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  • Apps Script Office Hours - September 13, 2012

    Apps Script Office Hours - September 13, 2012 In this week's episode of Google Apps Script office hours, Jan and Arun: - Introduce the Google Apps Script app that was recently published in the Chrome Web Store: chrome.google.com - Answer a variety of questions from the Google Moderator. - Answer live questions about UiApp, triggers, ScriptDb, and other topics. To find out when the next office hours will be held, visit developers.google.com From: GoogleDevelopers Views: 221 7 ratings Time: 17:26 More in Science & Technology

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  • Google Top Geek E05

    Google Top Geek E05 In Spanish! Google Top Geek (GTG) es un show semanal que generamos desde México con noticias, las tendencias en búsquedas y YouTube en América Latina, así como referencias a apps y eventos interesantes. GTG se transmite los lunes al medio día, 12 pm, desde Google Developers Live. Guión del programa Esta semana 1. Geeks interactuando y socializando en el mundo real, eso justamente es lo que ha logrado el juego masivo Ingress que liberó Google recientemente. Tienen que escoger un bando: resistance o enlightened, el proyecto Niantic. Campos de energía, elementos, intriga, combate, ... Y lo mejor de todo: mucha diversión. Cuando obtengan su código, si están del lado correcto, pueden encontrarnos en Ingress Enlightened Latin America +page en Google+. 2. Reality show para desarrolladores en Argentina: +Next Level, 40 estudiantes y profesionales de TI trabajarán siete días con cámaras todo el tiempo, expertos de toda América Latina via Google Hangouts... Del 26 de noviembre al 2 de diciembre, en la ciudad de Tandil. 3. Google Apps for Business Un tema relativamente nuevo en el mundo empresarial en nuestra región es la nube y cómo aprovecharla mejor. Google Apps for Business es un servicio basado en la nube que provee Mensajería y Colaboración a través de los productos que todos conocemos de Google pero con el nivel de controles y auditoría que requieren las empresas. El enfoque de Google es y siempre ha sido la satisfacción de nuestros usuarios y Google Apps for Business le <b>...</b> From: GoogleDevelopers Views: 1 0 ratings Time: 15:39 More in Science & Technology

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  • parallel_for_each from amp.h – part 1

