réaliser des écoutes. Il faut savoir qu'à la base, un système d'écoutes est extrêmement onéreux (près de 40.000 euros). Mais celui présenté en Allemagne est réalisable pour moins de 100 euros d'investissement ! Le toolkit utilise en effet des téléphones mobiles Motorola à 10 euros, sur lesquels le firmwar...

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  • Qt 4.8.5 est disponible, cette version de maintenance sera disponible par les mêmes outils de Qt 5.1 à sa sortie

    Sortie de Qt 4.8.1 : de nombreuses corrections de bugsMise à jour du 29/03/2012 par gbdiversQuelques mois après la sortie de Qt 4.8, voici la première mise à jour avec la sortie de Qt 4.8.1. Cette version apporte principalement des corrections de bugs et plus de 200 améliorations fonctionnelles. Digia, responsable du support commercial de Qt, a fait un travail majeur dans la correction des bugs en proposant un grand nombre de corrections. La version 1.2.1 du Qt SDK devrait être mis à jour également dans les semaines prochaines pour intégrer cette nouvelle version du framework.Vous pouvez télécharger Qt 4.8.1 sur la

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  • Les tablettes vont-elles réussir leur percée en entreprise ? 38 % des professionnels prêts à remplacer leur notebook par un de ces appareils

    Les tablettes vont-elles réussir leur percée en entreprise ? 38% des professionnels serait prêt à remplacer leur notebook par l'un de ces appareils C'est un scénario que plusieurs experts envisagent depuis des mois, et c'est aussi une possibilité que Steve Ballmer réfute : les tablettes pourraient remplacer les ordinateurs et les netbooks. A moins d'être sourd et aveugle, tout le monde a constaté le succès phénoménal qu'ont rencontré ces appareils. La société de conseil ChangeWave s'est penchée sur la question, mais depuis le point de vue des professionnels. Elle a ainsi interrogé 1641 employés à propos de leur intérêt pour les tablettes, dans le cadre de leurs fonctions. Et les résultats sont clairs :...

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  • java - unwanted object overwriting

    - by gosling
    Hello everyone! I'm trying to make a program that solves the logic wheels puzzle. I construct the root node and I try to produce the different child-nodes that are produced by making different moves of the wheels. The problem is that while I try to produce the children, the root node is overwrited,and everything is messed-up and I really don't know why. Here you can find the puzzle logic wheels. I represent the wheels as 3x3 arrays. Here is the code that implements the moves: public Node turn_right(Node aNode, int which_wheel) { Node newNode = new Node(aNode.getYellow_wheel(),aNode.getBlue_wheel(),aNode.getGreen_wheel()); int[][] yellow = new int[3][3]; int[][] blue = new int[3][3]; int[][] green = new int[3][3]; if(which_wheel==0) //turn yellow wheel of this node to right { yellow[1][0] = newNode.getYellow_wheel()[0][0]; yellow[2][0] = newNode.getYellow_wheel()[1][0]; yellow[2][1] = newNode.getYellow_wheel()[2][0]; yellow[2][2] = newNode.getYellow_wheel()[2][1]; yellow[1][2] = newNode.getYellow_wheel()[2][2]; yellow[0][2] = newNode.getYellow_wheel()[1][2]; yellow[0][1] = newNode.getYellow_wheel()[0][2]; yellow[0][0] = newNode.getYellow_wheel()[0][1]; blue = newNode.getBlue_wheel(); blue[1][0] = newNode.getYellow_wheel()[1][2]; blue[2][0] = newNode.getYellow_wheel()[2][2]; green = newNode.getGreen_wheel(); } else if(which_wheel == 1)// turn blue wheel of this node to right { blue[1][0] = newNode.getBlue_wheel()[0][0]; blue[2][0] = newNode.getBlue_wheel()[1][0]; blue[2][1] = newNode.getBlue_wheel()[2][0]; blue[2][2] = newNode.getBlue_wheel()[2][1]; blue[1][2] = newNode.getBlue_wheel()[2][2]; blue[0][2] = newNode.getBlue_wheel()[1][2]; blue[0][1] = newNode.getBlue_wheel()[0][2]; blue[0][0] = newNode.getBlue_wheel()[0][1]; yellow = newNode.getYellow_wheel(); yellow[0][2] = newNode.getBlue_wheel()[0][0]; yellow[1][2] = newNode.getBlue_wheel()[1][0]; green = newNode.getGreen_wheel(); green[1][0] = newNode.getBlue_wheel()[1][2]; green[2][0] = newNode.getBlue_wheel()[2][2]; } else if (which_wheel == 2)//turn green wheel of this node to right { green[0][0] = newNode.getGreen_wheel()[0][1]; green[0][1] = newNode.getGreen_wheel()[0][2]; green[0][2] = newNode.getGreen_wheel()[1][2]; green[1][2] = newNode.getGreen_wheel()[2][2]; green[2][2] = newNode.getGreen_wheel()[2][1]; green[2][1] = newNode.getGreen_wheel()[2][0]; green[2][0] = newNode.getGreen_wheel()[1][0]; green[1][0] = newNode.getGreen_wheel()[0][0]; yellow = newNode.getYellow_wheel(); blue = newNode.getBlue_wheel(); blue[0][2] = newNode.getGreen_wheel()[0][0]; blue[1][2] = newNode.getGreen_wheel()[1][0]; } newNode= new Node(yellow,blue,green); return newNode; } There is another function, like this one that does the oposite:it turns the wheels to left. My problem is that I do not want object's aNode tables to be overwritten. Thank you very much.

