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Juste publié ! Intel® Xeon Phi™ Coprocessor High Performance Programming 
Apprenez les fondements de la programmation pour cette nouvelle architecture et les nouveaux produits. Nouveau !
Intel® System Studio
Intel® System Studio est une suite exhaustive d’outils intégrés de développement de logiciels qui peut accélérer la mise sur le marché, renforcer la fiabilité des systèmes et améliorer l’efficacité énergétique et les performances. Nouveau !
Au cas où vous l’avez manqué – Rediffusion du webinaire en direct de deux jours
Introduction au développement d’applications hautes performances pour processeurs Intel® Xeon® et coprocesseurs Intel® Xeon Phi™.
Structured Parallel Programming
Les auteurs Michael McCool, Arch D. Robison et James Reinders utilisent une approche basée sur des modèles structurés qui devrait rendre le sujet accessible à tous les développeurs de logiciels.

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Intel® Parallel Studio

Intel® Parallel Studio, qui apporte aux développeurs Microsoft Visual Studio* C/C++ un traitement parallèle de bout en bout simplifié, fournit des outils avancés permettant d’optimiser les applications clientes pour un traitement multicœur et à nombreux cœurs.

Produits Intel® de développement logiciel ›

Explorez tous les outils qui vous aideront à optimiser vos applications pour l’architecture Intel. Certains outils sont disponibles pour une période d’évaluation gratuite de 45 jours.

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Trouvez des guides et des informations d'assistance sur les outils Intel.

