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  • Parallelism in .NET – Part 16, Creating Tasks via a TaskFactory

    - by Reed
    The Task class in the Task Parallel Library supplies a large set of features.  However, when creating the task, and assigning it to a TaskScheduler, and starting the Task, there are quite a few steps involved.  This gets even more cumbersome when multiple tasks are involved.  Each task must be constructed, duplicating any options required, then started individually, potentially on a specific scheduler.  At first glance, this makes the new Task class seem like more work than ThreadPool.QueueUserWorkItem in .NET 3.5. In order to simplify this process, and make Tasks simple to use in simple cases, without sacrificing their power and flexibility, the Task Parallel Library added a new class: TaskFactory. The TaskFactory class is intended to “Provide support for creating and scheduling Task objects.”  Its entire purpose is to simplify development when working with Task instances.  The Task class provides access to the default TaskFactory via the Task.Factory static property.  By default, TaskFactory uses the default TaskScheduler to schedule tasks on a ThreadPool thread.  By using Task.Factory, we can automatically create and start a task in a single “fire and forget” manner, similar to how we did with ThreadPool.QueueUserWorkItem: Task.Factory.StartNew(() => this.ExecuteBackgroundWork(myData) ); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } This provides us with the same level of simplicity we had with ThreadPool.QueueUserWorkItem, but even more power.  For example, we can now easily wait on the task: // Start our task on a background thread var task = Task.Factory.StartNew(() => this.ExecuteBackgroundWork(myData) ); // Do other work on the main thread, // while the task above executes in the background this.ExecuteWorkSynchronously(); // Wait for the background task to finish task.Wait(); TaskFactory simplifies creation and startup of simple background tasks dramatically. In addition to using the default TaskFactory, it’s often useful to construct a custom TaskFactory.  The TaskFactory class includes an entire set of constructors which allow you to specify the default configuration for every Task instance created by that factory.  This is particularly useful when using a custom TaskScheduler.  For example, look at the sample code for starting a task on the UI thread in Part 15: // Given the following, constructed on the UI thread // TaskScheduler uiScheduler = TaskScheduler.FromCurrentSynchronizationContext(); // When inside a background task, we can do string status = GetUpdatedStatus(); (new Task(() => { statusLabel.Text = status; })) .Start(uiScheduler); This is actually quite a bit more complicated than necessary.  When we create the uiScheduler instance, we can use that to construct a TaskFactory that will automatically schedule tasks on the UI thread.  To do that, we’d create the following on our main thread, prior to constructing our background tasks: // Construct a task scheduler from the current SynchronizationContext (UI thread) var uiScheduler = TaskScheduler.FromCurrentSynchronizationContext(); // Construct a new TaskFactory using our UI scheduler var uiTaskFactory = new TaskFactory(uiScheduler); If we do this, when we’re on a background thread, we can use this new TaskFactory to marshal a Task back onto the UI thread.  Our previous code simplifies to: // When inside a background task, we can do string status = GetUpdatedStatus(); // Update our UI uiTaskFactory.StartNew( () => statusLabel.Text = status); Notice how much simpler this becomes!  By taking advantage of the convenience provided by a custom TaskFactory, we can now marshal to set data on the UI thread in a single, clear line of code!

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  • Parallelism in .NET – Part 11, Divide and Conquer via Parallel.Invoke