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  • Pourquoi les entreprises passent-elles par des cabinets de recrutement lorsqu'elles ont besoin de développeurs ? Pour quels intérêts ?

    Pourquoi les boites ne publient pas des annonces détaillées des jobs de développeur qu'elles proposent au lieu de passer par des cabinets de recrutement ou même des SSII alors que le projet dure plus d'un an ?Je suis convaincu que dans 99% des cas c'est ridicule de passer par un cabinet de recrutement (déjà ça leur coûte au moins 1500 euros à payer quand le nouvel employé a terminé sa période d'essai) et que le principal effet en plus de la perte nette sur salaire (c'est 1500 euros de moins sur...

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  • Color drop down in Excel cell (with no text)? e.g. bgcolor = Red-Green-Amber-unknown

    - by adolf garlic
    I have an Excel sheet that I'm using to keep track of the status of certain things. I want to have a column which consists of cells containing a repeated drop down that allows you to select (as background) red amber green unknown I don't want any text in this cell, I just want a coloured block. Is this possible? I've tried playing around with data-validation-list (based on range containing all of said colours but to no avail)

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  • How John Got 15x Improvement Without Really Trying

    - by rchrd
    The following article was published on a Sun Microsystems website a number of years ago by John Feo. It is still useful and worth preserving. So I'm republishing it here.  How I Got 15x Improvement Without Really Trying John Feo, Sun Microsystems Taking ten "personal" program codes used in scientific and engineering research, the author was able to get from 2 to 15 times performance improvement easily by applying some simple general optimization techniques. Introduction Scientific research based on computer simulation depends on the simulation for advancement. The research can advance only as fast as the computational codes can execute. The codes' efficiency determines both the rate and quality of results. In the same amount of time, a faster program can generate more results and can carry out a more detailed simulation of physical phenomena than a slower program. Highly optimized programs help science advance quickly and insure that monies supporting scientific research are used as effectively as possible. Scientific computer codes divide into three broad categories: ISV, community, and personal. ISV codes are large, mature production codes developed and sold commercially. The codes improve slowly over time both in methods and capabilities, and they are well tuned for most vendor platforms. Since the codes are mature and complex, there are few opportunities to improve their performance solely through code optimization. Improvements of 10% to 15% are typical. Examples of ISV codes are DYNA3D, Gaussian, and Nastran. Community codes are non-commercial production codes used by a particular research field. Generally, they are developed and distributed by a single academic or research institution with assistance from the community. Most users just run the codes, but some develop new methods and extensions that feed back into the general release. The codes are available on most vendor platforms. Since these codes are younger than ISV codes, there are more opportunities to optimize the source code. Improvements of 50% are not unusual. Examples of community codes are AMBER, CHARM, BLAST, and FASTA. Personal codes are those written by single users or small research groups for their own use. These codes are not distributed, but may be passed from professor-to-student or student-to-student over several years. They form the primordial ocean of applications from which community and ISV codes emerge. Government research grants pay for the development of most personal codes. This paper reports on the nature and performance of this class of codes. Over the last year, I have looked at over two dozen personal codes from more than a dozen research institutions. The codes cover a variety of scientific fields, including astronomy, atmospheric sciences, bioinformatics, biology, chemistry, geology, and physics. The sources range from a few hundred lines to more than ten thousand lines, and are written in Fortran, Fortran 90, C, and C++. For the most part, the codes are modular, documented, and written in a clear, straightforward manner. They do not use complex language features, advanced data structures, programming tricks, or libraries. I had little trouble understanding what the codes did or how data structures were used. Most came with a makefile. Surprisingly, only one of the applications is parallel. All developers have access to parallel machines, so availability is not an issue. Several tried to parallelize their applications, but stopped