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  • Restrict whole system on certain cores except a few process?

    - by icando
    Hi I am running some latency sensitive program on a Linux machine (more specifically, CentOS 6), and I don't want the threads of the process being preempted. So in my plan, the first step is to set cpu affinity of the threads so that threads are running on separate cores, so they don't preempt each other. Then the second step is to make sure other processes in the system not running on these cores. So my question is: is it possible to restrict the whole system running on certain cores, except this process? This should apply to any newly created processes in the future.

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  • What is the difference between "render a view" and send the response using the Response's method "sendResponse()"?

    - by Green
    I've asked a question about what is "rendering a view". Got some answers: Rendering a view means showing up a View eg html part to user or browser. and So by rendering a view, the MVC framework has handled the data in the controller and done the backend work in the model, and then sends that data to the View to be output to the user. and render just means to emit. To print. To echo. To write to some source (probably stdout). but don't understand then the difference between rendering a view and using the Response class to send the output to the user using its sendResponse() method. If render a view means to echo the output to the user then why sendResponse() exists and vise versa? sendResponse() exactly sends headers and after headers outputs the body. They solve the same tasks but differently? What is the difference?

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  • How do I make time?

    - by SystemNetworks
    I wanted to output a text for a certain amount of time. One way is to use threads. Are there any other ways? I can't use threads for slick2d. This is my code when I use threads for slick: package javagame; import org.newdawn.slick.GameContainer; import org.newdawn.slick.Graphics; import org.newdawn.slick.Image; import java.util.Random; import org.newdawn.slick.Input; import org.newdawn.slick.*; import org.newdawn.slick.state.*; import org.lwjgl.input.Mouse; public class thread1 implements Runnable { String showUp; int timeLeft; public thread1(String s) { s = showUp; } public void run(Graphics g) { try { g.drawString("%s is sleeping %d", 500, 500); Thread.sleep(timeLeft); g.drawString("%s is awake", 600,600); } catch(Exception e) { } } @Override public void run() { // TODO Auto-generated method stub run(); } } It auto generates a new run() And also when I call it to my main class it has stack overflow!

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  • Matrix Multiplication with C++ AMP

    - by Daniel Moth
    As part of our API tour of C++ AMP, we looked recently at parallel_for_each. I ended that post by saying we would revisit parallel_for_each after introducing array and array_view. Now is the time, so this is part 2 of parallel_for_each, and also a post that brings together everything we've seen until now. The code for serial and accelerated Consider a naïve (or brute force) serial implementation of matrix multiplication  0: void MatrixMultiplySerial(std::vector<float>& vC, const std::vector<float>& vA, const std::vector<float>& vB, int M, int N, int W) 1: { 2: for (int row = 0; row < M; row++) 3: { 4: for (int col = 0; col < N; col++) 5: { 6: float sum = 0.0f; 7: for(int i = 0; i < W; i++) 8: sum += vA[row * W + i] * vB[i * N + col]; 9: vC[row * N + col] = sum; 10: } 11: } 12: } We notice that each loop iteration is independent from each other and so can be parallelized. If in addition we have really large amounts of data, then this is a good candidate to offload to an accelerator. First, I'll just show you an example of what that code may look like with C++ AMP, and then we'll analyze it. It is assumed that you included at the top of your file #include <amp.h> 13: void MatrixMultiplySimple(std::vector<float>& vC, const std::vector<float>& vA, const std::vector<float>& vB, int M, int N, int W) 14: { 15: concurrency::array_view<const float,2> a(M, W, vA); 16: concurrency::array_view<const float,2> b(W, N, vB); 17: concurrency::array_view<concurrency::writeonly<float>,2> c(M, N, vC); 18: concurrency::parallel_for_each(c.grid, 19: [=](concurrency::index<2> idx) restrict(direct3d) { 20: int row = idx[0]; int col = idx[1]; 21: float sum = 0.0f; 22: for(int i = 0; i < W; i++) 23: sum += a(row, i) * b(i, col); 24: c[idx] = sum; 25: }); 26: } First a visual comparison, just for fun: The beginning and end is the same, i.e. lines 0,1,12 are identical to lines 13,14,26. The double nested loop (lines 2,3,4,5 and 10,11) has been transformed into a parallel_for_each call (18,19,20 and 25). The core algorithm (lines 6,7,8,9) is essentially the same (lines 21,22,23,24). We have extra lines in the C++ AMP version (15,16,17). Now let's dig in deeper. Using array_view and extent When we decided to convert this function to run on an accelerator, we knew we couldn't use the std::vector objects in the restrict(direct3d) function. So we had a choice of copying the data to the the concurrency::array<T,N> object, or wrapping the vector container (and hence its data) with a concurrency::array_view<T,N> object from amp.h – here we used the latter (lines 15,16,17). Now we can access the same data through the array_view objects (a and b) instead of the vector objects (vA and vB), and the added benefit is that we can capture the array_view objects in the lambda (lines 19-25) that we pass to the parallel_for_each call (line 18) and the data will get copied on demand for us to the accelerator. Note that line 15 (and ditto for 16 and 17) could have been written as two lines instead of one: extent<2> e(M, W); array_view<const float, 2> a(e, vA); In other words, we could have explicitly created the extent object instead of letting the array_view create it for us under the covers through the constructor overload we chose. The benefit of the extent object in this instance is that we can express that the data is indeed two dimensional, i.e a matrix. When we were using a vector object we could not do that, and instead we had to track via additional unrelated variables the dimensions of the matrix (i.e. with the integers M and W) – aren't you loving C++ AMP already? Note that the const before the float when creating a and b, will result in the underling data only being copied to the accelerator and not be copied back – a nice optimization. A similar thing is happening on line 17 when creating array_view c, where we have indicated that we do not need to copy the data to the accelerator, only copy it back. The kernel dispatch On line 18 we make the call to the C++ AMP entry point (parallel_for_each) to invoke our parallel loop or, as some may say, dispatch our kernel. The first argument we need to pass describes how many threads we want for this computation. For this algorithm we decided that we want exactly the same number of threads as the number of elements in the output matrix, i.e. in array_view c which will eventually update the vector vC. So each thread will compute exactly one result. Since the elements in c are organized in a 2-dimensional manner we can organize our threads in a two-dimensional manner too. We don't have to think too much about how to create the first argument (a grid) since the array_view object helpfully exposes that as a property. Note that instead of c.grid we could have written grid<2>(c.extent) or grid<2>(extent<2>(M, N)) – the result is the same in that we have specified M*N threads to execute our lambda. The second argument is a restrict(direct3d) lambda that accepts an index object. Since we elected to use a two-dimensional extent as the first argument of parallel_for_each, the index will also be two-dimensional and as covered in the previous posts it represents the thread ID, which in our case maps perfectly to the index of each element in the resulting array_view. The kernel itself The lambda body (lines 20-24), or as some may say, the kernel, is the code that will actually execute on the accelerator. It will be called by M*N threads and we can use those threads to index into the two input array_views (a,b) and write results into the output array_view ( c ). The four lines (21-24) are essentially identical to the four lines of the serial algorithm (6-9). The only difference is how we index into a,b,c versus how we index into vA,vB,vC. The code we wrote with C++ AMP is much nicer in its indexing, because the dimensionality is a first class concept, so you don't have to do funny arithmetic calculating the index of where the next row starts, which you have to do when working with vectors directly (since they store all the data in a flat manner). I skipped over describing line 20. Note that we didn't really need to read the two components of the index into temporary local variables. This mostly reflects my personal choice, in some algorithms to break down the index into local variables with names that make sense for the algorithm, i.e. in this case row and col. In other cases it may i,j,k or x,y,z, or M,N or whatever. Also note that we could have written line 24 as: c(idx[0], idx[1])=sum  or  c(row, col)=sum instead of the simpler c[idx]=sum Targeting a specific accelerator Imagine that we had more than one hardware accelerator on a system and we wanted to pick a specific one to execute this parallel loop on. So there would be some code like this anywhere before line 18: vector<accelerator> accs = MyFunctionThatChoosesSuitableAccelerators(); accelerator acc = accs[0]; …and then we would modify line 18 so we would be calling another overload of parallel_for_each that accepts an accelerator_view as the first argument, so it would become: concurrency::parallel_for_each(acc.default_view, c.grid, ...and the rest of your code remains the same… how simple is that? Comments about this post by Daniel Moth welcome at the original blog.