    - by Reed
    Many algorithms are easily written to work via recursion.  For example, most data-oriented tasks where a tree of data must be processed are much more easily handled by starting at the root, and recursively “walking” the tree.  Some algorithms work this way on flat data structures, such as arrays, as well.  This is a form of divide and conquer: an algorithm design which is based around breaking up a set of work recursively, “dividing” the total work in each recursive step, and “conquering” the work when the remaining work is small enough to be solved easily. Recursive algorithms, especially ones based on a form of divide and conquer, are often a very good candidate for parallelization. This is apparent from a common sense standpoint.  Since we’re dividing up the total work in the algorithm, we have an obvious, built-in partitioning scheme.  Once partitioned, the data can be worked upon independently, so there is good, clean isolation of data. Implementing this type of algorithm is fairly simple.  The Parallel class in .NET 4 includes a method suited for this type of operation: Parallel.Invoke.  This method works by taking any number of delegates defined as an Action, and operating them all in parallel.  The method returns when every delegate has completed: Parallel.Invoke( () => { Console.WriteLine("Action 1 executing in thread {0}", Thread.CurrentThread.ManagedThreadId); }, () => { Console.WriteLine("Action 2 executing in thread {0}", Thread.CurrentThread.ManagedThreadId); }, () => { Console.WriteLine("Action 3 executing in thread {0}", Thread.CurrentThread.ManagedThreadId); } ); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Running this simple example demonstrates the ease of using this method.  For example, on my system, I get three separate thread IDs when running the above code.  By allowing any number of delegates to be executed directly, concurrently, the Parallel.Invoke method provides us an easy way to parallelize any algorithm based on divide and conquer.  We can divide our work in each step, and execute each task in parallel, recursively. For example, suppose we wanted to implement our own quicksort routine.  The quicksort algorithm can be designed based on divide and conquer.  In each iteration, we pick a pivot point, and use that to partition the total array.  We swap the elements around the pivot, then recursively sort the lists on each side of the pivot.  For example, let’s look at this simple, sequential implementation of quicksort: public static void QuickSort<T>(T[] array) where T : IComparable<T> { QuickSortInternal(array, 0, array.Length - 1); } private static void QuickSortInternal<T>(T[] array, int left, int right) where T : IComparable<T> { if (left >= right) { return; } SwapElements(array, left, (left + right) / 2); int last = left; for (int current = left + 1; current <= right; ++current) { if (array[current].CompareTo(array[left]) < 0) { ++last; SwapElements(array, last, current); } } SwapElements(array, left, last); QuickSortInternal(array, left, last - 1); QuickSortInternal(array, last + 1, right); } static void SwapElements<T>(T[] array, int i, int j) { T temp = array[i]; array[i] = array[j]; array[j] = temp; } Here, we implement the quicksort algorithm in a very common, divide and conquer approach.  Running this against the built-in Array.Sort routine shows that we get the exact same answers (although the framework’s sort routine is slightly faster).  On my system, for example, I can use framework’s sort to sort ten million random doubles in about 7.3s, and this implementation takes about 9.3s on average. Looking at this routine, though, there is a clear opportunity to parallelize.  At the end of QuickSortInternal, we recursively call into QuickSortInternal with each partition of the array after the pivot is chosen.  This can be rewritten to use Parallel.Invoke by simply changing it to: // Code above is unchanged... SwapElements(array, left, last); Parallel.Invoke( () => QuickSortInternal(array, left, last - 1), () => QuickSortInternal(array, last + 1, right) ); } This routine will now run in parallel.  When executing, we now see the CPU usage across all cores spike while it executes.  However, there is a significant problem here – by parallelizing this routine, we took it from an execution time of 9.3s to an execution time of approximately 14 seconds!  We’re using more resources as seen in the CPU usage, but the overall result is a dramatic slowdown in overall processing time. This occurs because parallelization adds overhead.  Each time we split this array, we spawn two new tasks to parallelize this algorithm!  This is far, far too many tasks for our cores to operate upon at a single time.  In effect, we’re “over-parallelizing” this routine.  This is a common problem when working with divide and conquer algorithms, and leads to an important observation: When parallelizing a recursive routine, take special care not to add more tasks than necessary to fully utilize your system. This can be done with a few different approaches, in this case.  Typically, the way to handle this is to stop parallelizing the routine at a certain point, and revert back to the serial approach.  Since the first few recursions will all still be parallelized, our “deeper” recursive tasks will be running in parallel, and can take full advantage of the machine.  This also dramatically reduces the overhead added by parallelizing, since we’re only adding overhead for the first few recursive calls.  There are two basic approaches we can take here.  The first approach would be to look at the total work size, and if it’s smaller than a specific threshold, revert to our serial implementation.  In this case, we could just check right-left, and if it’s under a threshold, call the methods directly instead of using Parallel.Invoke. The second approach is to track how “deep” in the “tree” we are currently at, and if we are below some number of levels, stop parallelizing.  This approach is a more general-purpose approach, since it works on routines which parse trees as well as routines working off of a single array, but may not work as well if a poor partitioning strategy is chosen or the tree is not balanced evenly. This can be written very easily.  If we pass a maxDepth parameter into our internal routine, we can restrict the amount of times we parallelize by changing the recursive call to: // Code above is unchanged... SwapElements(array, left, last); if (maxDepth < 1) { QuickSortInternal(array, left, last - 1, maxDepth); QuickSortInternal(array, last + 1, right, maxDepth); } else { --maxDepth; Parallel.Invoke( () => QuickSortInternal(array, left, last - 1, maxDepth), () => QuickSortInternal(array, last + 1, right, maxDepth)); } We no longer allow this to parallelize indefinitely – only to a specific depth, at which time we revert to a serial implementation.  By starting the routine with a maxDepth equal to Environment.ProcessorCount, we can restrict the total amount of parallel operations significantly, but still provide adequate work for each processing core. With this final change, my timings are much better.  On average, I get the following timings: Framework via Array.Sort: 7.3 seconds Serial Quicksort Implementation: 9.3 seconds Naive Parallel Implementation: 14 seconds Parallel Implementation Restricting Depth: 4.7 seconds Finally, we are now faster than the framework’s Array.Sort implementation.