Intel® Xeon Phi™ Coprocessor code named “Knights Landing” - Application Readiness
Par Indraneil Gokhale (Intel)Publié le 09/15/20140
As part of the application readiness efforts for future Intel® Xeon® processors and Intel® Xeon Phi™ coprocessors (code named Knights Landing), developers are interested in improving two key aspects of their workloads: Vectorization/code generation Thread parallelism This article mainly talks a...
Easy SIMD through Wrappers
Par adminPublié le 03/27/20150
SIMD operations are widely used for 3D graphics applications. This tutorial provides new insights into SIMD by comparing SIMD lanes and CPU threads, and steps you through the process of creating a simple, straightforward SIMD implementation in your own code.
Abaqus/Standard Performance Case Study on Intel® Xeon® E5-2600 v3 Product Family
Par Khang Nguyen (Intel)Publié le 03/27/20150
Background The whole point of simulation is to model the behavior of a design and potential changes against various conditions to determine whether we are getting an expected response; and simulation in software is far cheaper than building hardware and performing a physical simulation and modif...
Avoid frequency drop in GPU cores when executing applications in Heterogeneous mode
Par Anoop Madhusoodhanan Prabha (Intel)Publié le 03/23/20150
Introduction Intel(R) C++ Compiler 15.0 provides a feature which enables offloading general purpose compute kernels to processor graphics. This feature enables the processor graphics silicon area for general purpose computing. The key idea is to utilize the compute power of both CPU cores and GP...
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[Acceler8 '12] Scaling fast sequential algorithms using MapReduce
Par seviyor Publié le 30/05/12 0
Parallel algorithm vs. work in parallel As many of the forum posts have shown, fast algorithms for solving the problem of maximal common substrings gave good results on the benchmarck but didn't really scale with the number of threads. This is because those sub-square (linear or n*logn) algorithm...
Create a Ubuntu 11.04 LiveUSB to use Intel® Parallel Studio XE
Par Xavier H. (Intel) Publié le 14/05/12 0
You need a license for Intel® Parallel Studio XE for Linux and and at least a 4GB USB Key. Get an ISO image of Ubuntu 11.04. Create a new Ubuntu 11.04 LiveUSB, with persistence mode enabled (you can specify a size of 1mo for the persistence file, you will overwrite it with a ~3Go file in the next...
Getting system parameters in order to improve data structures
Par andreib Publié le 13/05/12 0
Dear programmer, there are a lot of situations when you have to deal with very efficient data structures to get a good performance. An important characteristic of a data structure is granularity. How big the data structure should be? Which is the optimum size of its elements? Of course there is ...
Retour d'expérience concours Acceler'8
Par Maxime RIVIERE Publié le 01/02/12 1
La nouvelle édition du concours acceler'8 a pris fin il y'a un peu plus d'un mois. Contrairement au concours précédent, nous n'avons pas publié d'article. Il faudrait que nous le fassions à l'occasion. C'etait une part intéressante du concours précédent. Les contraintes de la vie courante reprenn...
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Intel® Parallel Studio XE SP1 & Intel® Cluster Studio XE SP1
Par kathy-farrel (Intel)0
Intel® Parallel Studio XE SP1 & Intel® Cluster Studio XE SP1 - What's New - Webinar Tuesday, September 17 9am PDT Please join us for a technical presentation on the new features found in the recently released Intel® Parallel Studio XE 2013 SP1 Intel® Cluster Studio XE SP1. This release includes support for compilers and performance analysis on Intel® Xeon Phi™ on Windows*. The technical presentation will briefly cover new features for both C++ and Fortran on Linux*, Windows*, and OS X* operating systems as well as error checking and performance profiling tools. Learn how to efficiently boost your application performance! Not too late! - Register Now  Learn about Upcoming Webinars
Parallel Image Processing in OpenMP - Image Blocks
Par Royi0
Hello, I'm doing my first steps in the OpenMP world. I have an image I want to apply a filter on. Since the image is large I wanted to break it into non overlapping parts and apply the filter on each independently in parallel. Namely, I'm creating 4 images I want to have different threads. I'm using Intel IPP for the handling of the images and the function to apply on each sub image. I described the code here: The problem is I tried both sections and parallel for and got only 20% improvement. What am I doing wrong? How can I tell each "Worker" that though data is taken from the same array, it is safe to read (Data won't change) and write (Each worker has exclusive approach to its part of the result image). Thank You.
Par Rafael R.2
Hi, In our university bought a machine with CO-PROCESSOR PHI. The description in the site: It is reported that there is no support JAVA yet. The answer is 2013 and we are already in 2015. Is there a Java option for coding? Tks Rafael
Intel® Xeon Phi™ Coprocessor Developer Training Coming to a City Near You in 2015
Par Mike Pearce (Intel)0
Mixing kernel space and userspace in a new kernel.
Par Jog L.0
Hello, I was thinking of creating an open source kernel (with block already written in the linux kernel obviously). Now I would like to hear from experts what are the dangers to run in ring0 if no users and no external connections are done. We are in a situation in which the processor is isolated from the whole world. No one can mess with it. all the processes running on top of it have to register and are created and compiled by root using a specific memory range. No process can be launched without the acceptation of root. No human accesses it. The code running inside is reviewed and we have facilities to be sure no other memory range than the one we expect each process to use can be used. That is for the -restrictive- context. Now, could we imagine it be possible for such a kernel to exist or are there some limitations that I don't predict ? The kernel is to be massively specialized, hence the "almost starting from scratch". Thanks for your insights, Jog
linking with two versions of mkl (multi threaded and single threaded) in one application
Par Michal K.3
Hi, Is it possible to use both the single threaded version of mkl library and the multi threaded version of mkl in one application? I need the single threaded version to use with PLASMA library, yet at some other part of my code, I need use mkl PARDISO, for which I need the multi threaded version. Any help will be greatly appreciated. Cheers Michal  
PCIe 3.0 reference clock jitter tool
Par Sonal C.0
Where can I access the Intel PCIe clock jitter tool
Memory to CPU (mov) bandwidth limitations
Par albus d.3
(sorry for weak english I am not native english, Not sure if right forum, first time here - This is general about some hardware limits i do not understand technical reason and I would very like to know) We have now parallelised SIMD arithmetic (like 8 float mulls or divisions in one step) theoretical (but also nearly practical) arithmetical bandwidth per core is thus like 4GHz * 8 floats = about 30 GFLOPS per core or something like that But we still AFAIK have quite low RAM to CPU bandwidth at the level of read or write of 1 or 2 int of float per nanosecond, such ram-2-cpu bandwidth when i am testing it is like only 2 GLOP per second per core or something like that; (both those values are rough but this difference seem to be physical truth at least from my experience) I mean arithmetic can be paralelised (like 8-vectorised) but load/store movs are not - thus SIMD paralistation has obly a fraction of its potential power This is extremally crusial to increase this memory bandwith (muc...
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