after encountering difficulties. Lack of education and a perception that parallelism is difficult prevented most from trying. I parallelized several of the codes using OpenMP, and did not judge any of the codes as difficult to parallelize. Even more surprising than the lack of parallelism is the inefficiency of the codes. I was able to get large improvements in performance in a matter of a few days applying simple optimization techniques. Table 1 lists ten representative codes [names and affiliation are omitted to preserve anonymity]. Improvements on one processor range from 2x to 15.5x with a simple average of 4.75x. I did not use sophisticated performance tools or drill deep into the program's execution character as one would do when tuning ISV or community codes. Using only a profiler and source line timers, I identified inefficient sections of code and improved their performance by inspection. The changes were at a high level. I am sure there is another factor of 2 or 3 in each code, and more if the codes are parallelized. The study’s results show that personal scientific codes are running many times slower than they should and that the problem is pervasive. Computational scientists are not sloppy programmers; however, few are trained in the art of computer programming or code optimization. I found that most have a working knowledge of some programming language and standard software engineering practices; but they do not know, or think about, how to make their programs run faster. They simply do not know the standard techniques used to make codes run faster. In fact, they do not even perceive that such techniques exist. The case studies described in this paper show that applying simple, well known techniques can significantly increase the performance of personal codes. It is important that the scientific community and the Government agencies that support scientific research find ways to better educate academic scientific programmers. The inefficiency of their codes is so bad that it is retarding both the quality and progress of scientific research. # cacheperformance redundantoperations loopstructures performanceimprovement 1 x x 15.5 2 x 2.8 3 x x 2.5 4 x 2.1 5 x x 2.0 6 x 5.0 7 x 5.8 8 x 6.3 9 2.2 10 x x 3.3 Table 1 — Area of improvement and performance gains of 10 codes The remainder of the paper is organized as follows: sections 2, 3, and 4 discuss the three most common sources of inefficiencies in the codes studied. These are cache performance, redundant operations, and loop structures. Each section includes several examples. The last section summaries the work and suggests a possible solution to the issues raised. Optimizing cache performance Commodity microprocessor systems use caches to increase memory bandwidth and reduce memory latencies. Typical latencies from processor to L1, L2, local, and remote memory are 3, 10, 50, and 200 cycles, respectively. Moreover, bandwidth falls off dramatically as memory distances increase. Programs that do not use cache effectively run many times slower than programs that do. When optimizing for cache, the biggest performance gains are achieved by accessing data in cache order and reusing data to amortize the overhead of cache misses. Secondary considerations are prefetching, associativity, and replacement; however, the understanding and analysis required to optimize for the latter are probably beyond the capabilities of the non-expert. Much can be gained simply by accessing data in the correct order and maximizing data reuse. 6 out of the 10 codes studied here benefited from such high level optimizations. Array Accesses The most important cache optimization is the most basic: accessing Fortran array elements in column order and C array elements in row order. Four of the ten codes—1, 2, 4, and 10—got it wrong. Compilers will restructure nested loops to optimize cache performance, but may not do so if the loop structure is too complex, or the loop body includes conditionals, complex addressing, or function calls. In code 1, the compiler failed to invert a key loop because of complex addressing do I = 0, 1010, delta_x IM = I - delta_x IP = I + delta_x do J = 5, 995, delta_x JM = J - delta_x JP = J + delta_x T1 = CA1(IP, J) + CA1(I, JP) T2 = CA1(IM, J) + CA1(I, JM) S1 = T1 + T2 - 4 * CA1(I, J) CA(I, J) = CA1(I, J) + D * S1 end do end do In code 2, the culprit is conditionals do I = 1, N do J = 1, N If (IFLAG(I,J) .EQ. 0) then T1 = Value(I, J-1) T2 = Value(I-1, J) T3 = Value(I, J) T4 = Value(I+1, J) T5 = Value(I, J+1) Value(I,J) = 0.25 * (T1 + T2 + T5 + T4) Delta = ABS(T3 - Value(I,J)) If (Delta .GT. MaxDelta) MaxDelta = Delta endif enddo enddo I fixed both programs by inverting the loops by hand. Code 10 has three-dimensional arrays and triply nested loops. The structure of the most computationally intensive loops is too complex to invert automatically or by hand. The only