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  • Creating sitemap for Googlebot - how to mark dynamic content / dynamic subpages?

    - by ojek
    I have a website that is an Internet forum. This forum has many categories, and a single category page that contains a lot of subpages with listed threads. This Internet forum is brand new, and about a week ago I filled it with a few hundred thousand threads. I then looked at my Google Webmasters Tools page to see any changes in indexing, but the index went up from 300 to about 1200, so that means it did not index my added threads (although it added something). The following is what my sitemap.xml contains, which I uploaded to their website. Of course there is a lot more code, this is just a snippet for a single category. In my real sitemap file I have all the categories listed as below: <url> <loc>http://mysite.com/Forums/Physics</loc> <changefreq>hourly</changefreq> </url> Now, I would expect Googlebot to go into mysite.com/Forums/Physics, and crawl through all the subpages with thread links, and then crawl inside of each thread and index its content. How can I achieve this? Also if this is unclear, I will add a real link to my website.

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  • The Importance of Fully Specifying a Problem

    - by Alan
    I had a customer call this week where we were provided a forced crashdump and asked to determine why the system was hung. Normally when you are looking at a hung system, you will find a lot of threads blocked on various locks, and most likely very little actually running on the system (unless it's threads spinning on busy wait type locks). This vmcore showed none of that. In fact we were seeing hundreds of threads actively on cpu in the second before the dump was forced. This prompted the question back to the customer: What exactly were you seeing that made you believe that the system was hung? It took a few days to get a response, but the response that I got back was that they were not able to ssh into the system and when they tried to login to the console, they got the login prompt, but after typing "root" and hitting return, the console was no longer responsive. This description puts a whole new light on the "hang". You immediately start thinking "name services". Looking at the crashdump, yes the sshds are all in door calls to nscd, and nscd is idle waiting on responses from the network. Looking at the connections I see a lot of connections to the secure ldap port in CLOSE_WAIT, but more interestingly I am seeing a few connections over the non-secure ldap port to a different LDAP server just sitting open. My feeling at this point is that we have an either non-responding LDAP server, or one that is responding slowly, the resolution being to investigate that server. Moral When you log a service ticket for a "system hang", it's great to get the forced crashdump first up, but it's even better to get a description of what you observed to make to believe that the system was hung.

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  • Uses of persistent data structures in non-functional languages

    - by Ray Toal
    Languages that are purely functional or near-purely functional benefit from persistent data structures because they are immutable and fit well with the stateless style of functional programming. But from time to time we see libraries of persistent data structures for (state-based, OOP) languages like Java. A claim often heard in favor of persistent data structures is that because they are immutable, they are thread-safe. However, the reason that persistent data structures are thread-safe is that if one thread were to "add" an element to a persistent collection, the operation returns a new collection like the original but with the element added. Other threads therefore see the original collection. The two collections share a lot of internal state, of course -- that's why these persistent structures are efficient. But since different threads see different states of data, it would seem that persistent data structures are not in themselves sufficient to handle scenarios where one thread makes a change that is visible to other threads. For this, it seems we must use devices such as atoms, references, software transactional memory, or even classic locks and synchronization mechanisms. Why then, is the immutability of PDSs touted as something beneficial for "thread safety"? Are there any real examples where PDSs help in synchronization, or solving concurrency problems? Or are PDSs simply a way to provide a stateless interface to an object in support of a functional programming style?

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  • Concurrency pattern of logger in multithreaded application

    - by Dipan Mehta
    The context: We are working on a multi-threaded (Linux-C) application that follows a pipeline model. Each module has a private thread and encapsulated objects which do processing of data; and each stage has a standard form of exchanging data with next unit. The application is free from memory leak and is threadsafe using locks at the point where they exchange data. Total number of threads is about 15- and each thread can have from 1 to 4 objects. Making about 25 - 30 odd objects which all have some critical logging to do. Most discussion I have seen about different levels as in Log4J and it's other translations. The real big questions is about how the overall logging should really happen? One approach is all local logging does fprintf to stderr. The stderr is redirected to some file. This approach is very bad when logs become too big. If all object instantiate their individual loggers - (about 30-40 of them) there will be too many files. And unlike above, one won't have the idea of true order of events. Timestamping is one possibility - but it is still a mess to collate. If there is a single global logger (singleton) pattern - it indirectly blocks so many threads while one is busy putting up logs. This is unacceptable when processing of the threads are heavy. So what should be the ideal way to structure the logging objects? What are some of the best practices in actual large scale applications? I would also love to learn from some of the real designs of large scale applications to get inspirations from!

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  • Creating sitemap for google bot - how to mark dynamic content / dynamic subpages?

    - by ojek
    I have a website that is internet forum. This forum has many categories, and single category page contains alot of subpages with listed threads. This internet forum is brand new, and about a week ago I filled it with few hundred thousands threads. I then looked at google webmasters page to see any changes in indexing, but the index went up from 300 to about 1200, so that means it did not index my added threads (although it added something). This is what my sitemap.xml contains which I uploaded on their website (of course there is a lot more of the code, this is just a snipped for a single category, in my real sitemap file I have all the categories listed as below): <url> <loc>http://mysite.com/Forums/Physics</loc> <changefreq>hourly</changefreq> </url> Now, I would expect google bot to go into http://mysite.com/Forums/Physics, and move through all the subpages with thread links, and then get inside of each thread and index it's content. How can I do this? Also if this will be unclear, I will add a real link to my website.

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  • Marking Discussions as Answered

    As a contributor to a number of projects on CodePlex I really like the fact that the discussions feature exists but also I need ways to help me sort the discussions threads so I can make sure no-one is getting forgotten about. Seems like a lot of you agreed as the feature request Provide feature to allow Coordinators to mark Discussions threads as 'Answered' is our number 2 voted feature right now with 178 votes.  Today we rolled out the first iteration of “answer” support to discussions. In this first iteration we wanted to keep it simple and lightweight. The original poster of the thread along with project owners, developers or editors can mark any post to the thread as an answer. You can have any number of answers marked in a thread and it’s very quick to mark or unmark a post as an answer.  We deliberately keep the answers in the originally posted order so that you can see them in context with the discussion thread. When viewing discussions the default view is still to see everything, but you can easily filter by “Unanswered”.  You can even save that as a bookmark so as someone interested in the project can quickly jump to the unanswered discussion threads to go help out on. As I mention, we kept this first pass of the answering feature as simple and as lightweight as possible so that we can get some feedback on it. Head on over to the issue tracking this feature if you have any thoughts once you have used it for a bit or feel free to respond in the comments. I already have a couple of things I think we want to do such as a refresh of the look and feel of discussions in general along, make it easier to navigate to posts that are marked an answered and surface posts that you do that were marked as answered in your profile page - but if you have ideas then please let us know.

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  • How can I tell if I am overusing multi-threading?