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  • Parallelism in .NET – Part 6, Declarative Data Parallelism

    - by Reed
    When working with a problem that can be decomposed by data, we have a collection, and some operation being performed upon the collection.  I’ve demonstrated how this can be parallelized using the Task Parallel Library and imperative programming using imperative data parallelism via the Parallel class.  While this provides a huge step forward in terms of power and capabilities, in many cases, special care must still be given for relative common scenarios. C# 3.0 and Visual Basic 9.0 introduced a new, declarative programming model to .NET via the LINQ Project.  When working with collections, we can now write software that describes what we want to occur without having to explicitly state how the program should accomplish the task.  By taking advantage of LINQ, many operations become much shorter, more elegant, and easier to understand and maintain.  Version 4.0 of the .NET framework extends this concept into the parallel computation space by introducing Parallel LINQ. Before we delve into PLINQ, let’s begin with a short discussion of LINQ.  LINQ, the extensions to the .NET Framework which implement language integrated query, set, and transform operations, is implemented in many flavors.  For our purposes, we are interested in LINQ to Objects.  When dealing with parallelizing a routine, we typically are dealing with in-memory data storage.  More data-access oriented LINQ variants, such as LINQ to SQL and LINQ to Entities in the Entity Framework fall outside of our concern, since the parallelism there is the concern of the data base engine processing the query itself. LINQ (LINQ to Objects in particular) works by implementing a series of extension methods, most of which work on IEnumerable<T>.  The language enhancements use these extension methods to create a very concise, readable alternative to using traditional foreach statement.  For example, let’s revisit our minimum aggregation routine we wrote in Part 4: double min = double.MaxValue; foreach(var item in collection) { double value = item.PerformComputation(); min = System.Math.Min(min, value); } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Here, we’re doing a very simple computation, but writing this in an imperative style.  This can be loosely translated to English as: Create a very large number, and save it in min Loop through each item in the collection. For every item: Perform some computation, and save the result If the computation is less than min, set min to the computation Although this is fairly easy to follow, it’s quite a few lines of code, and it requires us to read through the code, step by step, line by line, in order to understand the intention of the developer. We can rework this same statement, using LINQ: double min = collection.Min(item => item.PerformComputation()); Here, we’re after the same information.  However, this is written using a declarative programming style.  When we see this code, we’d naturally translate this to English as: Save the Min value of collection, determined via calling item.PerformComputation() That’s it – instead of multiple logical steps, we have one single, declarative request.  This makes the developer’s intentions very clear, and very easy to follow.  The system is free to implement this using whatever method required. Parallel LINQ (PLINQ) extends LINQ to Objects to support parallel operations.  This is a perfect fit in many cases when you have a problem that can be decomposed by data.  To show this, let’s again refer to our minimum aggregation routine from Part 4, but this time, let’s review our final, parallelized version: // Safe, and fast! double min = double.MaxValue; // Make a "lock" object object syncObject = new object(); Parallel.ForEach( collection, // First, we provide a local state initialization delegate. () => double.MaxValue, // Next, we supply the body, which takes the original item, loop state, // and local state, and returns a new local state (item, loopState, localState) => { double value = item.PerformComputation(); return System.Math.Min(localState, value); }, // Finally, we provide an Action<TLocal>, to "merge" results together localState => { // This requires locking, but it's only once per used thread lock(syncObj) min = System.Math.Min(min, localState); } ); Here, we’re doing the same computation as above, but fully parallelized.  Describing this in English becomes quite a feat: Create a very large number, and save it in min Create a temporary object we can use for locking Call Parallel.ForEach, specifying three delegates For the first delegate: Initialize a local variable to hold the local state to a very large number For the second delegate: For each item in the collection, perform some computation, save the result If the result is less than our local state, save the result in local state For the final delegate: Take a lock on our temporary object to protect our min variable Save the min of our min and local state variables Although this solves our problem, and does it in a very efficient way, we’ve created a set of code that is quite a bit more difficult to understand and maintain. PLINQ provides us with a very nice alternative.  In order to use PLINQ, we need to learn one new extension method that works on IEnumerable<T> – ParallelEnumerable.AsParallel(). That’s all we need to learn in order to use PLINQ: one single method.  We can write our minimum aggregation in PLINQ very simply: double min = collection.AsParallel().Min(item => item.PerformComputation()); By simply adding “.AsParallel()” to our LINQ to Objects query, we converted this to using PLINQ and running this computation in parallel!  This can be loosely translated into English easily, as well: Process the collection in parallel Get the Minimum value, determined by calling PerformComputation on each item Here, our intention is very clear and easy to understand.  We just want to perform the same operation we did in serial, but run it “as parallel”.  PLINQ completely extends LINQ to Objects: the entire functionality of LINQ to Objects is available.  By simply adding a call to AsParallel(), we can specify that a collection should be processed in parallel.  This is simple, safe, and incredibly useful.