practical solution is to transpose the arrays so that the dimension accessed by the innermost loop is in cache order. The arrays can be transposed at construction or prior to entering a computationally intensive section of code. The former requires all array references to be modified, while the latter is cost effective only if the cost of the transpose is amortized over many accesses. I used the second approach to optimize code 10. Code 5 has four-dimensional arrays and loops are nested four deep. For all of the reasons cited above the compiler is not able to restructure three key loops. Assume C arrays and let the four dimensions of the arrays be i, j, k, and l. In the original code, the index structure of the three loops is L1: for i L2: for i L3: for i for l for l for j for k for j for k for j for k for l So only L3 accesses array elements in cache order. L1 is a very complex loop—much too complex to invert. I brought the loop into cache alignment by transposing the second and fourth dimensions of the arrays. Since the code uses a macro to compute all array indexes, I effected the transpose at construction and changed the macro appropriately. The dimensions of the new arrays are now: i, l, k, and j. L3 is a simple loop and easily inverted. L2 has a loop-carried scalar dependence in k. By promoting the scalar name that carries the dependence to an array, I was able to invert the third and fourth subloops aligning the loop with cache. Code 5 is by far the most difficult of the four codes to optimize for array accesses; but the knowledge required to fix the problems is no more than that required for the other codes. I would judge this code at the limits of, but not beyond, the capabilities of appropriately trained computational scientists. Array Strides When a cache miss occurs, a line (64 bytes) rather than just one word is loaded into the cache. If data is accessed stride 1, than the cost of the miss is amortized over 8 words. Any stride other than one reduces the cost savings. Two of the ten codes studied suffered from non-unit strides. The codes represent two important classes of "strided" codes. Code 1 employs a multi-grid algorithm to reduce time to convergence. The grids are every tenth, fifth, second, and unit element. Since time to convergence is inversely proportional to the distance between elements, coarse grids converge quickly providing good starting values for finer grids. The better starting values further reduce the time to convergence. The downside is that grids of every nth element, n > 1, introduce non-unit strides into the computation. In the original code, much of the savings of the multi-grid algorithm were lost due to this problem. I eliminated the problem by compressing (copying) coarse grids into continuous memory, and rewriting the computation as a function of the compressed grid. On convergence, I copied the final values of the compressed grid back to the original grid. The savings gained from unit stride access of the compressed grid more than paid for the cost of copying. Using compressed grids, the loop from code 1 included in the previous section becomes do j = 1, GZ do i = 1, GZ T1 = CA(i+0, j-1) + CA(i-1, j+0) T4 = CA1(i+1, j+0) + CA1(i+0, j+1) S1 = T1 + T4 - 4 * CA1(i+0, j+0) CA(i+0, j+0) = CA1(i+0, j+0) + DD * S1 enddo enddo where CA and CA1 are compressed arrays of size GZ. Code 7 traverses a list of objects selecting objects for later processing. The labels of the selected objects are stored in an array. The selection step has unit stride, but the processing steps have irregular stride. A fix is to save the parameters of the selected objects in temporary arrays as they are selected, and pass the temporary arrays to the processing functions. The fix is practical if the same parameters are used in selection as in processing, or if processing comprises a series of distinct steps which use overlapping subsets of the parameters. Both conditions are true for code 7, so I achieved significant improvement by copying parameters to temporary arrays during selection. Data reuse In the previous sections, we optimized for spatial locality. It is also important to optimize for temporal locality. Once read, a datum should be used as much as possible before it is forced from cache. Loop fusion and loop unrolling are two techniques that increase temporal locality. Unfortunately, both techniques increase register pressure—as loop bodies become larger, the number of registers required to hold temporary values grows. Once register spilling occurs, any gains evaporate quickly. For multiprocessors with small register sets or small caches, the sweet spot can be very small. In the ten codes presented here, I found no opportunities for loop fusion and only two opportunities for loop unrolling (codes 1 and 3). In code 1, unrolling the outer and inner loop one iteration increases the number of result values computed