    - by exhuma
    NOTE: This is a complete re-write of the question. The text before was way too lengthy and did not get to the point! If you're interested in the original question, you can look it up in the edit history. I currently feel like I am over-using multi-threading. I have 3 types of data, A, B and C. Each A can be converted to multiple Bs and each B can be converted to multiple Cs. I am only interested in treating Cs. I could write this fairly easily with a couple of conversion functions. But I caught myself implementing it with threads, three queues (queue_a, queue_b and queue_c). There are two threads doing the different conversions, and one worker: ConverterA reads from queue_a and writes to queue_b ConverterB reads from queue_b and writes to queue_c Worker handles each element from queue_c The conversions are fairly mundane, and I don't know if this model is too convoluted. But it seems extremely robust to me. Each "converter" can start working even before data has arrived on the queues, and at any time in the code I can just "submit" new As or Bs and it will trigger the conversion pipeline which in turn will trigger a job by the worker thread. Even the resulting code looks simpler. But I still am unsure if I am abusing threads for something simple.

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  • multi-thread in mmorpg server

    - by jean
    For MMORPG, there is a tick function to update every object's state in a map. The function was triggered by a timer in fixed interval. So each map's update can be dispatch to different thread. At other side, server handle player incoming package have its own threads also: I/O threads. Generally, the handler of the corresponding incoming package run in I/O threads. So there is a problem: thread synchronization. I have consider two methods: Synchronize with mutex. I/O thread lock a mutex before execute handler function and map thread lock same mutex before it execute map's update. Execute all handler functions in map's thread, I/O thread only queue the incoming handler and let map thread to pop the queue then call handler function. These two have a disadvantage: delay. For method 1, if the map's tick function is running, then all clients' request need to waiting the lock release. For method 2, if map's tick function is running, all clients' request need to waiting for next tick to be handle. Of course, there is another method: add lock to functions that use data which will be accessed both in I/O thread & map thread. But this is hard to maintain and easy to goes incorrect. It needs carefully check all variables whether or not accessed by both two kinds thread. My problem is: is there better way to do this? Notice that I said map is logic concept means no interactions can happen between two map except transport. I/O thread means thread in 3rd part network lib which used to handle client request.

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  • Context switches much slower in new linux kernels

    - by Michael Goldshteyn
    We are looking to upgrade the OS on our servers from Ubuntu 10.04 LTS to Ubuntu 12.04 LTS. Unfortunately, it seems that the latency to run a thread that has become runnable has significantly increased from the 2.6 kernel to the 3.2 kernel. In fact the latency numbers we are getting are hard to believe. Let me be more specific about the test. We have a program that has two threads. The first thread gets the current time (in ticks using RDTSC) and then signals a condition variable once a second. The second thread waits on the condition variable and wakes up when it is signaled. It then gets the current time (in ticks using RDTSC). The difference between the time in the second thread and the time in the first thread is computed and displayed on the console. After this the second thread waits on the condition variable once more. So, we get a thread to thread signaling latency measurement once a second as a result. In linux 2.6.32, this latency is somewhere on the order of 2.8-3.5 us, which is reasonable. In linux 3.2.0, this latency is somewhere on the order of 40-100 us. I have excluded any differences in hardware between the two host hosts. They run on identical hardware (dual socket X5687 {Westmere-EP} processors running at 3.6 GHz with hyperthreading, speedstep and all C states turned off). We are changing the affinity to run both threads on physical cores of the same socket (i.e., the first thread is run on Core 0 and the second thread is run on Core 1), so there is no bouncing of threads on cores or bouncing/communication between sockets. The only difference between the two hosts is that one is running Ubuntu 10.04 LTS with kernel 2.6.32-28 (the fast context switch box) and the other is running the latest Ubuntu 12.04 LTS with kernel 3.2.0-23 (the slow context switch box). Have there been any changes in the kernel that could account for this ridiculous slow down in how long it takes for a thread to be scheduled to run?

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  • Understanding G1 GC Logs