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  • Parallelism in .NET – Part 20, Using Task with Existing APIs

    - by Reed
    Although the Task class provides a huge amount of flexibility for handling asynchronous actions, the .NET Framework still contains a large number of APIs that are based on the previous asynchronous programming model.  While Task and Task<T> provide a much nicer syntax as well as extending the flexibility, allowing features such as continuations based on multiple tasks, the existing APIs don’t directly support this workflow. There is a method in the TaskFactory class which can be used to adapt the existing APIs to the new Task class: TaskFactory.FromAsync.  This method provides a way to convert from the BeginOperation/EndOperation method pair syntax common through .NET Framework directly to a Task<T> containing the results of the operation in the task’s Result parameter. While this method does exist, it unfortunately comes at a cost – the method overloads are far from simple to decipher, and the resulting code is not always as easily understood as newer code based directly on the Task class.  For example, a single call to handle WebRequest.BeginGetResponse/EndGetReponse, one of the easiest “pairs” of methods to use, looks like the following: var task = Task.Factory.FromAsync<WebResponse>( request.BeginGetResponse, request.EndGetResponse, null); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } The compiler is unfortunately unable to infer the correct type, and, as a result, the WebReponse must be explicitly mentioned in the method call.  As a result, I typically recommend wrapping this into an extension method to ease use.  For example, I would place the above in an extension method like: public static class WebRequestExtensions { public static Task<WebResponse> GetReponseAsync(this WebRequest request) { return Task.Factory.FromAsync<WebResponse>( request.BeginGetResponse, request.EndGetResponse, null); } } This dramatically simplifies usage.  For example, if we wanted to asynchronously check to see if this blog supported XHTML 1.0, and report that in a text box to the user, we could do: var webRequest = WebRequest.Create("http://www.reedcopsey.com"); webRequest.GetReponseAsync().ContinueWith(t => { using (var sr = new StreamReader(t.Result.GetResponseStream())) { string str = sr.ReadLine();; this.textBox1.Text = string.Format("Page at {0} supports XHTML 1.0: {1}", t.Result.ResponseUri, str.Contains("XHTML 1.0")); } }, TaskScheduler.FromCurrentSynchronizationContext());   By using a continuation with a TaskScheduler based on the current synchronization context, we can keep this request asynchronous, check based on the first line of the response string, and report the results back on our UI directly.

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  • Parallelism in .NET – Part 1, Decomposition

    - by Reed
    The first step in designing any parallelized system is Decomposition.  Decomposition is nothing more than taking a problem space and breaking it into discrete parts.  When we want to work in parallel, we need to have at least two separate things that we are trying to run.  We do this by taking our problem and decomposing it into parts. There are two common abstractions that are useful when discussing parallel decomposition: Data Decomposition and Task Decomposition.  These two abstractions allow us to think about our problem in a way that helps leads us to correct decision making in terms of the algorithms we’ll use to parallelize our routine. To start, I will make a couple of minor points. I’d like to stress that Decomposition has nothing to do with specific algorithms or techniques.  It’s about how you approach and think about the problem, not how you solve the problem using a specific tool, technique, or library.  Decomposing the problem is about constructing the appropriate mental model: once this is done, you can choose the appropriate design and tools, which is a subject for future posts. Decomposition, being unrelated to tools or specific techniques, is not specific to .NET in any way.  This should be the first step to parallelizing a problem, and is valid using any framework, language, or toolset.  However, this gives us a starting point – without a proper understanding of decomposition, it is difficult to understand the proper usage of specific classes and tools within the .NET framework. Data Decomposition is often the simpler abstraction to use when trying to parallelize a routine.  In order to decompose our problem domain by data, we take our entire set of data and break it into smaller, discrete portions, or chunks.  We then work on each chunk in the data set in parallel. This is particularly useful if we can process each element of data independently of the rest of the data.  In a situation like this, there are some wonderfully simple techniques we can use to take advantage of our data.  By decomposing our domain by data, we can very simply parallelize our routines.  In general, we, as developers, should be always searching for data that can be decomposed. Finding data to decompose if fairly simple, in many instances.  Data decomposition is typically used with collections of data.  Any time you have a collection of items, and you’re going to perform work on or with each of the items, you potentially have a situation where parallelism can be exploited.  This is fairly easy to do in practice: look for iteration statements in your code, such as for and foreach. Granted, every for loop is not a candidate to be parallelized.  If the collection is being modified as it’s iterated, or the processing of elements depends on other elements, the iteration block may need to be processed in serial.  However, if this is not the case, data decomposition may be possible. Let’s look at one example of how we might use data decomposition.  Suppose we were working with an image, and we were applying a simple contrast stretching filter.  When we go to apply the filter, once we know the minimum and maximum values, we can apply this to each pixel independently of the other pixels.  This means that we can easily decompose this problem based off data – we will do the same operation, in parallel, on individual chunks of data (each pixel). Task Decomposition, on the other hand, is focused on the individual tasks that need to be performed instead of focusing on the data.  In order to decompose our problem domain by tasks, we need to think about our algorithm in terms of discrete operations, or tasks, which can then later be parallelized. Task decomposition, in practice, can be a bit more tricky than data decomposition.  Here, we need to look at what our algorithm actually does, and how it performs its actions.  Once we have all of the basic steps taken into account, we can try to analyze them and determine whether there are any constraints in terms of shared data or ordering.  There are no simple things to look for in terms of finding tasks we can decompose for parallelism; every algorithm is unique in terms of its tasks, so every algorithm will have unique opportunities for task decomposition. For example, say we want our software to perform some customized actions on startup, prior to showing our main screen.  Perhaps we want to check for proper licensing, notify the user if the license is not valid, and also check for updates to the program.  Once we verify the license, and that there are no updates, we’ll start normally.  In this case, we can decompose this problem into tasks – we have a few tasks, but there are at least two discrete, independent tasks (check licensing, check for updates) which we can perform in parallel.  Once those are completed, we will continue on with our other tasks. One final note – Data Decomposition and Task Decomposition are not mutually exclusive.  Often, you’ll mix the two approaches while trying to parallelize a single routine.  It’s possible to decompose your problem based off data, then further decompose the processing of each element of data based on tasks.  This just provides a framework for thinking about our algorithms, and for discussing the problem.