by the loop body from 1 to 4, do J = 1, GZ-2, 2 do I = 1, GZ-2, 2 T1 = CA1(i+0, j-1) + CA1(i-1, j+0) T2 = CA1(i+1, j-1) + CA1(i+0, j+0) T3 = CA1(i+0, j+0) + CA1(i-1, j+1) T4 = CA1(i+1, j+0) + CA1(i+0, j+1) T5 = CA1(i+2, j+0) + CA1(i+1, j+1) T6 = CA1(i+1, j+1) + CA1(i+0, j+2) T7 = CA1(i+2, j+1) + CA1(i+1, j+2) S1 = T1 + T4 - 4 * CA1(i+0, j+0) S2 = T2 + T5 - 4 * CA1(i+1, j+0) S3 = T3 + T6 - 4 * CA1(i+0, j+1) S4 = T4 + T7 - 4 * CA1(i+1, j+1) CA(i+0, j+0) = CA1(i+0, j+0) + DD * S1 CA(i+1, j+0) = CA1(i+1, j+0) + DD * S2 CA(i+0, j+1) = CA1(i+0, j+1) + DD * S3 CA(i+1, j+1) = CA1(i+1, j+1) + DD * S4 enddo enddo The loop body executes 12 reads, whereas as the rolled loop shown in the previous section executes 20 reads to compute the same four values. In code 3, two loops are unrolled 8 times and one loop is unrolled 4 times. Here is the before for (k = 0; k < NK[u]; k++) { sum = 0.0; for (y = 0; y < NY; y++) { sum += W[y][u][k] * delta[y]; } backprop[i++]=sum; } and after code for (k = 0; k < KK - 8; k+=8) { sum0 = 0.0; sum1 = 0.0; sum2 = 0.0; sum3 = 0.0; sum4 = 0.0; sum5 = 0.0; sum6 = 0.0; sum7 = 0.0; for (y = 0; y < NY; y++) { sum0 += W[y][0][k+0] * delta[y]; sum1 += W[y][0][k+1] * delta[y]; sum2 += W[y][0][k+2] * delta[y]; sum3 += W[y][0][k+3] * delta[y]; sum4 += W[y][0][k+4] * delta[y]; sum5 += W[y][0][k+5] * delta[y]; sum6 += W[y][0][k+6] * delta[y]; sum7 += W[y][0][k+7] * delta[y]; } backprop[k+0] = sum0; backprop[k+1] = sum1; backprop[k+2] = sum2; backprop[k+3] = sum3; backprop[k+4] = sum4; backprop[k+5] = sum5; backprop[k+6] = sum6; backprop[k+7] = sum7; } for one of the loops unrolled 8 times. Optimizing for temporal locality is the most difficult optimization considered in this paper. The concepts are not difficult, but the sweet spot is small. Identifying where the program can benefit from loop unrolling or loop fusion is not trivial. Moreover, it takes some effort to get it right. Still, educating scientific programmers about temporal locality and teaching them how to optimize for it will pay dividends. Reducing instruction count Execution time is a function of instruction count. Reduce the count and you usually reduce the time. The best solution is to use a more efficient algorithm; that is, an algorithm whose order of complexity is smaller, that converges quicker, or is more accurate. Optimizing source code without changing the algorithm yields smaller, but still significant, gains. This paper considers only the latter because the intent is to study how much better codes can run if written by programmers schooled in basic code optimization techniques. The ten codes studied benefited from three types of "instruction reducing" optimizations. The two most prevalent were hoisting invariant memory and data operations out of inner loops. The third was eliminating unnecessary data copying. The nature of these inefficiencies is language dependent. Memory operations The semantics of C make it difficult for the compiler to determine all the invariant memory operations in a loop. The problem is particularly acute for loops in functions since the compiler may not know the values of the function's parameters at every call site when compiling the function. Most compilers support pragmas to help resolve ambiguities; however, these pragmas are not comprehensive and there is no standard syntax. To guarantee that invariant memory operations are not executed repetitively, the user has little choice but to hoist the operations by hand. The problem is not as severe in Fortran programs because in the absence of equivalence statements, it is a violation of the language's semantics for two names to share memory. Codes 3 and 5 are C programs. In both cases, the compiler did not hoist all invariant memory operations from inner loops. Consider the following loop from code 3 for (y = 0; y < NY; y++) { i = 0; for (u = 0; u < NU; u++) { for (k = 0; k < NK[u]; k++) { dW[y][u][k] += delta[y] * I1[i++]; } } } Since dW[y][u] can point to the same memory space as delta for one or more values of y and u, assignment to dW[y][u][k] may change the value of delta[y]. In reality, dW and delta do not overlap in memory, so I rewrote the loop as for (y = 0; y < NY; y++) { i = 0; Dy = delta[y]; for (u = 0; u < NU; u++) { for (k = 0; k < NK[u]; k++) { dW[y][u][k] += Dy * I1[i++]; } } } Failure to hoist invariant memory operations may be due to complex address calculations. If the compiler can not determine that the address calculation is invariant, then it can hoist neither the calculation nor the associated memory operations. As noted above, code 5 uses a macro to address four-dimensional arrays #define MAT4D(a,q,i,j,k) (double *)((a)->data + (q)*(a)->strides[0] + (i)*(a)->strides[3] + (j)*(a)->strides[2] + (k)*(a)->strides[1]) The macro is too complex for the compiler to understand and so, it does not identify any subexpressions as loop invariant. The simplest