    - by poonam
    The purpose of this post is to explain the meaning of GC logs generated with some tracing and diagnostic options for G1 GC. We will take a look at the output generated with PrintGCDetails which is a product flag and provides the most detailed level of information. Along with that, we will also look at the output of two diagnostic flags that get enabled with -XX:+UnlockDiagnosticVMOptions option - G1PrintRegionLivenessInfo that prints the occupancy and the amount of space used by live objects in each region at the end of the marking cycle and G1PrintHeapRegions that provides detailed information on the heap regions being allocated and reclaimed. We will be looking at the logs generated with JDK 1.7.0_04 using these options. Option -XX:+PrintGCDetails Here's a sample log of G1 collection generated with PrintGCDetails. 0.522: [GC pause (young), 0.15877971 secs] [Parallel Time: 157.1 ms] [GC Worker Start (ms): 522.1 522.2 522.2 522.2 Avg: 522.2, Min: 522.1, Max: 522.2, Diff: 0.1] [Ext Root Scanning (ms): 1.6 1.5 1.6 1.9 Avg: 1.7, Min: 1.5, Max: 1.9, Diff: 0.4] [Update RS (ms): 38.7 38.8 50.6 37.3 Avg: 41.3, Min: 37.3, Max: 50.6, Diff: 13.3] [Processed Buffers : 2 2 3 2 Sum: 9, Avg: 2, Min: 2, Max: 3, Diff: 1] [Scan RS (ms): 9.9 9.7 0.0 9.7 Avg: 7.3, Min: 0.0, Max: 9.9, Diff: 9.9] [Object Copy (ms): 106.7 106.8 104.6 107.9 Avg: 106.5, Min: 104.6, Max: 107.9, Diff: 3.3] [Termination (ms): 0.0 0.0 0.0 0.0 Avg: 0.0, Min: 0.0, Max: 0.0, Diff: 0.0] [Termination Attempts : 1 4 4 6 Sum: 15, Avg: 3, Min: 1, Max: 6, Diff: 5] [GC Worker End (ms): 679.1 679.1 679.1 679.1 Avg: 679.1, Min: 679.1, Max: 679.1, Diff: 0.1] [GC Worker (ms): 156.9 157.0 156.9 156.9 Avg: 156.9, Min: 156.9, Max: 157.0, Diff: 0.1] [GC Worker Other (ms): 0.3 0.3 0.3 0.3 Avg: 0.3, Min: 0.3, Max: 0.3, Diff: 0.0] [Clear CT: 0.1 ms] [Other: 1.5 ms] [Choose CSet: 0.0 ms] [Ref Proc: 0.3 ms] [Ref Enq: 0.0 ms] [Free CSet: 0.3 ms] [Eden: 12M(12M)->0B(10M) Survivors: 0B->2048K Heap: 13M(64M)->9739K(64M)] [Times: user=0.59 sys=0.02, real=0.16 secs] This is the typical log of an Evacuation Pause (G1 collection) in which live objects are copied from one set of regions (young OR young+old) to another set. It is a stop-the-world activity and all the application threads are stopped at a safepoint during this time. This pause is made up of several sub-tasks indicated by the indentation in the log entries. Here's is the top most line that gets printed for the Evacuation Pause. 0.522: [GC pause (young), 0.15877971 secs] This is the highest level information telling us that it is an Evacuation Pause that started at 0.522 secs from the start of the process, in which all the regions being evacuated are Young i.e. Eden and Survivor regions. This collection took 0.15877971 secs to finish. Evacuation Pauses can be mixed as well. In which case the set of regions selected include all of the young regions as well as some old regions. 1.730: [GC pause (mixed), 0.32714353 secs] Let's take a look at all the sub-tasks performed in this Evacuation Pause. [Parallel Time: 157.1 ms] Parallel Time is the total elapsed time spent by all the parallel GC worker threads. The following lines correspond to the parallel tasks performed by these worker threads in this total parallel time, which in this case is 157.1 ms. [GC Worker Start (ms): 522.1 522.2 522.2 522.2Avg: 522.2, Min: 522.1, Max: 522.2, Diff: 0.1] The first line tells us the start time of each of the worker thread in milliseconds. The start times are ordered with respect to the worker thread ids – thread 0 started at 522.1ms and thread 1 started at 522.2ms from the start of the process. The second line tells the Avg, Min, Max and Diff of the start times of all of the worker threads. [Ext Root Scanning (ms): 1.6 1.5 1.6 1.9 Avg: 1.7, Min: 1.5, Max: 1.9, Diff: 0.4] This gives us the time spent by each worker thread scanning the roots (globals, registers, thread stacks and VM data structures). Here, thread 0 took 1.6ms to perform the root scanning task and thread 1 took 1.5 ms. The second line clearly shows the Avg, Min, Max and Diff of the times spent by all the worker threads. [Update RS (ms): 38.7 38.8 50.6 37.3 Avg: 41.3, Min: 37.3, Max: 50.6, Diff: 13.3] Update RS gives us the time each thread spent in updating the Remembered Sets. Remembered Sets are the data structures that keep track of the references that point into a heap region. Mutator threads keep changing the object graph and thus the references that point into a particular region. We keep track of these changes in buffers called Update Buffers. The Update RS sub-task processes the update buffers that were not able to be processed concurrently, and updates the corresponding remembered sets of all regions. [Processed Buffers : 2 2 3 2Sum: 9, Avg: 2, Min: 2, Max: 3, Diff: 1] This tells us the number of Update Buffers (mentioned above) processed by each worker thread. [Scan RS (ms): 9.9 9.7 0.0 9.7 Avg: 7.3, Min: 0.0, Max: 9.9, Diff: 9.9] These are the times each worker thread had spent in scanning the Remembered Sets. Remembered Set of a region contains cards that correspond to the references pointing into that region. This phase scans those cards looking for the references pointing into all the regions of the collection set. [Object Copy (ms): 106.7 106.8 104.6 107.9 Avg: 106.5, Min: 104.6, Max: 107.9, Diff: 3.3] These are the times spent by each worker thread copying live objects from the regions in the Collection Set to the other regions. [Termination (ms): 0.0 0.0 0.0 0.0 Avg: 0.0, Min: 0.0, Max: 0.0, Diff: 0.0] Termination time is the time spent by the worker thread offering to terminate. But before terminating, it checks the work queues of other threads and if there are still object references in other work queues, it tries to steal object references, and if it succeeds in stealing a reference, it processes that and offers to terminate again. [Termination Attempts : 1 4 4 6 Sum: 15, Avg: 3, Min: 1, Max: 6, Diff: 5] This gives the number of times each thread has offered to terminate. [GC Worker End (ms): 679.1 679.1 679.1 679.1 Avg: 679.1, Min: 679.1, Max: 679.1, Diff: 0.1] These are the times in milliseconds at which each worker thread stopped. [GC Worker (ms): 156.9 157.0 156.9 156.9 Avg: 156.9, Min: 156.9, Max: 157.0, Diff: 0.1] These are the total lifetimes of each worker thread. [GC Worker Other (ms): 0.3 0.3 0.3 0.3Avg: 0.3, Min: 0.3, Max: 0.3, Diff: 0.0] These are the times that each worker thread spent in performing some other tasks that we have not accounted above for the total Parallel Time. [Clear CT: 0.1 ms] This is the time spent in clearing the Card Table. This task is performed in serial mode. [Other: 1.5 ms] Time spent in the some other tasks listed below. The following sub-tasks (which individually may be parallelized) are performed serially. [Choose CSet: 0.0 ms] Time spent in selecting the regions for the Collection Set. [Ref Proc: 0.3 ms] Total time spent in processing Reference objects. [Ref Enq: 0.0 ms] Time spent in enqueuing references to the ReferenceQueues. [Free CSet: 0.3 ms] Time spent in freeing the collection set data structure. [Eden: 12M(12M)->0B(13M) Survivors: 0B->2048K Heap: 14M(64M)->9739K(64M)] This line gives the details on the heap size changes with the Evacuation Pause. This shows that Eden had the occupancy of 12M and its capacity was also 12M before the collection. After the collection, its occupancy got reduced to 0 since everything is evacuated/promoted from Eden during a collection, and its target size grew to 13M. The new Eden capacity of 13M is not reserved at this point. This value is the target size of the Eden. Regions are added to Eden as the demand is made and when the added regions reach to the target size, we start the next collection. Similarly, Survivors had the occupancy of 0 bytes and it grew to 2048K after the collection. The total heap occupancy and capacity was 14M and 64M receptively before the collection and it became 9739K and 64M after the collection. Apart from the evacuation pauses, G1 also performs concurrent-marking to build the live data information of regions. 1.416: [GC pause (young) (initial-mark), 0.62417980 secs] ….... 2.042: [GC concurrent-root-region-scan-start] 2.067: [GC concurrent-root-region-scan-end, 0.0251507] 2.068: [GC concurrent-mark-start] 3.198: [GC concurrent-mark-reset-for-overflow] 4.053: [GC concurrent-mark-end, 1.9849672 sec] 4.055: [GC remark 4.055: [GC ref-proc, 0.0000254 secs], 0.0030184 secs] [Times: user=0.00 sys=0.00, real=0.00 secs] 4.088: [GC cleanup 117M->106M(138M), 0.0015198 secs] [Times: user=0.00 sys=0.00, real=0.00 secs] 4.090: [GC concurrent-cleanup-start] 4.091: [GC concurrent-cleanup-end, 0.0002721] The first phase of a marking cycle is Initial Marking where all the objects directly reachable from the roots are marked and this phase is piggy-backed on a fully young Evacuation Pause. 2.042: [GC concurrent-root-region-scan-start] This marks the start of a concurrent phase that scans the set of root-regions which are directly reachable from the survivors of the initial marking phase. 2.067: [GC concurrent-root-region-scan-end, 0.0251507] End of the concurrent root region scan phase and it lasted for 0.0251507 seconds. 2.068: [GC concurrent-mark-start] Start of the concurrent marking at 2.068 secs from the start of the process. 3.198: [GC concurrent-mark-reset-for-overflow] This indicates that the global marking stack had became full and there was an overflow of the stack. Concurrent marking detected this overflow and had to reset the data structures to start the marking again. 4.053: [GC concurrent-mark-end, 1.9849672 sec] End of the concurrent marking phase and it lasted for 1.9849672 seconds. 4.055: [GC remark 4.055: [GC ref-proc, 0.0000254 secs], 0.0030184 secs] This corresponds to the remark phase which is a stop-the-world phase. It completes the left over marking work (SATB buffers processing) from the previous phase. In this case, this phase took 0.0030184 secs and out of which 0.0000254 secs were spent on Reference processing. 