Search Results

Search found 87956 results on 3519 pages for 'code hinting'.

facebook twitter digg google delicious technorati stumbleupon myspace wordpress linkedin gmail igoogle windows live tumblr viadeo yahoo buzz yahoo mail yahoo bookmarks favorites email print

Page 538/3519 | < Previous Page | 534 535 536 537 538 539 540 541 542 543 544 545  | Next Page >

  • Inside BackgroundWorker

    - by João Angelo
    The BackgroundWorker is a reusable component that can be used in different contexts, but sometimes with unexpected results. If you are like me, you have mostly used background workers while doing Windows Forms development due to the flexibility they offer for running a background task. They support cancellation and give events that signal progress updates and task completion. When used in Windows Forms, these events (ProgressChanged and RunWorkerCompleted) get executed back on the UI thread where you can freely access your form controls. However, the logic of the progress changed and worker completed events being invoked in the thread that started the background worker is not something you get directly from the BackgroundWorker, but instead from the fact that you are running in the context of Windows Forms. Take the following example that illustrates the use of a worker in three different scenarios: – Console Application or Windows Service; – Windows Forms; – WPF. using System; using System.ComponentModel; using System.Threading; using System.Windows.Forms; using System.Windows.Threading; class Program { static AutoResetEvent Synch = new AutoResetEvent(false); static void Main() { var bw1 = new BackgroundWorker(); var bw2 = new BackgroundWorker(); var bw3 = new BackgroundWorker(); Console.WriteLine("DEFAULT"); var unspecializedThread = new Thread(() => { OutputCaller(1); SynchronizationContext.SetSynchronizationContext( new SynchronizationContext()); bw1.DoWork += (sender, e) => OutputWork(1); bw1.RunWorkerCompleted += (sender, e) => OutputCompleted(1); // Uses default SynchronizationContext bw1.RunWorkerAsync(); }); unspecializedThread.IsBackground = true; unspecializedThread.Start(); Synch.WaitOne(); Console.WriteLine(); Console.WriteLine("WINDOWS FORMS"); var windowsFormsThread = new Thread(() => { OutputCaller(2); SynchronizationContext.SetSynchronizationContext( new WindowsFormsSynchronizationContext()); bw2.DoWork += (sender, e) => OutputWork(2); bw2.RunWorkerCompleted += (sender, e) => OutputCompleted(2); // Uses WindowsFormsSynchronizationContext bw2.RunWorkerAsync(); Application.Run(); }); windowsFormsThread.IsBackground = true; windowsFormsThread.SetApartmentState(ApartmentState.STA); windowsFormsThread.Start(); Synch.WaitOne(); Console.WriteLine(); Console.WriteLine("WPF"); var wpfThread = new Thread(() => { OutputCaller(3); SynchronizationContext.SetSynchronizationContext( new DispatcherSynchronizationContext()); bw3.DoWork += (sender, e) => OutputWork(3); bw3.RunWorkerCompleted += (sender, e) => OutputCompleted(3); // Uses DispatcherSynchronizationContext bw3.RunWorkerAsync(); Dispatcher.Run(); }); wpfThread.IsBackground = true; wpfThread.SetApartmentState(ApartmentState.STA); wpfThread.Start(); Synch.WaitOne(); } static void OutputCaller(int workerId) { Console.WriteLine( "bw{0}.{1} | Thread: {2} | IsThreadPool: {3}", workerId, "RunWorkerAsync".PadRight(18), Thread.CurrentThread.ManagedThreadId, Thread.CurrentThread.IsThreadPoolThread); } static void OutputWork(int workerId) { Console.WriteLine( "bw{0}.{1} | Thread: {2} | IsThreadPool: {3}", workerId, "DoWork".PadRight(18), Thread.CurrentThread.ManagedThreadId, Thread.CurrentThread.IsThreadPoolThread); } static void OutputCompleted(int workerId) { Console.WriteLine( "bw{0}.{1} | Thread: {2} | IsThreadPool: {3}", workerId, "RunWorkerCompleted".PadRight(18), Thread.CurrentThread.ManagedThreadId, Thread.CurrentThread.IsThreadPoolThread); Synch.Set(); } } Output: //DEFAULT //bw1.RunWorkerAsync | Thread: 3 | IsThreadPool: False //bw1.DoWork | Thread: 4 | IsThreadPool: True //bw1.RunWorkerCompleted | Thread: 5 | IsThreadPool: True //WINDOWS FORMS //bw2.RunWorkerAsync | Thread: 6 | IsThreadPool: False //bw2.DoWork | Thread: 5 | IsThreadPool: True //bw2.RunWorkerCompleted | Thread: 6 | IsThreadPool: False //WPF //bw3.RunWorkerAsync | Thread: 7 | IsThreadPool: False //bw3.DoWork | Thread: 5 | IsThreadPool: True //bw3.RunWorkerCompleted | Thread: 7 | IsThreadPool: False As you can see the output between the first and remaining scenarios is somewhat different. While in Windows Forms and WPF the worker completed event runs on the thread that called RunWorkerAsync, in the first scenario the same event runs on any thread available in the thread pool. Another scenario where you can get the first behavior, even when on Windows Forms or WPF, is if you chain the creation of background workers, that is, you create a second worker in the DoWork event handler of an already running worker. Since the DoWork executes in a thread from the pool the second worker will use the default synchronization context and the completed event will not run in the UI thread.