    - by Daniel Moth
    This posts assumes that you've read my other C++ AMP posts on index<N> and extent<N>, as well as about the restrict modifier. It also assumes you are familiar with C++ lambdas (if not, follow my links to C++ documentation). Basic structure and parameters Now we are ready for part 1 of the description of the new overload for the concurrency::parallel_for_each function. The basic new parallel_for_each method signature returns void and accepts two parameters: a grid<N> (think of it as an alias to extent) a restrict(direct3d) lambda, whose signature is such that it returns void and accepts an index of the same rank as the grid So it looks something like this (with generous returns for more palatable formatting) assuming we are dealing with a 2-dimensional space: // some_code_A parallel_for_each( g, // g is of type grid<2> [ ](index<2> idx) restrict(direct3d) { // kernel code } ); // some_code_B The parallel_for_each will execute the body of the lambda (which must have the restrict modifier), on the GPU. We also call the lambda body the "kernel". The kernel will be executed multiple times, once per scheduled GPU thread. The only difference in each execution is the value of the index object (aka as the GPU thread ID in this context) that gets passed to your kernel code. The number of GPU threads (and the values of each index) is determined by the grid object you pass, as described next. You know that grid is simply a wrapper on extent. In this context, one way to think about it is that the extent generates a number of index objects. So for the example above, if your grid was setup by some_code_A as follows: extent<2> e(2,3); grid<2> g(e); ...then given that: e.size()==6, e[0]==2, and e[1]=3 ...the six index<2> objects it generates (and hence the values that your lambda would receive) are:    (0,0) (1,0) (0,1) (1,1) (0,2) (1,2) So what the above means is that the lambda body with the algorithm that you wrote will get executed 6 times and the index<2> object you receive each time will have one of the values just listed above (of course, each one will only appear once, the order is indeterminate, and they are likely to call your code at the same exact time). Obviously, in real GPU programming, you'd typically be scheduling thousands if not millions of threads, not just 6. If you've been following along you should be thinking: "that is all fine and makes sense, but what can I do in the kernel since I passed nothing else meaningful to it, and it is not returning any values out to me?" Passing data in and out It is a good question, and in data parallel algorithms indeed you typically want to pass some data in, perform some operation, and then typically return some results out. The way you pass data into the kernel, is by capturing variables in the lambda (again, if you are not familiar with them, follow the links about C++ lambdas), and the way you use data after the kernel is done executing is simply by using those same variables. In the example above, the lambda was written in a fairly useless way with an empty capture list: [ ](index<2> idx) restrict(direct3d), where the empty square brackets means that no variables were captured. If instead I write it like this [&](index<2> idx) restrict(direct3d), then all variables in the some_code_A region are made available to the lambda by reference, but as soon as I try to use any of those variables in the lambda, I will receive a compiler error. This has to do with one of the direct3d restrictions, where only one type can be capture by reference: objects of the new concurrency::array class that I'll introduce in the next post (suffice for now to think of it as a container of data). If I write the lambda line like this [=](index<2> idx) restrict(direct3d), all variables in the some_code_A region are made available to the lambda by value. This works for some types (e.g. an integer), but not for all, as per the restrictions for direct3d. In particular, no useful data classes work except for one new type we introduce with C++ AMP: objects of the new concurrency::array_view class, that I'll introduce in the post after next. Also note that if you capture some variable by value, you could use it as input to your algorithm, but you wouldn’t be able to observe changes to it after the parallel_for_each call (e.g. in some_code_B region since it was passed by value) – the exception to this rule is the array_view since (as we'll see in a future post) it is a wrapper for data, not a container. Finally, for completeness, you can write your lambda, e.g. like this [av, &ar](index<2> idx) restrict(direct3d) where av is a variable of type array_view and ar is a variable of type array - the point being you can be very specific about what variables you capture and how. So it looks like from a large data perspective you can only capture array and array_view objects in the lambda (that is how you pass data to your kernel) and then use the many threads that call your code (each with a unique index) to perform some operation. You can also capture some limited types by value, as input only. When the last thread completes execution of your lambda, the data in the array_view or array are ready to be used in the some_code_B region. We'll talk more about all this in future posts… (a)synchronous Please note that the parallel_for_each executes as if synchronous to the calling code, but in reality, it is asynchronous. I.e. once the parallel_for_each call is made and the kernel has been passed to the runtime, the some_code_B region continues to execute immediately by the CPU thread, while in parallel the kernel is executed by the GPU threads. However, if you try to access the (array or array_view) data that you captured in the lambda in the some_code_B region, your code will block until the results become available. Hence the correct statement: the parallel_for_each is as-if synchronous in terms of visible side-effects, but asynchronous in reality.   That's all for now, we'll revisit the parallel_for_each description, once we introduce properly array and array_view – coming next. Comments about this post by Daniel Moth welcome at the original blog.

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  • Running C++ AMP kernels on the CPU

    - by Daniel Moth
    One of the FAQs we receive is whether C++ AMP can be used to target the CPU. For targeting multi-core we have a technology we released with VS2010 called PPL, which has had enhancements for VS 11 – that is what you should be using! FYI, it also has a Linux implementation via Intel's TBB which conforms to the same interface. When you choose to use C++ AMP, you choose to take advantage of massively parallel hardware, through accelerators like the GPU. Having said that, you can always use the accelerator class to check if you are running on a system where the is no hardware with a DirectX 11 driver, and decide what alternative code path you wish to follow.  In fact, if you do nothing in code, if the runtime does not find DX11 hardware to run your code on, it will choose the WARP accelerator which will run your code on the CPU, taking advantage of multi-core and SSE2 (depending on the CPU capabilities WARP also uses SSE3 and SSE 4.1 – it does not currently use AVX and on such systems you hopefully have a DX 11 GPU anyway). A few things to know about WARP It is our fallback CPU solution, not intended as a primary target of C++ AMP. WARP stands for Windows Advanced Rasterization Platform and you can read old info on this MSDN page on WARP. What is new in Windows 8 Developer Preview is that WARP now supports DirectCompute, which is what C++ AMP builds on. It is not currently clear if we will have a CPU fallback solution for non-Windows 8 platforms when we ship. When you create a WARP accelerator, its is_emulated property returns true. WARP does not currently support double precision.   BTW, when we refer to WARP, we refer to this accelerator described above. If we use lower case "warp", that refers to a bunch of threads that run concurrently in lock step and share the same instruction. In the VS 11 Developer Preview, the size of warp in our Ref emulator is 4 – Ref is another emulator that runs on the CPU, but it is extremely slow not intended for production, just for debugging. Comments about this post by Daniel Moth welcome at the original blog.

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  • Google Chrome Extensions: Launch Event (part 5)

    Google Chrome Extensions: Launch Event (part 5) Video Footage from the Google Chrome Extensions launch event on 12/09/09. Xmarks, ebay and Google Translate present their experience developing an extension for Google Chrome. From: GoogleDevelopers Views: 3039 18 ratings Time: 10:30 More in Science & Technology

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  • How do I get localized names of application in python?