4.088: [GC cleanup 117M->106M(138M), 0.0015198 secs] Cleanup phase which is again a stop-the-world phase. It goes through the marking information of all the regions, computes the live data information of each region, resets the marking data structures and sorts the regions according to their gc-efficiency. In this example, the total heap size is 138M and after the live data counting it was found that the total live data size dropped down from 117M to 106M. 4.090: [GC concurrent-cleanup-start] This concurrent cleanup phase frees up the regions that were found to be empty (didn't contain any live data) during the previous stop-the-world phase. 4.091: [GC concurrent-cleanup-end, 0.0002721] Concurrent cleanup phase took 0.0002721 secs to free up the empty regions. Option -XX:G1PrintRegionLivenessInfo Now, let's look at the output generated with the flag G1PrintRegionLivenessInfo. This is a diagnostic option and gets enabled with -XX:+UnlockDiagnosticVMOptions. G1PrintRegionLivenessInfo prints the live data information of each region during the Cleanup phase of the concurrent-marking cycle. 26.896: [GC cleanup ### PHASE Post-Marking @ 26.896### HEAP committed: 0x02e00000-0x0fe00000 reserved: 0x02e00000-0x12e00000 region-size: 1048576 Cleanup phase of the concurrent-marking cycle started at 26.896 secs from the start of the process and this live data information is being printed after the marking phase. Committed G1 heap ranges from 0x02e00000 to 0x0fe00000 and the total G1 heap reserved by JVM is from 0x02e00000 to 0x12e00000. Each region in the G1 heap is of size 1048576 bytes. ### type address-range used prev-live next-live gc-eff### (bytes) (bytes) (bytes) (bytes/ms) This is the header of the output that tells us about the type of the region, address-range of the region, used space in the region, live bytes in the region with respect to the previous marking cycle, live bytes in the region with respect to the current marking cycle and the GC efficiency of that region. ### FREE 0x02e00000-0x02f00000 0 0 0 0.0 This is a Free region. ### OLD 0x02f00000-0x03000000 1048576 1038592 1038592 0.0 Old region with address-range from 0x02f00000 to 0x03000000. Total used space in the region is 1048576 bytes, live bytes as per the previous marking cycle are 1038592 and live bytes with respect to the current marking cycle are also 1038592. The GC efficiency has been computed as 0. ### EDEN 0x03400000-0x03500000 20992 20992 20992 0.0 This is an Eden region. ### HUMS 0x0ae00000-0x0af00000 1048576 1048576 1048576 0.0### HUMC 0x0af00000-0x0b000000 1048576 1048576 1048576 0.0### HUMC 0x0b000000-0x0b100000 1048576 1048576 1048576 0.0### HUMC 0x0b100000-0x0b200000 1048576 1048576 1048576 0.0### HUMC 0x0b200000-0x0b300000 1048576 1048576 1048576 0.0### HUMC 0x0b300000-0x0b400000 1048576 1048576 1048576 0.0### HUMC 0x0b400000-0x0b500000 1001480 1001480 1001480 0.0 These are the continuous set of regions called Humongous regions for storing a large object. HUMS (Humongous starts) marks the start of the set of humongous regions and HUMC (Humongous continues) tags the subsequent regions of the humongous regions set. ### SURV 0x09300000-0x09400000 16384 16384 16384 0.0 This is a Survivor region. ### SUMMARY capacity: 208.00 MB used: 150.16 MB / 72.19 % prev-live: 149.78 MB / 72.01 % next-live: 142.82 MB / 68.66 % At the end, a summary is printed listing the capacity, the used space and the change in the liveness after the completion of concurrent marking. In this case, G1 heap capacity is 208MB, total used space is 150.16MB which is 72.19% of the total heap size, live data in the previous marking was 149.78MB which was 72.01% of the total heap size and the live data as per the current marking is 142.82MB which is 68.66% of the total heap size. Option -XX:+G1PrintHeapRegions G1PrintHeapRegions option logs the regions related events when regions are committed, allocated into or are reclaimed. COMMIT/UNCOMMIT events G1HR COMMIT [0x6e900000,0x6ea00000]G1HR COMMIT [0x6ea00000,0x6eb00000] Here, the heap is being initialized or expanded and the region (with bottom: 0x6eb00000 and end: 0x6ec00000) is being freshly committed. COMMIT events are always generated in order i.e. the next COMMIT event will always be for the uncommitted region with the lowest address. G1HR UNCOMMIT [0x72700000,0x72800000]G1HR UNCOMMIT [0x72600000,0x72700000] Opposite to COMMIT. The heap got shrunk at the end of a Full GC and the regions are being uncommitted. Like COMMIT, UNCOMMIT events are also generated in order i.e. the next UNCOMMIT event will always be for the committed region with the highest address. GC Cycle events G1HR #StartGC 7G1HR CSET 0x6e900000G1HR REUSE 0x70500000G1HR ALLOC(Old) 0x6f800000G1HR RETIRE 0x6f800000 0x6f821b20G1HR #EndGC 7 This shows start and end of an Evacuation pause. This event is followed by a GC counter tracking both evacuation pauses and Full GCs. Here, this is the 7th GC since the start of the process. G1HR #StartFullGC 17G1HR UNCOMMIT [0x6ed00000,0x6ee00000]G1HR POST-COMPACTION(Old) 0x6e800000 0x6e854f58G1HR #EndFullGC 17 Shows start and end of a Full GC. This event is also followed by the same GC counter as above. This is the 17th GC since the start of the process. ALLOC events G1HR ALLOC(Eden) 0x6e800000 The region with bottom 0x6e800000 just started being used for allocation. In this case it is an Eden region and allocated into by a mutator thread. G1HR ALLOC(StartsH) 0x6ec00000 0x6ed00000G1HR ALLOC(ContinuesH) 0x6ed00000 0x6e000000 Regions being used for the allocation of Humongous object. The object spans over two regions. G1HR ALLOC(SingleH) 0x6f900000 0x6f9eb010 Single region being used for the allocation of Humongous object. G1HR COMMIT [0x6ee00000,0x6ef00000]G1HR COMMIT [0x6ef00000,0x6f000000]G1HR COMMIT [0x6f000000,0x6f100000]G1HR COMMIT [0x6f100000,0x6f200000]G1HR ALLOC(StartsH) 0x6ee00000 0x6ef00000G1HR ALLOC(ContinuesH) 0x6ef00000 0x6f000000G1HR ALLOC(ContinuesH) 0x6f000000 0x6f100000G1HR ALLOC(ContinuesH) 0x6f100000 0x6f102010 Here, Humongous object allocation request could not be satisfied by the free committed regions that existed in the heap, so the heap needed to be expanded. Thus new regions are committed and then allocated into for the Humongous object. G1HR ALLOC(Old) 0x6f800000 Old region started being used for allocation during GC. G1HR ALLOC(Survivor) 0x6fa00000 Region being used for copying old objects into during a GC. Note that Eden and Humongous ALLOC events are generated outside the GC boundaries and Old and Survivor ALLOC events are generated inside the GC boundaries. Other Events G1HR RETIRE 0x6e800000 0x6e87bd98 Retire and stop using the region having bottom 0x6e800000 and top 0x6e87bd98 for allocation. Note that most regions are full when they are retired and we omit those events to reduce the output volume. A region is retired when another region of the same type is allocated or we reach the start or end of a GC(depending on the region). So for Eden regions: For example: 1. ALLOC(Eden) Foo2. ALLOC(Eden) Bar3. StartGC At point 2, Foo has just been retired and it was full. At point 3, Bar was retired and it was full. If they were not full when they were retired, we will have a RETIRE event: 1. ALLOC(Eden) Foo2. RETIRE Foo top3. ALLOC(Eden) Bar4. StartGC G1HR CSET 0x6e900000 Region (bottom: 0x6e900000) is selected for the Collection Set. The region might have been selected for the collection set earlier (i.e. when it was allocated). However, we generate the CSET events for all regions in the CSet at the start of a GC to make sure there's no confusion about which regions are part of the CSet. G1HR POST-COMPACTION(Old) 0x6e800000 0x6e839858 POST-COMPACTION event is generated for each non-empty region in the heap after a full compaction. A full compaction moves objects around, so we don't know what the resulting shape of the heap is (which regions were written to, which were emptied, etc.). To deal with this, we generate a POST-COMPACTION event for each non-empty region with its type (old/humongous) and the heap boundaries. At this point we should only have Old and Humongous regions, as we have collapsed the young generation, so we should not have eden and survivors. POST-COMPACTION events are generated within the Full GC boundary. G1HR CLEANUP 0x6f400000G1HR CLEANUP 0x6f300000G1HR CLEANUP 0x6f200000 These regions were found empty after remark phase of Concurrent Marking and are reclaimed shortly afterwards. G1HR #StartGC 5G1HR CSET 0x6f400000G1HR CSET 0x6e900000G1HR REUSE 0x6f800000 At the end of a GC we retire the old region we are allocating into. Given that its not full, we will carry on allocating into it during the next GC. This is what REUSE means. In the above case 0x6f800000 should have been the last region with an ALLOC(Old) event during the previous GC and should have been retired before the end of the previous GC. G1HR ALLOC-FORCE(Eden) 0x6f800000 A specialization of ALLOC which indicates that we have reached the max desired number of the particular region type (in this case: Eden), but we decided to allocate one more. Currently it's only used for Eden regions when we extend the young generation because we cannot do a GC as the GC-Locker is active. G1HR EVAC-FAILURE 0x6f800000 During a GC, we have failed to evacuate an object from the given region as the heap is full and there is no space left to copy the object. This event is generated within GC boundaries and exactly once for each region from which we failed to evacuate objects. When Heap Regions are reclaimed ? It is also worth mentioning when the heap regions in the G1 heap are reclaimed. All regions that are in the CSet (the ones that appear in CSET events) are reclaimed at the end of a GC. The exception to that are regions with EVAC-FAILURE events. All regions with CLEANUP events are reclaimed. After a Full GC some regions get reclaimed (the ones from which we moved the objects out). But that is not shown explicitly, instead the non-empty regions that are left in the heap are printed out with the POST-COMPACTION events.