way to eliminate the address calculation from the innermost loop (over i) is to define a0 = MAT4D(a,q,0,j,k) before the loop and then replace all instances of *MAT4D(a,q,i,j,k) in the loop with a0[i] A similar problem appears in code 6, a Fortran program. The key loop in this program is do n1 = 1, nh nx1 = (n1 - 1) / nz + 1 nz1 = n1 - nz * (nx1 - 1) do n2 = 1, nh nx2 = (n2 - 1) / nz + 1 nz2 = n2 - nz * (nx2 - 1) ndx = nx2 - nx1 ndy = nz2 - nz1 gxx = grn(1,ndx,ndy) gyy = grn(2,ndx,ndy) gxy = grn(3,ndx,ndy) balance(n1,1) = balance(n1,1) + (force(n2,1) * gxx + force(n2,2) * gxy) * h1 balance(n1,2) = balance(n1,2) + (force(n2,1) * gxy + force(n2,2) * gyy)*h1 end do end do The programmer has written this loop well—there are no loop invariant operations with respect to n1 and n2. However, the loop resides within an iterative loop over time and the index calculations are independent with respect to time. Trading space for time, I precomputed the index values prior to the entering the time loop and stored the values in two arrays. I then replaced the index calculations with reads of the arrays. Data operations Ways to reduce data operations can appear in many forms. Implementing a more efficient algorithm produces the biggest gains. The closest I came to an algorithm change was in code 4. This code computes the inner product of K-vectors A(i) and B(j), 0 = i < N, 0 = j < M, for most values of i and j. Since the program computes most of the NM possible inner products, it is more efficient to compute all the inner products in one triply-nested loop rather than one at a time when needed. The savings accrue from reading A(i) once for all B(j) vectors and from loop unrolling. for (i = 0; i < N; i+=8) { for (j = 0; j < M; j++) { sum0 = 0.0; sum1 = 0.0; sum2 = 0.0; sum3 = 0.0; sum4 = 0.0; sum5 = 0.0; sum6 = 0.0; sum7 = 0.0; for (k = 0; k < K; k++) { sum0 += A[i+0][k] * B[j][k]; sum1 += A[i+1][k] * B[j][k]; sum2 += A[i+2][k] * B[j][k]; sum3 += A[i+3][k] * B[j][k]; sum4 += A[i+4][k] * B[j][k]; sum5 += A[i+5][k] * B[j][k]; sum6 += A[i+6][k] * B[j][k]; sum7 += A[i+7][k] * B[j][k]; } C[i+0][j] = sum0; C[i+1][j] = sum1; C[i+2][j] = sum2; C[i+3][j] = sum3; C[i+4][j] = sum4; C[i+5][j] = sum5; C[i+6][j] = sum6; C[i+7][j] = sum7; }} This change requires knowledge of a typical run; i.e., that most inner products are computed. The reasons for the change, however, derive from basic optimization concepts. It is the type of change easily made at development time by a knowledgeable programmer. In code 5, we have the data version of the index optimization in code 6. Here a very expensive computation is a function of the loop indices and so cannot be hoisted out of the loop; however, the computation is invariant with respect to an outer iterative loop over time. We can compute its value for each iteration of the computation loop prior to entering the time loop and save the values in an array. The increase in memory required to store the values is small in comparison to the large savings in time. The main loop in Code 8 is doubly nested. The inner loop includes a series of guarded computations; some are a function of the inner loop index but not the outer loop index while others are a function of the outer loop index but not the inner loop index for (j = 0; j < N; j++) { for (i = 0; i < M; i++) { r = i * hrmax; R = A[j]; temp = (PRM[3] == 0.0) ? 1.0 : pow(r, PRM[3]); high = temp * kcoeff * B[j] * PRM[2] * PRM[4]; low = high * PRM[6] * PRM[6] / (1.0 + pow(PRM[4] * PRM[6], 2.0)); kap = (R > PRM[6]) ? high * R * R / (1.0 + pow(PRM[4]*r, 2.0) : low * pow(R/PRM[6], PRM[5]); < rest of loop omitted > }} Note that the value of temp is invariant to j. Thus, we can hoist the computation for temp out of the loop and save its values in an array. for (i = 0; i < M; i++) { r = i * hrmax; TEMP[i] = pow(r, PRM[3]); } [N.B. – the case for PRM[3] = 0 is omitted and will be reintroduced later.] We now hoist out of the inner loop the computations invariant to i. Since the conditional guarding the value of kap is invariant to i, it behooves us to hoist the computation out of the inner loop, thereby executing the guard once rather than M times. The final version of the code is for (j = 0; j < N; j++) { R = rig[j] / 1000.; tmp1 = kcoeff * par[2] * beta[j] * par[4]; tmp2 = 1.0 + (par[4] * par[4] * par[6] * par[6]); tmp3 = 1.0 + (par[4] * par[4] * R * R); tmp4 = par[6] * par[6] / tmp2; tmp5 = R * R / tmp3; tmp6 = pow(R / par[6], par[5]); if ((par[3] == 0.0) && (R > par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * tmp5; } else if ((par[3] == 0.0) && (R <= par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * tmp4 * tmp6; } else if ((par[3] != 0.0) && (R > par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * TEMP[i] * tmp5; } else if ((par[3] != 0.0) && (R <= par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * TEMP[i] * tmp4 * tmp6; } for (i = 0; i < M; i++) { kap = KAP[i]; r = i * hrmax; < rest of loop omitted > } } Maybe not the prettiest piece of code, but certainly much more efficient than the original loop, Copy operations Several programs unnecessarily copy data from one data structure to another. This problem occurs in both Fortran and C programs, although it manifests itself differently in the two languages. Code 1 declares two arrays—one for old values and one for new values. At the end of each iteration, the array of new values is copied to the array of old values to reset the data structures for the next iteration. This problem occurs in Fortran programs not included in this study and in both Fortran 77 and Fortran 90 code. Introducing pointers to the arrays and swapping pointer values is an obvious way to eliminate the copying; but pointers is not a feature that many Fortran programmers know well or are comfortable using. An easy solution not involving pointers is to extend the dimension of the value array by 1 and use the last dimension to differentiate between arrays at different times. For example, if the data space is N x N, declare the array (N, N, 2). Then store the problem’s initial values in (_, _, 2) and define the scalar names new = 2 and old = 1. At the start of each iteration, swap old and new to reset the arrays. The old–new copy problem did not appear in any C program. In programs that had new and old values, the code swapped pointers to reset data structures. Where unnecessary coping did occur is in structure assignment and parameter passing. Structures in C are handled much like scalars. Assignment causes the data space of the right-hand name to be copied to the data space of the left-hand name. Similarly, when a structure is passed to a function, the data space of the actual parameter is copied to the data space of the formal parameter. If the structure is large and the assignment or function call is in an inner loop, then copying costs can grow quite large. While none of the ten programs considered here manifested this problem, it did occur in programs not included in the study. A simple fix is always to refer to structures via pointers. Optimizing loop structures Since scientific programs spend almost all their time in loops, efficient loops are the key to good performance. Conditionals, function calls, little instruction level parallelism, and large numbers of temporary values make it difficult for the compiler to generate tightly packed, highly efficient code. Conditionals and function calls introduce jumps that disrupt code flow. Users should eliminate or isolate conditionls to their own loops as much as possible. Often logical expressions can be substituted for if-then-else statements. For example, code 2 includes the following snippet MaxDelta = 0.0 do J = 1, N do I = 1, M < code omitted > Delta = abs(OldValue ? NewValue) if (Delta > MaxDelta) MaxDelta = Delta enddo enddo if (MaxDelta .gt. 0.001) goto 200 Since the only use of MaxDelta is to control the jump to 200 and all that matters is whether or not it is greater than 0.001, I made MaxDelta a boolean and rewrote the snippet as MaxDelta = .false. do J = 1, N do I = 1, M < code omitted > Delta = abs(OldValue ? NewValue) MaxDelta = MaxDelta .or. (Delta .gt. 0.001) enddo enddo if (MaxDelta) goto 200 thereby, eliminating the conditional expression from the inner loop. A microprocessor can execute many instructions per instruction cycle. Typically, it can execute one or more memory, floating point, integer, and jump operations. To be executed simultaneously, the operations must be independent. Thick loops tend to have more instruction level parallelism than thin loops. Moreover, they reduce memory traffice by maximizing data reuse. Loop unrolling and loop fusion are two techniques to increase the size of loop bodies. Several of the codes studied benefitted from loop unrolling, but none benefitted from loop fusion. This observation is not too surpising since it is the general tendency of programmers to write thick loops. As loops become thicker, the number of temporary values grows, increasing register pressure. If registers spill, then memory traffic increases and code flow is disrupted. A thick loop with many temporary values may execute slower than an equivalent series of thin loops. The biggest gain will be achieved if the thick loop can be split into a series of independent loops eliminating the need to write and read temporary arrays. I found such an occasion in code 10 where I split the loop do i = 1, n do j = 1, m A24(j,i)= S24(j,i) * T24(j,i) + S25(j,i) * U25(j,i) B24(j,i)= S24(j,i) * T25(j,i) + S25(j,i) * U24(j,i) A25(j,i)= S24(j,i) * C24(j,i) + S25(j,i) * V24(j,i) B25(j,i)= S24(j,i) * U25(j,i) + S25(j,i) * V25(j,i) C24(j,i)= S26(j,i) * T26(j,i) + S27(j,i) * U26(j,i) D24(j,i)= S26(j,i) * T27(j,i) + S27(j,i) * V26(j,i) C25(j,i)= S27(j,i) * S28(j,i) + S26(j,i) * U28(j,i) D25(j,i)= S27(j,i) * T28(j,i) + S26(j,i) * V28(j,i) end