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  • How do I run Firefox OS as a standalone application?

    - by JamesTheAwesomeDude
    I got the add-on for the Firefox OS simulator, and it works great! It even keeps functioning after Firefox is closed, so I can save processing power for other things. I'd like to run it as a standalone application, so that I don't even have to open Firefox in the first place. I've gone to the System Monitor, and it says that the process (I guessed which by CPU usage and filename) was started via /home/james/.mozilla/firefox-trunk/vkuuxfit.default/extensions/[email protected]/resources/r2d2b2g/data/linux64/b2g/plugin-container 3386 true tab, so I tried running that in the Terminal (after I'd closed the simulator, of course,) but it gives this: james@james-OptiPlex-GX620:~/.mozilla/firefox-trunk/vkuuxfit.default/extensions/[email protected]/resources/r2d2b2g/data/linux64/b2g$ ./plugin-container 3386 true tab ./plugin-container: error while loading shared libraries: libxpcom.so: cannot open shared object file: No such file or directory james@james-OptiPlex-GX620:~/.mozilla/firefox-trunk/vkuuxfit.default/extensions/[email protected]/resources/r2d2b2g/data/linux64/b2g$ What should I do? Is what I'm attempting even possible? (It should be, since the simulator kept running even after Firefox itself was closed...) NOTE: I've tried chmod u+sx plugin-container, but that didn't help.

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  • Performance and Optimization Isn’t Evil

    - by Reed
    Donald Knuth is a fairly amazing guy.  I consider him one of the most influential contributors to computer science of all time.  Unfortunately, most of the time I hear his name, I cringe.  This is because it’s typically somebody quoting a small portion of one of his famous statements on optimization: “premature optimization is the root of all evil.” I mention that this is only a portion of the entire quote, and, as such, I feel that Knuth is being quoted out of context.  Optimization is important.  It is a critical part of every software development effort, and should never be ignored.  A developer who ignores optimization is not a professional.  Every developer should understand optimization – know what to optimize, when to optimize it, and how to think about code in a way that is intelligent and productive from day one. I want to start by discussing my own, personal motivation here.  I recently wrote about a performance issue I ran across, and was slammed by multiple comments and emails that effectively boiled down to: “You’re an idiot.  Premature optimization is the root of all evil.  This doesn’t matter.”  It didn’t matter that I discovered this while measuring in a profiler, and that it was a portion of my code base that can take “many hours to complete.”  Even so, multiple people instantly jump to “it’s premature – it doesn’t matter.” This is a common thread I see.  For example, StackOverflow has many pages of posts with answers that boil down to (mis)quoting Knuth.  In fact, just about any question relating to a performance related issue gets this quote thrown at it immediately – whether it deserves it or not.  That being said, I did receive some positive comments and emails as well.  Many people want to understand how to optimize their code, approaches to take, tools and techniques they can use, and any other advice they can discover. First, lets get back to Knuth – I mentioned before that Knuth is being quoted out of context.  Lets start by looking at the entire quote from his 1974 paper Structured Programming with go to Statements: “We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%. A good programmer will not be lulled into complacency by such reasoning, he will be wise to look carefully at the critical code; but only after that code has been identified.” Ironically, if you read Knuth’s original paper, this statement was made in the middle of a discussion of how Knuth himself had changed how he approaches optimization.  It was never a statement saying “don’t optimize”, but rather, “optimizing intelligently provides huge advantages.”  His approach had three benefits: “a) it doesn’t take long” … “b) the payoff is real”, c) you can “be less efficient in the other parts of my programs, which therefore are more readable and more easily written and debugged.” Looking at Knuth’s premise here, and reading that section of his paper, really leads to a few observations: Optimization is important  “he will be wise to look carefully at the critical code” Normally, 3% of your code – three lines out of every 100 you write, are “critical code” and will require some optimization: “we should not pass up our opportunities in that critical 3%” Optimization, if done well, should not be time consuming: “it doesn’t take long” Optimization, if done correctly, provides real benefits: “the payoff is real” None of this is new information.  People who care about optimization have been discussing this for years – for example, Rico Mariani’s Designing For Performance (a fantastic article) discusses many of the same issues very intelligently. That being said, many developers seem unable or unwilling to consider optimization.  Many others don’t seem to know where to start.  As such, I’m going to spend some time writing about optimization – what is it, how should we think about it, and what can we do to improve our own code.