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  • Create a Lucky Desktop with our Saint Patrick’s Day Icons Three Pack

    - by Asian Angel
    Saint Patrick’s Day is almost here, so if you are wanting to add a nice touch of luck (and green) to your desktop then take a look at these three fun icon packs we have for you. Note: Available in .ico and .png format. Irish Icons [Icon Stick] Note: Available for Windows and Mac. St. Patty’s Kidcons [Iconfactory] Note: Available for Windows and Mac. St. Patrick’s Day Icons [Bry-Back Manor] These icons will make a nice addition to our Saint Patrick’s Day Wallpaper Five Pack, so browse on over and go for the green! Internet Explorer 9 Released: Here’s What You Need To KnowHTG Explains: How Does Email Work?How To Make a Youtube Video Into an Animated GIF

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  • xgamma -display parameter for dual monitor setup

    - by Shiplu
    I want to change gamma for my first monitor. Every time I invoke xgamma with different -display parameters it somehow points to my second monitor. But I want to modify first one. I tried these commands. The parameters I have used for -display are :0, :0.0, :0.1, :1.0, :1.1, :1. Only the first 2 works. But it points to my second monitor. Not the first one. Here is a shell script to test it. shiplu@KubuntuD:~$ xgamma -display :0 -> Red 1.000, Green 1.000, Blue 1.000 shiplu@KubuntuD:~$ xgamma -display :0.0 -> Red 1.000, Green 1.000, Blue 1.000 shiplu@KubuntuD:~$ xgamma -display :0.1 xgamma: unable to open display ':0.1' shiplu@KubuntuD:~$ xgamma -display :1.0 xgamma: unable to open display ':1.0' shiplu@KubuntuD:~$ xgamma -display :1.1 xgamma: unable to open display ':1.1' shiplu@KubuntuD:~$ xgamma -display :1 xgamma: unable to open display ':1' How can I change the gamma for the first/primary monitor?

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  • Oracle TimesTen In-Memory Database Performance on SPARC T4-2

    - by Brian
    The Oracle TimesTen In-Memory Database is optimized to run on Oracle's SPARC T4 processor platforms running Oracle Solaris 11 providing unsurpassed scalability, performance, upgradability, protection of investment and return on investment. The following demonstrate the value of combining Oracle TimesTen In-Memory Database with SPARC T4 servers and Oracle Solaris 11: On a Mobile Call Processing test, the 2-socket SPARC T4-2 server outperforms: Oracle's SPARC Enterprise M4000 server (4 x 2.66 GHz SPARC64 VII+) by 34%. Oracle's SPARC T3-4 (4 x 1.65 GHz SPARC T3) by 2.7x, or 5.4x per processor. Utilizing the TimesTen Performance Throughput Benchmark (TPTBM), the SPARC T4-2 server protects investments with: 2.1x the overall performance of a 4-socket SPARC Enterprise M4000 server in read-only mode and 1.5x the performance in update-only testing. This is 4.2x more performance per processor than the SPARC64 VII+ 2.66 GHz based system. 10x more performance per processor than the SPARC T2+ 1.4 GHz server. 1.6x better performance per processor than the SPARC T3 1.65 GHz based server. In replication testing, the two socket SPARC T4-2 server is over 3x faster than the performance of a four socket SPARC Enterprise T5440 server in both asynchronous replication environment and the highly available 2-Safe replication. This testing emphasizes parallel replication between systems. Performance Landscape Mobile Call Processing Test Performance System Processor Sockets/Cores/Threads Tps SPARC T4-2 SPARC T4, 2.85 GHz 2 16 128 218,400 M4000 SPARC64 VII+, 2.66 GHz 4 16 32 162,900 SPARC T3-4 SPARC T3, 1.65 GHz 4 64 512 80,400 TimesTen Performance Throughput Benchmark (TPTBM) Read-Only System Processor Sockets/Cores/Threads Tps SPARC T3-4 SPARC T3, 1.65 GHz 4 64 512 7.9M SPARC T4-2 SPARC T4, 2.85 GHz 2 16 128 6.5M M4000 SPARC64 VII+, 2.66 GHz 4 16 32 3.1M T5440 SPARC T2+, 1.4 GHz 4 32 256 3.1M TimesTen Performance Throughput Benchmark (TPTBM) Update-Only System Processor Sockets/Cores/Threads Tps SPARC T4-2 SPARC T4, 2.85 GHz 2 16 128 547,800 M4000 SPARC64 VII+, 2.66 GHz 4 16 32 363,800 SPARC T3-4 SPARC T3, 1.65 GHz 4 64 512 240,500 TimesTen Replication Tests System Processor Sockets/Cores/Threads Asynchronous 2-Safe SPARC T4-2 SPARC T4, 2.85 GHz 2 16 128 38,024 13,701 SPARC T5440 SPARC T2+, 1.4 GHz 4 32 256 11,621 4,615 Configuration Summary Hardware Configurations: SPARC T4-2 server 2 x SPARC T4 processors, 2.85 GHz 256 GB memory 1 x 8 Gbs FC Qlogic HBA 1 x 6 Gbs SAS HBA 4 x 300 GB internal disks Sun Storage F5100 Flash Array (40 x 24 GB flash modules) 1 x Sun Fire X4275 server configured as COMSTAR head SPARC T3-4 server 4 x SPARC T3 processors, 1.6 GHz 512 GB memory 1 x 8 Gbs FC Qlogic HBA 8 x 146 GB internal disks 1 x Sun Fire X4275 server configured as COMSTAR head SPARC Enterprise M4000 server 4 x SPARC64 VII+ processors, 2.66 GHz 128 GB memory 1 x 8 Gbs FC Qlogic HBA 1 x 6 Gbs SAS HBA 2 x 146 GB internal disks Sun Storage F5100 Flash Array (40 x 24 GB flash modules) 1 x Sun Fire X4275 server configured as COMSTAR head Software Configuration: Oracle Solaris 11 11/11 Oracle TimesTen 11.2.2.4 Benchmark Descriptions TimesTen Performance Throughput BenchMark (TPTBM) is shipped with TimesTen and measures the total throughput of the system. The workload can test read-only, update-only, delete and insert operations as required. Mobile Call Processing is a customer-based workload for processing calls made by mobile phone subscribers. The workload has a mixture of read-only, update, and insert-only transactions. The peak throughput performance is measured from multiple concurrent processes executing the transactions until a peak performance is reached via saturation of the available resources. Parallel Replication tests using both asynchronous and 2-Safe replication methods. For asynchronous replication, transactions are processed in batches to maximize the throughput capabilities of the replication server and network. In 2-Safe replication, also known as no data-loss or high availability, transactions are replicated between servers immediately emphasizing low latency. For both environments, performance is measured in the number of parallel replication servers and the maximum transactions-per-second for all concurrent processes. See Also SPARC T4-2 Server oracle.com OTN Oracle TimesTen In-Memory Database oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 1 October 2012.