do end do into two disjoint loops do i = 1, n do j = 1, m A24(j,i)= S24(j,i) * T24(j,i) + S25(j,i) * U25(j,i) B24(j,i)= S24(j,i) * T25(j,i) + S25(j,i) * U24(j,i) A25(j,i)= S24(j,i) * C24(j,i) + S25(j,i) * V24(j,i) B25(j,i)= S24(j,i) * U25(j,i) + S25(j,i) * V25(j,i) end do end do do i = 1, n do j = 1, m C24(j,i)= S26(j,i) * T26(j,i) + S27(j,i) * U26(j,i) D24(j,i)= S26(j,i) * T27(j,i) + S27(j,i) * V26(j,i) C25(j,i)= S27(j,i) * S28(j,i) + S26(j,i) * U28(j,i) D25(j,i)= S27(j,i) * T28(j,i) + S26(j,i) * V28(j,i) end do end do Conclusions Over the course of the last year, I have had the opportunity to work with over two dozen academic scientific programmers at leading research universities. Their research interests span a broad range of scientific fields. Except for two programs that relied almost exclusively on library routines (matrix multiply and fast Fourier transform), I was able to improve significantly the single processor performance of all codes. Improvements range from 2x to 15.5x with a simple average of 4.75x. Changes to the source code were at a very high level. I did not use sophisticated techniques or programming tools to discover inefficiencies or effect the changes. Only one code was parallel despite the availability of parallel systems to all developers. Clearly, we have a problem—personal scientific research codes are highly inefficient and not running parallel. The developers are unaware of simple optimization techniques to make programs run faster. They lack education in the art of code optimization and parallel programming. I do not believe we can fix the problem by publishing additional books or training manuals. To date, the developers in questions have not studied the books or manual available, and are unlikely to do so in the future. Short courses are a possible solution, but I believe they are too concentrated to be much use. The general concepts can be taught in a three or four day course, but that is not enough time for students to practice what they learn and acquire the experience to apply and extend the concepts to their codes. Practice is the key to becoming proficient at optimization. I recommend that graduate students be required to take a semester length course in optimization and parallel programming. We would never give someone access to state-of-the-art scientific equipment costing hundreds of thousands of dollars without first requiring them to demonstrate that they know how to use the equipment. Yet the criterion for time on state-of-the-art supercomputers is at most an interesting project. Requestors are never asked to demonstrate that they know how to use the system, or can use the system effectively. A semester course would teach them the required skills. Government agencies that fund academic scientific research pay for most of the computer systems supporting scientific research as well as the development of most personal scientific codes. These agencies should require graduate schools to offer a course in optimization and parallel programming as a requirement for funding. About the Author John Feo received his Ph.D. in Computer Science from The University of Texas at Austin in 1986. After graduate school, Dr. Feo worked at Lawrence Livermore National Laboratory where he was the Group Leader of the Computer Research Group and principal investigator of the Sisal Language Project. In 1997, Dr. Feo joined Tera Computer Company where he was project manager for the MTA, and oversaw the programming and evaluation of the MTA at the San Diego Supercomputer Center. In 2000, Dr. Feo joined Sun Microsystems as an HPC application specialist. He works with university research groups to optimize and parallelize scientific codes. Dr. Feo has published over two dozen research articles in the areas of parallel parallel programming, parallel programming languages, and application performance.

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  • Equivalent of public static final fields in Scala

    - by JT
    I'm learning Scala, and I can't figure out how to best express this simple Java class in Scala: public class Color { public static final Color BLACK = new Color(0, 0, 0); public static final Color WHITE = new Color(255, 255, 255); public static final Color GREEN = new Color(0, 0, 255); private static final int red; private static final int blue; private static final int green; public Color(int red, int blue, int green) { this.red = red; this.blue = blue; this.green = green; } // getters, et cetera } The best I have is the following: class Color(val red: Int, val blue: Int, val green: Int) object BLACK extends Color(0, 0, 0) object WHITE extends Color(255, 255, 255) object GREEN extends Color(0, 0, 255) But I lose the advantages of having BLACK, WHITE, and GREEN being tied to the Color namespace.

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