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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 Maps API Round-up

    Google Maps API Round-up This week, Mano Marks and Paul Saxman go over recent launches and things you might have missed with the Google Maps APIs, including the new Google Time Zone API, traffic estimates with the Directions API (for enterprise customers), and the Places Autocomplete API query results and data service enhancements. From: GoogleDevelopers Views: 0 0 ratings Time: 00:00 More in Education

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  • CodePlex learns to talk to other services!

    CodePlex is now able to talk to other services! For example, if you want CodePlex to tell Trello to update cards on your Trello board, it can do it. Or if you want CodePlex to notify your Campfire chat room when updates are pushed, it can do that too. To start off, we are going to be adding support for the following services: Campfire – Notify a Campfire chat room when commits occur HipChat – Notify a HipChat chat room when commits occur Trello – Add commit summaries to Trello cards by referencing those cards in commit messages Twitter – Notify your Twitter followers when updates are pushed to your project In addition, we will continue to support our existing integrations with Windows Azure – Continuously deploy to Windows Azure on pushes (For Git and Hg projects) AppHarbor – Continuously deploy to AppHarbor on pushes To set up these integrations for your project, navigate to the project settings page as a project coordinator, and click on the services section as seen below:   While we are starting with these six services, the infrastructure is now in place to allow us to quickly roll out new integrations. We would love to hear which services and integrations you would like to see most on our suggestions page. We realize that there are some services and URLs that only make sense for your project to send notifications to. To support this scenario, we plan to add generic web hooks in the near future. Have ideas on how to improve CodePlex? Please visit our suggestions page! Vote for existing ideas or submit a new one. As always you can reach out to the CodePlex team on Twitter @codeplex or reach me directly @Rick_Marron.    

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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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  • Detecting Installed .NET Framework Versions

    - by João Angelo
    A new year is upon us and it’s also time for me to end my blogging vacations and get back to the blogosphere. However, let’s start simple… and short. More specifically with a quick way to detect the installed .NET Framework versions on a machine. You just need to fire up Internet Explorer, write the following in the address bar and press enter: javascript:alert(navigator.userAgent) If for any reason you need to copy/paste the resulting information then use the next command instead: javascript:document.write(navigator.userAgent)

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  • Load Balance and Parallel Performance

    Load balancing an application workload among threads is critical to performance. However, achieving perfect load balance is non-trivial, and it depends on the parallelism within the application, workload, the number of threads, load balancing policy, and the threading implementation.

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  • Methodology to understanding JQuery plugin & API's developed by third parties

    - by Taoist
    I have a question about third party created JQuery plug ins and API's and the methodology for understanding them. Recently I downloaded the JQuery Masonry/Infinite scroll plug in and I couldn't figure out how to configure it based on the instructions. So I downloaded a fully developed demo, then manually deleted everything that wouldn't break the functionality. The code that was left allowed me to understand the plug in much greater detail than the documentation. I'm now having a similar issue with a plug in called JQuery knob. http://anthonyterrien.com/knob/ If you look at the JQuery Knob readme file it says this is working code: $(function() { $('.dial') .trigger( 'configure', { "min":10, "max":40, "fgColor":"#FF0000", "skin":"tron", "cursor":true } ); }); But as far as I can tell it isn't at all. The read me also says the Plug in uses Canvas. I am wondering if I am suppose to wrap this code in a canvas context or if this functionality is already part of the plug in. I know this kind of "question" might not fit in here but I'm a bit confused on the assumptions around reading these kinds of documentation and thought I would post the query regardless. Curious to see if this is due to my "newbi" programming experience or if this is something seasoned coders also fight with. Thank you. Edit In response to Tyanna's reply. I modified the code and it still doesn't work. I posted it below. I made sure that I checked the Google Console to insure the basics were taken care of, such as not getting a read-error on the library. <!DOCTYPE html> <meta charset="UTF-8"> <title>knob</title> <link rel="stylesheet" href="http://ajax.googleapis.com/ajax/libs/jqueryui/1.7.2/themes/hot-sneaks/jquery-ui.css" type="text/css" /> <script type="text/javascript" src="https://ajax.googleapis.com/ajax/libs/jquery/1.7.2/jquery.js" charset="utf-8"></script> <script src="https://ajax.googleapis.com/ajax/libs/jqueryui/1.8.21/jquery-ui.min.js"></script> <script src="js/jquery.knob.js"></script> <div id="button1">test </div> <script> $(function() { $("#button1").click(function () { $('.dial').trigger( 'configure', { "min":10, "max":40, "fgColor":"#FF0000", "skin":"tron", "cursor":true } ); }); }); </script>