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  • GDL Presents: Van Gogh Meets Alan Turing

    GDL Presents: Van Gogh Meets Alan Turing How can art and daily life be joined together? Host Ido Green chats with creators Uri Shaked & Tom Teman about tackling this question with their "Music Room" -- a case study in the power of Android -- and with Emmanuel Witzthum on his project "Dissolving Realities," which aims to connect the virtual environment of the Internet using Google Street View. Host: Ido Green, Developer Advocate Guests: Uri Shaked and Emmanuel Witzthum From: GoogleDevelopers Views: 0 0 ratings Time: 00:00 More in Science & Technology

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  • Enum driving a Visual State change via the ViewModel

    - by Chris Skardon
    Exciting title eh? So, here’s the problem, I want to use my ViewModel to drive my Visual State, I’ve used the ‘DataStateBehavior’ before, but the trouble with it is that it only works for bool values, and the minute you jump to more than 2 Visual States, you’re kind of screwed. A quick search has shown up a couple of points of interest, first, the DataStateSwitchBehavior, which is part of the Expression Samples (on Codeplex), and also available via Pete Blois’ blog. The second interest is to use a DataTrigger with GoToStateAction (from the Silverlight forums). So, onwards… first let’s create a basic switch Visual State, so, a DataObj with one property: IsAce… public class DataObj : NotifyPropertyChanger { private bool _isAce; public bool IsAce { get { return _isAce; } set { _isAce = value; RaisePropertyChanged("IsAce"); } } } The ‘NotifyPropertyChanger’ is literally a base class with RaisePropertyChanged, implementing INotifyPropertyChanged. OK, so we then create a ViewModel: public class MainPageViewModel : NotifyPropertyChanger { private DataObj _dataObj; public MainPageViewModel() { DataObj = new DataObj {IsAce = true}; ChangeAcenessCommand = new RelayCommand(() => DataObj.IsAce = !DataObj.IsAce); } public ICommand ChangeAcenessCommand { get; private set; } public DataObj DataObj { get { return _dataObj; } set { _dataObj = value; RaisePropertyChanged("DataObj"); } } } Aaaand finally – hook it all up to the XAML, which is a very simple UI: A Rectangle, a TextBlock and a Button. The Button is hooked up to ChangeAcenessCommand, the TextBlock is bound to the ‘DataObj.IsAce’ property and the Rectangle has 2 visual states: IsAce and NotAce. To make the Rectangle change it’s visual state I’ve used a DataStateBehavior inside the Layout Root Grid: <i:Interaction.Behaviors> <ei:DataStateBehavior Binding="{Binding DataObj.IsAce}" Value="true" TrueState="IsAce" FalseState="NotAce"/> </i:Interaction.Behaviors> So now we have the button changing the ‘IsAce’ property and giving us the other visual state: Great! So – the next stage is to get that to work inside a DataTemplate… Which (thankfully) is easy money. All we do is add a ListBox to the View and an ObservableCollection to the ViewModel. Well – ok, a little bit more than that. Once we’ve got the ListBox with it’s ItemsSource property set, it’s time to add the DataTemplate itself. Again, this isn’t exactly taxing, and is purely going to be a Grid with a Textblock and a Rectangle (again, I’m nothing if not consistent). Though, to be a little jazzy I’ve swapped the rectangle to the other side (living the dream). So, all that’s left is to add some States to the template.. (Yes – you can do that), these can be the same names as the others, or indeed, something else, I have chosen to stick with the same names and take the extra confusion hit right on the nose. Once again, I add the DataStateBehavior to the root Grid element: <i:Interaction.Behaviors> <ei:DataStateBehavior Binding="{Binding IsAce}" Value="true" TrueState="IsAce" FalseState="NotAce"/> </i:Interaction.Behaviors> The key difference here is the ‘Binding’ attribute, where I’m now binding to the IsAce property directly, and boom! It’s all gravy!   So far, so good. We can use boolean values to change the visual states, and (crucially) it works in a DataTemplate, bingo! Now. Onwards to the Enum part of this (finally!). Obviously we can’t use the DataStateBehavior, it' only gives us true/false options. So, let’s give the GoToStateAction a go. Now, I warn you, things get a bit complex from here, instead of a bool with 2 values, I’m gonna max it out and bring in an Enum with 3 (count ‘em) 3 values: Red, Amber and Green (those of you with exceptionally sharp minds will be reminded of traffic lights). We’re gonna have a rectangle which also has 3 visual states – cunningly called ‘Red’, ‘Amber’ and ‘Green’. A new class called DataObj2: public class DataObj2 : NotifyPropertyChanger { private Status _statusValue; public DataObj2(Status status) { StatusValue = status; } public Status StatusValue { get { return _statusValue; } set { _statusValue = value; RaisePropertyChanged("StatusValue"); } } } Where ‘Status’ is my enum. Good times are here! Ok, so let’s get to the beefy stuff. So, we’ll start off in the same manner as the last time, we will have a single DataObj2 instance available to the Page and bind to that. Let’s add some Triggers (these are in the LayoutRoot again). <i:Interaction.Triggers> <ei:DataTrigger Binding="{Binding DataObject2.StatusValue}" Value="Amber"> <ei:GoToStateAction StateName="Amber" UseTransitions="False" /> </ei:DataTrigger> <ei:DataTrigger Binding="{Binding DataObject2.StatusValue}" Value="Green"> <ei:GoToStateAction StateName="Green" UseTransitions="False" /> </ei:DataTrigger> <ei:DataTrigger Binding="{Binding DataObject2.StatusValue}" Value="Red"> <ei:GoToStateAction StateName="Red" UseTransitions="False" /> </ei:DataTrigger> </i:Interaction.Triggers> So what we’re saying here is that when the DataObject2.StatusValue is equal to ‘Red’ then we’ll go to the ‘Red’ state. Same deal for Green and Amber (but you knew that already). Hook it all up and start teh project. Hmm. Just grey. Not what I wanted. Ok, let’s add a ‘ChangeStatusCommand’, hook that up to a button and give it a whirl: Right, so the DataTrigger isn’t picking up the data on load. On the plus side, changing the status is making the visual states change. So. We’ll cross the ‘Grey’ hurdle in a bit, what about doing the same in the DataTemplate? <Codey Codey/> Grey again, but if we press the button: (I should mention, pressing the button sets the StatusValue property on the DataObj2 being represented to the next colour). Right. Let’s look at this ‘Grey’ issue. First ‘fix’ (and I use the term ‘fix’ in a very loose way): The Dispatcher Fix This involves using the Dispatcher on the View to call something like ‘RefreshProperties’ on the ViewModel, which will in turn raise all the appropriate ‘PropertyChanged’ events on the data objects being represented. So, here goes, into turdcode-ville – population – me: First, add the ‘RefreshProperties’ method to the DataObj2: internal void RefreshProperties() { RaisePropertyChanged("StatusValue"); } (shudder) Now, add it to the hosting ViewModel: public void RefreshProperties() { DataObject2.RefreshProperties(); if (DataObjects != null && DataObjects.Count > 0) { foreach (DataObj2 dataObject in DataObjects) dataObject.RefreshProperties(); } } (double shudder) and now for the cream on the cake, adding the following line to the code behind of the View: Dispatcher.BeginInvoke(() => ((MoreVisualStatesViewModel)DataContext).RefreshProperties()); So, what does this *ahem* code give us: Awesome, it makes the single bound data object show the colour, but frankly ignores the DataTemplate items. This (by the way) is the same output you get from: Dispatcher.BeginInvoke(() => ((MoreVisualStatesViewModel)DataContext).ChangeStatusCommand.Execute(null)); So… Where does that leave me? What about adding a button to the Page to refresh the properties – maybe it’s a timer thing? Yes, that works. Right, what about using the Loaded event then eh? Loaded += (s, e) => ((MoreVisualStatesViewModel) DataContext).RefreshProperties(); Ahhh No. What about converting the DataTemplate into a UserControl? Anything is worth a shot.. Though – I still suspect I’m going to have to ‘RefreshProperties’ if I want the rectangles to update. Still. No. This DataTemplate DataTrigger binding is becoming a bit of a pain… I can’t add a ‘refresh’ button to the actual code base, it’s not exactly user friendly. I’m going to end this one now, and put some investigating into the use of the DataStateSwitchBehavior (all the ones I’ve found, well, all 2 of them are working in SL3, but not 4…)