    Read the article

  • Google+ Platform Office Hours for June 13th, 2012

    Google+ Platform Office Hours for June 13th, 2012 Here are the show notes for this week's office hours. This week was devoted to your questions and our answers. We covered a wide breadth of topics. 0:43 - Introductions 2:54 - About Tabletop Forge's KickStarter - goo.gl 10:00 - Can I run multiple Hangout Apps at the same time? 12:28 - Is Google looking into adding more powerful Hangout moderation controls? 13:47 - How do you use Hangout Apps with Hangouts on Air? - +Fraser Cain's tips and tricks for Hangouts on Air: goo.gl 23:40 - I have an Android game. How do I port it to the Hangouts API? 27:57 - Pre-hangout Apps, Hangouts on Air pre-rolls, scheduling hangouts and other ways to help viewers find your Hangouts on Air 33:55 - How do I bookmark useful Google+ posts with Google+? 38:13 - Can you add a host ID field to the Hangouts API? When will the overlay garbage collection improve? 40:17 - Hand movement tracking as part of the Hangouts API Thanks to everyone who joined the hangout and asked questions on Google+! From: GoogleDevelopers Views: 698 18 ratings Time: 44:16 More in Science & Technology

    Read the article

  • Should I use JavaFx properties?

    - by Mike G
    I'm usually very careful to keep my Model, View, and Controller code separate. The thing is JavaFx properties are so convenient to bind them all together. The issue is that it makes my entire code design dependent on JavaFx, which I feel I should not being doing. I should be able to change the view without changing too much of the model and controller. So should I ignore the convenience of JavaFx properties, or should I embrace them and the fact that it reduces my codes flexibility.

    Read the article

  • How to remove the boundary effects arising due to zero padding in scipy/numpy fft?

    - by Omkar
    I have made a python code to smoothen a given signal using the Weierstrass transform, which is basically the convolution of a normalised gaussian with a signal. The code is as follows: #Importing relevant libraries from __future__ import division from scipy.signal import fftconvolve import numpy as np def smooth_func(sig, x, t= 0.002): N = len(x) x1 = x[-1] x0 = x[0] # defining a new array y which is symmetric around zero, to make the gaussian symmetric. y = np.linspace(-(x1-x0)/2, (x1-x0)/2, N) #gaussian centered around zero. gaus = np.exp(-y**(2)/t) #using fftconvolve to speed up the convolution; gaus.sum() is the normalization constant. return fftconvolve(sig, gaus/gaus.sum(), mode='same') If I run this code for say a step function, it smoothens the corner, but at the boundary it interprets another corner and smoothens that too, as a result giving unnecessary behaviour at the boundary. I explain this with a figure shown in the link below. Boundary effects This problem does not arise if we directly integrate to find convolution. Hence the problem is not in Weierstrass transform, and hence the problem is in the fftconvolve function of scipy. To understand why this problem arises we first need to understand the working of fftconvolve in scipy. The fftconvolve function basically uses the convolution theorem to speed up the computation. In short it says: convolution(int1,int2)=ifft(fft(int1)*fft(int2)) If we directly apply this theorem we dont get the desired result. To get the desired result we need to take the fft on a array double the size of max(int1,int2). But this leads to the undesired boundary effects. This is because in the fft code, if size(int) is greater than the size(over which to take fft) it zero pads the input and then takes the fft. This zero padding is exactly what is responsible for the undesired boundary effects. Can you suggest a way to remove this boundary effects? I have tried to remove it by a simple trick. After smoothening the function I am compairing the value of the smoothened signal with the original signal near the boundaries and if they dont match I replace the value of the smoothened func with the input signal at that point. It is as follows: i = 0 eps=1e-3 while abs(smooth[i]-sig[i])> eps: #compairing the signals on the left boundary smooth[i] = sig[i] i = i + 1 j = -1 while abs(smooth[j]-sig[j])> eps: # compairing on the right boundary. smooth[j] = sig[j] j = j - 1 There is a problem with this method, because of using an epsilon there are small jumps in the smoothened function, as shown below: jumps in the smooth func Can there be any changes made in the above method to solve this boundary problem?