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  • Parallelism in .NET – Part 19, TaskContinuationOptions

    - by Reed
    My introduction to Task continuations demonstrates continuations on the Task class.  In addition, I’ve shown how continuations allow handling of multiple tasks in a clean, concise manner.  Continuations can also be used to handle exceptional situations using a clean, simple syntax. In addition to standard Task continuations , the Task class provides some options for filtering continuations automatically.  This is handled via the TaskContinationOptions enumeration, which provides hints to the TaskScheduler that it should only continue based on the operation of the antecedent task. This is especially useful when dealing with exceptions.  For example, we can extend the sample from our earlier continuation discussion to include support for handling exceptions thrown by the Factorize method: // Get a copy of the UI-thread task scheduler up front to use later var uiScheduler = TaskScheduler.FromCurrentSynchronizationContext(); // Start our task var factorize = Task.Factory.StartNew( () => { int primeFactor1 = 0; int primeFactor2 = 0; bool result = Factorize(10298312, ref primeFactor1, ref primeFactor2); return new { Result = result, Factor1 = primeFactor1, Factor2 = primeFactor2 }; }); // When we succeed, report the results to the UI factorize.ContinueWith(task => textBox1.Text = string.Format("{0}/{1} [Succeeded {2}]", task.Result.Factor1, task.Result.Factor2, task.Result.Result), CancellationToken.None, TaskContinuationOptions.NotOnFaulted, uiScheduler); // When we have an exception, report it factorize.ContinueWith(task => textBox1.Text = string.Format("Error: {0}", task.Exception.Message), CancellationToken.None, TaskContinuationOptions.OnlyOnFaulted, uiScheduler); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } The above code works by using a combination of features.  First, we schedule our task, the same way as in the previous example.  However, in this case, we use a different overload of Task.ContinueWith which allows us to specify both a specific TaskScheduler (in order to have your continuation run on the UI’s synchronization context) as well as a TaskContinuationOption.  In the first continuation, we tell the continuation that we only want it to run when there was not an exception by specifying TaskContinuationOptions.NotOnFaulted.  When our factorize task completes successfully, this continuation will automatically run on the UI thread, and provide the appropriate feedback. However, if the factorize task has an exception – for example, if the Factorize method throws an exception due to an improper input value, the second continuation will run.  This occurs due to the specification of TaskContinuationOptions.OnlyOnFaulted in the options.  In this case, we’ll report the error received to the user. We can use TaskContinuationOptions to filter our continuations by whether or not an exception occurred and whether or not a task was cancelled.  This allows us to handle many situations, and is especially useful when trying to maintain a valid application state without ever blocking the user interface.  The same concepts can be extended even further, and allow you to chain together many tasks based on the success of the previous ones.  Continuations can even be used to create a state machine with full error handling, all without blocking the user interface thread.

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  • Launching a WPF Window in a Separate Thread, Part 1