    - by Mystic-Mirage
    This code gives me only English application name if .desktop file does not have "Name[*]" options (like in totem.desktop) but only "X-Ubuntu-Gettext-Domain: totem": from gi.repository import Gio app = Gio.app_info_get_default_for_type('video/x-flv', True) print app.get_name() This like code gives me proper result for vlc.desktop. Ubuntu Dash shows proper localized names for all applications. How do I get localized names of application in python? Sorry for my English.

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  • YouTube Developers Live: Magnify.net

    YouTube Developers Live: Magnify.net Questions? Please use Google Moderator rather than commenting on this video: goo.gl Join us in a discussion with Steve Rosenbaum (author of _Curation Nation_) and Kimberly Peterson from Magnify.net. We'll discuss content curation and how developers can use the YouTube API to power their own creation applications. From: GoogleDevelopers Views: 0 3 ratings Time: 00:00 More in Science & Technology

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  • Google Top Geek E07

    Google Top Geek E07 In Spanish! Noticias: 1. Gráfico de conocimiento ahora en español y varios idiomas más. Totalmente localizado. 2. Nueva versión de Snapseed para iOS y Android. Gmail para Android y la versión 2.0 para iOS. Nuevo estilo para YouTube. 3. 500Millones de usuarios en Google+ y una nueva característica: comunidades. Las búsquedas de la semana y lo más visto en YouTube. Recomendamos Picket, una app para Android que funciona en México y te da la cartelera en cines. Noticias para desarrolladores: 1. Mejores mapas para apps de Android, nuevo API. 2. Una imagen dice más que mil palabras: Place Photos y Radar Search Ligas y más información en el blog: programa-con-google.blogspot.com From: GoogleDevelopers Views: 80 11 ratings Time: 18:09 More in Science & Technology

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  • Is Cygwin or Windows Command Prompt preferable for getting a consistent terminal experience for development?

    - by Paul Hazen
    The question: Which is better, installing cygwin or one of its cousins on all my windows machines to have a consistent terminal experience across all my development machines, or becoming well trained in the skill of mentally switching from linux terminal to windows command prompt? Systems I use: OSX Lion on a Macbook Air Windows 8 on a desktop Windows 7 on the same desktop Fedora 16 on the same desktop What I'm trying to accomplish Configure an entirely consistent (or consistent enough) terminal experience across all my machines. "enough" in this context is clearly subjective. Please be clear in your answer why the configuration you suggest is consistent enough. One more thing to keep in mind: While I do write a lot of code intended to run on Windows (actually code that runs on Windows Phone which necessitates a windows machine), I also write a lot of Java code, and prefer to do so in vim. I test a local repo in Java on my windows machine, and push to another test machine running ubuntu later in the development stage. When I push to the ubuntu machine, I'm exclusively in terminal, since I'm accessing it via SSH. Summary, with more accurate question: Is there a good way to accomplish what I'm trying to do, or is it better to get accustomed to remembering different commands based on the system I'm on? Which (if either) is considered "best practice" by the development community? Alternatively, for a consistent development experience, would it be better to write all my code SSHed into another machine, and move things to windows for compile / build only when I needed to? That seems like too much work... but could be a solution. Update: While there are insightful responses below, I have yet to hear an answer that talks about why any given solution is superior. Cygwin/GnuWin32 is certainly a way to accomplish a similar experience on all platforms, but since I'm just learning all things command line, I don't want to set myself up to do a lot of relearning/unlearning in the future. Cygwin/GnuWin32 has its peculiarities I would imagine, and being aware of how that set up works on Windows is a learning curve. Additionally, using Cygwin/GnuWin32 robs me of learning the benefits of PowerShell. As a newcomer to working in a command line, which path should I choose to minimize having to relearn/unlearn things in the future? or as my first paragraph poses: [is it better to use Cygwin] ...or [become] well trained in the skill of mentally switching from linux terminal to windows command prompt?

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  • Google I/O 2012 - Dart - A Modern Web Language

    Google I/O 2012 - Dart - A Modern Web Language Lars Bak, Kasper Lund The two creators of Dart will discuss the rationale behind Dart's design and its impact on web scalability and performance. They'll also present how Dart helps developers innovate by increasing their productivity without breaking backwards compatibility. For all I/O 2012 sessions, go to developers.google.com From: GoogleDevelopers Views: 2066 36 ratings Time: 01:03:40 More in Science & Technology

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