    Read the article

  • Google I/O 2012 - A Master Class in Map Styling

    Google I/O 2012 - A Master Class in Map Styling Scott Shawcroft, Jonah Jones Custom Styled Maps allow developers to customize the look and feel of the underlying Google Maps tiles. This makes it really easy to make a great looking map. You can tailor your map to your message, to your color scheme, or to help emphasize your data. In this class, master maps designers will help you build beautiful, elegant styles that make your maps work for you. For all I/O 2012 sessions, go to developers.google.com From: GoogleDevelopers Views: 23 0 ratings Time: 38:21 More in Science & Technology

    Read the article

  • 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.

    Read the article

  • NetBeans IDE 7.3 Knows Null

    - by Geertjan
    What's the difference between these two methods, "test1" and "test2"? public int test1(String str) {     return str.length(); } public int test2(String str) {     if (str == null) {         System.err.println("Passed null!.");         //forgotten return;     }     return str.length(); } The difference, or at least, the difference that is relevant for this blog entry, is that whoever wrote "test2" apparently thinks that the variable "str" may be null, though did not provide a null check. In NetBeans IDE 7.3, you see this hint for "test2", but no hint for "test1", since in that case we don't know anything about the developer's intention for the variable and providing a hint in that case would flood the source code with too many false positives:  Annotations are supported in understanding how a piece of code is intended to be used. If method return types use @Nullable, @NullAllowed, @CheckForNull, the value is considered to be "strongly possible to be null", as well as if the variable is tested to be null, as shown above. When using @NotNull, @NonNull, @Nonnull, the value is considered to be non-null. (The exact FQNs of the annotations are ignored, only simple names are checked.) Here are examples showing where the hints are displayed for the non-null hints (the "strongly possible to be null" hints are not shown below, though you can see one of them in the screenshot above), together with a comment showing what is shown when you hover over the hint: There isn't a "one size fits all" refactoring for these various instances relating to null checks, hence you can't do an automated refactoring across your code base via tools in NetBeans IDE, as shown yesterday for class member reordering across code bases. However, you can, instead, go to Source | Inspect and then do a scan throughout a scope (e.g., current file/package/project or combinations of these or all open projects) for class elements that the IDE identifies as potentially having a problem in this area: Thanks to Jan Lahoda, who reports that this currently also works in NetBeans IDE 7.3 dev builds for fields but that may need to be disabled since right now too many false positives are returned, for help with the info above and any misunderstandings are my own fault!

    Read the article

  • Life, Identity, and Everything

    Life, Identity, and Everything Tim Bray is the Developer Advocate, and Breno de Madeiros is the tech lead, in the group at Google that does authentication and authorization APIs; specifically, those involving OAuth and OpenID. Breno also has his name on the front of a few of the OAuth RFCs. We're going to talk for a VERY few (less than 10) minutes on why OAuth is a good idea, and a couple of things we're working on right now to help do away with passwords. After that, ask us anything. From: GoogleDevelopers Views: 0 0 ratings Time: 30:00 More in Science & Technology

    Read the article

  • Google I/O 2012 - How to Build Apps that Love Each Other with Web Intents

    Google I/O 2012 - How to Build Apps that Love Each Other with Web Intents Paul Kinlan, James Hawkins Web Intents allows you to build applications that integrate with one another with an ease that has never been seen on the web before. In this session we will show you how to connect applications using Web Intents and how to best integrate with the many actions available in Web Intents such as editing, saving and sharing. For all I/O 2012 sessions, go to developers.google.com From: GoogleDevelopers Views: 1394 15 ratings Time: 57:48 More in Science & Technology

    Read the article

  • 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.

    Read the article

  • 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.

    Read the article

facebook twitter digg google delicious technorati stumbleupon myspace wordpress linkedin gmail igoogle windows live tumblr viadeo yahoo buzz yahoo mail yahoo bookmarks favorites email print

< Previous Page | 534 535 536 537 538 539 540 541 542 543 544 545  | Next Page >