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  • Naming your unit tests

    - by kerry
    When you create a test for your class, what kind of naming convention do you use for the tests? How thorough are your tests? I have lately switched from the conventional camel case test names to lower case letters with underscores. I have found this increases the readability and causes me to write better tests. A simple utility class: public class ArrayUtils { public static T[] gimmeASlice(T[] anArray, Integer start, Integer end) { // implementation (feeling lazy today) } } I have seen some people who would write a test like this: public class ArrayUtilsTest { @Test public void testGimmeASliceMethod() { // do some tests } } A more thorough and readable test would be: public class ArrayUtilsTest { @Test public void gimmeASlice_returns_appropriate_slice() { // ... } @Test public void gimmeASlice_throws_NullPointerException_when_passed_null() { // ... } @Test public void gimmeASlice_returns_end_of_array_when_slice_is_partly_out_of_bounds() { // ... } @Test public void gimmeASlice_returns_empty_array_when_slice_is_completely_out_of_bounds() { // ... } } Looking at this test, you have no doubt what the method is supposed to do. And, when one fails, you will know exactly what the issue is.

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  • Using runtime generic type reflection to build a smarter DAO

    - by kerry
    Have you ever wished you could get the runtime type of your generic class? I wonder why they didn’t put this in the language. It is possible, however, with reflection: Consider a data access object (DAO) (note: I had to use brackets b/c the arrows were messing with wordpress): public interface Identifiable { public Long getId(); } public interface Dao { public T findById(Long id); public void save(T obj); public void delete(T obj); } Using reflection, we can create a DAO implementation base class, HibernateDao, that will work for any object: import java.lang.reflect.Field; import java.lang.reflect.ParameterizedType; public class HibernateDao implements Dao { private final Class clazz; public HibernateDao(Session session) { // the magic ParameterizedType parameterizedType = (ParameterizedType) clazz.getGenericSuperclass(); return (Class) parameterizedType.getActualTypeArguments()[0]; } public T findById(Long id) { return session.get(clazz, id); } public void save(T obj) { session.saveOrUpdate(obj); } public void delete(T obj) { session.delete(obj); } } Then, all we have to do is extend from the class: public class BookDaoHibernateImpl extends HibernateDao { }

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  • Should I keep investing into data structures and algorithms?

    - by 4bu3li
    These days, I'm investing heavily in data structures and algorithms and trying to solve some programming puzzles. I'm trying to code and solve with Java and Clojure. Am I wasting my time? should I invest more in technologies and frameworks that I already know in order to gain deeper knowledge (the ins and the outs) and be able to code with them more quickly? By studying data structures and algorithms, am I going to become a better programmer or those subjects are only important during college years?

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  • Google I/O 2012 - Spatial Data Visualization

    Google I/O 2012 - Spatial Data Visualization Brendan Kenny, Enoch Lau Maps were among the first data visualizations, but they can also provide the backdrop for visualizing your own spatial data. In this session, we'll take a voyage through the world of map based data visualization, arming you with the tools you need to most effectively bring your data to life on a map using the Maps API v3. For all I/O 2012 sessions, go to developers.google.com From: GoogleDevelopers Views: 1053 26 ratings Time: 01:00:17 More in Science & Technology

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