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  • Jersey non blocking client

    - by Pavel Bucek
    Although Jersey already have support for making asynchronous requests, it is implemented by standard blocking way - every asynchronous request is handled by one thread and that thread is released only after request is completely processed. That is OK for lots of cases, but imagine how that will work when you need to do lots of parallel requests. Of course you can limit (and its really wise thing to do, you do want control your resources) number of threads used for asynchronous requests, but you'll get another maybe not pleasant consequence - obviously processing time will incerase. There are few projects which are trying to deal with that problem, commonly named as async http clients. I didn't want to "re-implement a wheel" and I decided I'll use AHC - Async Http Client made by Jeanfrancois Arcand. There is also interesting implementation from Apache - HttpAsyncClient, but it is still in "very early stages of development" and others haven't been in similar or better shape as AHC. How this works? Non-blocking clients allow users to make same asynchronous requests as we can do with standard approach but implementation is different - threads are better utilized, they don't spend most of time in idle state. Simply described - when you make a request (send it over the network), you are waiting for reply from other side. And there comes main advantage of non-blocking approach - it uses these threads for further work, like making other requests or processing responses etc.. Idle time is minimized and your resources (threads) will be far better used. Who should consider using this? Everyone who is making lots of asynchronous requests. I haven't done proper benchmark yet, but some simple dumb tests are showing huge improvement in cases where lots of concurrent asynchronous requests are made in short period. Last but not least - this module is still experimental, so if you don't like something or if you have ideas for improvements/any feedback, feel free to comment this blog post, send mail to [email protected] or contact me personally. All feedback is greatly appreciated! maven dependency (will be present in java.net maven 2 repo by the end of the day): link: http://download.java.net/maven/2/com/sun/jersey/experimental/jersey-non-blocking-client <dependency> <groupId>com.sun.jersey.experimental</groupId> <artifactId>jersey-non-blocking-client</artifactId> <version>1.9-SNAPSHOT</version> </dependency> code snippet: ClientConfig cc = new DefaultNonBlockingClientConfig(); cc.getProperties().put(NonBlockingClientConfig.PROPERTY_THREADPOOL_SIZE, 10); // default value, feel free to change Client c = NonBlockingClient.create(cc); AsyncWebResource awr = c.asyncResource("http://oracle.com"); Future<ClientResponse> responseFuture = awr.get(ClientResponse.class); // or awr.get(new TypeListener<ClientResponse>(ClientResponse.class) { @Override public void onComplete(Future<ClientResponse> f) throws InterruptedException { ... } }); javadoc (temporary location, won't be updated): http://anise.cz/~paja/jersey-non-blocking-client/

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  • Oracle Announces the Winners of the 2014 Oracle Sustainability Innovation Award

    - by Evelyn Neumayr
    Oracle will be honoring the winners of the 2014 Sustainability Innovation Award, one of the Oracle Excellence Awards, at the Oracle OpenWorld conference in San Francisco. This award recognizes the innovative use of Oracle technology to address global sustainability business challenges. The winning customers reduced their environmental footprint while also reducing costs using green business practices and Oracle technology. For these customers, environmental sustainability has become an essential ingredient to doing business responsibly and successfully. Oracle will also be awarding Lacey Lewis, Senior Vice President – Finance at Cox Enterprises, with Oracle's 2014 Chief Sustainability Officer of the Year award. Lacey is being honored for the comprehensive, deep-rooted environmental sustainability program at Cox Enterprises. With a focus on conserving and protecting the environment, Cox Enterprises uses Oracle Applications and technology to drive efficiency and green business processes throughout its organization. These awards will be presented by Jeff Henley, Oracle Chairman of the Board, in Oracle's seventh annual sustainability awards session. Please join us at this awards session on Wednesday October 1 in Moscone West Room 3002 if you will be attending Oracle OpenWorld.

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  • Moving around/avoiding obstacles

    - by János Harsányi
    I would like to write a "game", where you can place an obstacle (red), and then the black dot tries to avoid it, and get to the green target. I'm using a very easy way to avoid it, if the black dot is close to the red, it changes its direction, and moves for a while, then it moves forward to the green point. How could I create a "smooth" path for the computer controlled "player"? Edit: Not the smoothness is the main point, but to avoid the red blocking "wall" and not to crash into it and then avoid it. How could I implement some path finding algorithm if I just have basically 3 points? (And what would it make the things much more complicated, if you could place multiple obstacles?)

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  • Why won't title attributes get indexed in Google?

    - by Sam
    When I search for Ride On + my site's name, I see that it's indexed. But when I search for Green Horse + my site's name, I don't see my site appearing in the results anywhere! Here's my code: <td><a href="#" title="Green Horse Ride">Ride On</a></td> Does this mean that title attributes are not indexed/shown by Google at all? What is better to use, alt? What are the other alternatives except title and alt?

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  • OWSM vs. OEG - When to use which component - 11g

    - by Prakash Yamuna
    A lot of people both internal to Oracle and customers keep asking about when should OWSM be used vs. OEG. Sometime back I posted Oracle's vision for layered SOA security Here is a quick summary: Use OWSM in Green Zone Use OEG in Red Zone (DMZ) If you need end-to-end security in which case they will want both OWSM and OEG. This is the topology I would recommend for most customers. If you need only Green Zone security - then use OWSM in conjunction with Oracle FMW products like SOA Suite, OSB, ADF, WLS, BI, etc both on the Client Side and Service Side (assuming you are using FMW technologies for both Clients and Services). If you need only Red Zone security - then use OEG on the Service Side. You can use OWSM for the Client Side if you are using FMW to build your clients.

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