    - by Reed
    Typically, I strongly recommend keeping the user interface within an application’s main thread, and using multiple threads to move the actual “work” into background threads.  However, there are rare times when creating a separate, dedicated thread for a Window can be beneficial.  This is even acknowledged in the MSDN samples, such as the Multiple Windows, Multiple Threads sample.  However, doing this correctly is difficult.  Even the referenced MSDN sample has major flaws, and will fail horribly in certain scenarios.  To ease this, I wrote a small class that alleviates some of the difficulties involved. The MSDN Multiple Windows, Multiple Threads Sample shows how to launch a new thread with a WPF Window, and will work in most cases.  The sample code (commented and slightly modified) works out to the following: // Create a thread Thread newWindowThread = new Thread(new ThreadStart( () => { // Create and show the Window Window1 tempWindow = new Window1(); tempWindow.Show(); // Start the Dispatcher Processing System.Windows.Threading.Dispatcher.Run(); })); // Set the apartment state newWindowThread.SetApartmentState(ApartmentState.STA); // Make the thread a background thread newWindowThread.IsBackground = true; // Start the thread newWindowThread.Start(); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } This sample creates a thread, marks it as single threaded apartment state, and starts the Dispatcher on that thread. That is the minimum requirements to get a Window displaying and handling messages correctly, but, unfortunately, has some serious flaws. The first issue – the created thread will run continuously until the application shuts down, given the code in the sample.  The problem is that the ThreadStart delegate used ends with running the Dispatcher.  However, nothing ever stops the Dispatcher processing.  The thread was created as a Background thread, which prevents it from keeping the application alive, but the Dispatcher will continue to pump dispatcher frames until the application shuts down. In order to fix this, we need to call Dispatcher.InvokeShutdown after the Window is closed.  This would require modifying the above sample to subscribe to the Window’s Closed event, and, at that point, shutdown the Dispatcher: // Create a thread Thread newWindowThread = new Thread(new ThreadStart( () => { Window1 tempWindow = new Window1(); // When the window closes, shut down the dispatcher tempWindow.Closed += (s,e) => Dispatcher.CurrentDispatcher.BeginInvokeShutdown(DispatcherPriority.Background); tempWindow.Show(); // Start the Dispatcher Processing System.Windows.Threading.Dispatcher.Run(); })); // Setup and start thread as before This eliminates the first issue.  Now, when the Window is closed, the new thread’s Dispatcher will shut itself down, which in turn will cause the thread to complete. The above code will work correctly for most situations.  However, there is still a potential problem which could arise depending on the content of the Window1 class.  This is particularly nasty, as the code could easily work for most windows, but fail on others. The problem is, at the point where the Window is constructed, there is no active SynchronizationContext.  This is unlikely to be a problem in most cases, but is an absolute requirement if there is code within the constructor of Window1 which relies on a context being in place. While this sounds like an edge case, it’s fairly common.  For example, if a BackgroundWorker is started within the constructor, or a TaskScheduler is built using TaskScheduler.FromCurrentSynchronizationContext() with the expectation of synchronizing work to the UI thread, an exception will be raised at some point.  Both of these classes rely on the existence of a proper context being installed to SynchronizationContext.Current, which happens automatically, but not until Dispatcher.Run is called.  In the above case, SynchronizationContext.Current will return null during the Window’s construction, which can cause exceptions to occur or unexpected behavior. Luckily, this is fairly easy to correct.  We need to do three things, in order, prior to creating our Window: Create and initialize the Dispatcher for the new thread manually Create a synchronization context for the thread which uses the Dispatcher Install the synchronization context Creating the Dispatcher is quite simple – The Dispatcher.CurrentDispatcher property gets the current thread’s Dispatcher and “creates a new Dispatcher if one is not already associated with the thread.”  Once we have the correct Dispatcher, we can create a SynchronizationContext which uses the dispatcher by creating a DispatcherSynchronizationContext.  Finally, this synchronization context can be installed as the current thread’s context via SynchronizationContext.SetSynchronizationContext.  These three steps can easily be added to the above via a single line of code: // Create a thread Thread newWindowThread = new Thread(new ThreadStart( () => { // Create our context, and install it: SynchronizationContext.SetSynchronizationContext( new DispatcherSynchronizationContext( Dispatcher.CurrentDispatcher)); Window1 tempWindow = new Window1(); // When the window closes, shut down the dispatcher tempWindow.Closed += (s,e) => Dispatcher.CurrentDispatcher.BeginInvokeShutdown(DispatcherPriority.Background); tempWindow.Show(); // Start the Dispatcher Processing System.Windows.Threading.Dispatcher.Run(); })); // Setup and start thread as before This now forces the synchronization context to be in place before the Window is created and correctly shuts down the Dispatcher when the window closes. However, there are quite a few steps.  In my next post, I’ll show how to make this operation more reusable by creating a class with a far simpler API…

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  • Parallelism in .NET – Part 8, PLINQ’s ForAll Method

    - by Reed
    Parallel LINQ extends LINQ to Objects, and is typically very similar.  However, as I previously discussed, there are some differences.  Although the standard way to handle simple Data Parellelism is via Parallel.ForEach, it’s possible to do the same thing via PLINQ. PLINQ adds a new method unavailable in standard LINQ which provides new functionality… LINQ is designed to provide a much simpler way of handling querying, including filtering, ordering, grouping, and many other benefits.  Reading the description in LINQ to Objects on MSDN, it becomes clear that the thinking behind LINQ deals with retrieval of data.  LINQ works by adding a functional programming style on top of .NET, allowing us to express filters in terms of predicate functions, for example. PLINQ is, generally, very similar.  Typically, when using PLINQ, we write declarative statements to filter a dataset or perform an aggregation.  However, PLINQ adds one new method, which provides a very different purpose: ForAll. The ForAll method is defined on ParallelEnumerable, and will work upon any ParallelQuery<T>.  Unlike the sequence operators in LINQ and PLINQ, ForAll is intended to cause side effects.  It does not filter a collection, but rather invokes an action on each element of the collection. At first glance, this seems like a bad idea.  For example, Eric Lippert clearly explained two philosophical objections to providing an IEnumerable<T>.ForEach extension method, one of which still applies when parallelized.  The sole purpose of this method is to cause side effects, and as such, I agree that the ForAll method “violates the functional programming principles that all the other sequence operators are based upon”, in exactly the same manner an IEnumerable<T>.ForEach extension method would violate these principles.  Eric Lippert’s second reason for disliking a ForEach extension method does not necessarily apply to ForAll – replacing ForAll with a call to Parallel.ForEach has the same closure semantics, so there is no loss there. Although ForAll may have philosophical issues, there is a pragmatic reason to include this method.  Without ForAll, we would take a fairly serious performance hit in many situations.  Often, we need to perform some filtering or grouping, then perform an action using the results of our filter.  Using a standard foreach statement to perform our action would avoid this philosophical issue: // Filter our collection var filteredItems = collection.AsParallel().Where( i => i.SomePredicate() ); // Now perform an action foreach (var item in filteredItems) { // These will now run serially item.DoSomething(); } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } This would cause a loss in performance, since we lose any parallelism in place, and cause all of our actions to be run serially. We could easily use a Parallel.ForEach instead, which adds parallelism to the actions: // Filter our collection var filteredItems = collection.AsParallel().Where( i => i.SomePredicate() ); // Now perform an action once the filter completes Parallel.ForEach(filteredItems, item => { // These will now run in parallel item.DoSomething(); }); This is a noticeable improvement, since both our filtering and our actions run parallelized.  However, there is still a large bottleneck in place here.  The problem lies with my comment “perform an action once the filter completes”.  Here, we’re parallelizing the filter, then collecting all of the results, blocking until the filter completes.  Once the filtering of every element is completed, we then repartition the results of the filter, reschedule into multiple threads, and perform the action on each element.  By moving this into two separate statements, we potentially double our parallelization overhead, since we’re forcing the work to be partitioned and scheduled twice as many times. This is where the pragmatism comes into play.  By violating our functional principles, we gain the ability to avoid the overhead and cost of rescheduling the work: // Perform an action on the results of our filter collection .AsParallel() .Where( i => i.SomePredicate() ) .ForAll( i => i.DoSomething() ); The ability to avoid the scheduling overhead is a compelling reason to use ForAll.  This really goes back to one of the key points I discussed in data parallelism: Partition your problem in a way to place the most work possible into each task.  Here, this means leaving the statement attached to the expression, even though it causes side effects and is not standard usage for LINQ. This leads to my one guideline for using ForAll: The ForAll extension method should only be used to process the results of a parallel query, as returned by a PLINQ expression. Any other usage scenario should use Parallel.ForEach, instead.

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  • Dell XPS + Apple Mini-Display Adaptor + Projector = KABOOM

    - by D'Arcy Lussier
    Remember how I blogged about rolling with the punches? So today my buddy and Prairie Dev Con speaker James Chambers went to present on Microsoft MVC. He brought his fancy new Dell XPS laptop, which he had used to do presentations and stream internet video to a projector before. But today, the technology gods were not smiling… James tries to connect his laptop to the projector. Nothing. The projector just can’t recognize the connection. Realize that we’ve already had two sessions in that room already, so everything *should* work. The Dell XPS laptop James has doesn’t have a VGA port. Instead, it has HDMI and Mini-Display ports. James had one of the Apple mini-display to VGA adapters. If I connected my Mac Book Pro, I could connect no problem, so it wasn’t the adapter. He could connect his laptop to projectors before, so it wasn’t the laptop. And we’d already had sessions, so it wasn’t the projector. So what was it? Well, all three it turns out. Thanks to this post over at Irongeek.com we discovered that using Windows 7 on a Dell XPS with the Apple connector and a non-plug-in-play projector won’t work. So the fix was ditch the Apple connector, use the Star Tech model instead (listed in the Irongeek article). So James is good to go, he’s going to talk at the last session and we’ve moved the Windows 8 talk Jim Russell and I were going to do until tomorrow. Roll with the punches…

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  • Parallelism in .NET – Part 17, Think Continuations, not Callbacks

    - by Reed
    In traditional asynchronous programming, we’d often use a callback to handle notification of a background task’s completion.  The Task class in the Task Parallel Library introduces a cleaner alternative to the traditional callback: continuation tasks. Asynchronous programming methods typically required callback functions.  For example, MSDN’s Asynchronous Delegates Programming Sample shows a class that factorizes a number.  The original method in the example has the following signature: public static bool Factorize(int number, ref int primefactor1, ref int primefactor2) { //... .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } However, calling this is quite “tricky”, even if we modernize the sample to use lambda expressions via C# 3.0.  Normally, we could call this method like so: int primeFactor1 = 0; int primeFactor2 = 0; bool answer = Factorize(10298312, ref primeFactor1, ref primeFactor2); Console.WriteLine("{0}/{1} [Succeeded {2}]", primeFactor1, primeFactor2, answer); If we want to make this operation run in the background, and report to the console via a callback, things get tricker.  First, we need a delegate definition: public delegate bool AsyncFactorCaller( int number, ref int primefactor1, ref int primefactor2); Then we need to use BeginInvoke to run this method asynchronously: int primeFactor1 = 0; int primeFactor2 = 0; AsyncFactorCaller caller = new AsyncFactorCaller(Factorize); caller.BeginInvoke(10298312, ref primeFactor1, ref primeFactor2, result => { int factor1 = 0; int factor2 = 0; bool answer = caller.EndInvoke(ref factor1, ref factor2, result); Console.WriteLine("{0}/{1} [Succeeded {2}]", factor1, factor2, answer); }, null); This works, but is quite difficult to understand from a conceptual standpoint.  To combat this, the framework added the Event-based Asynchronous Pattern, but it isn’t much easier to understand or author. Using .NET 4’s new Task<T> class and a continuation, we can dramatically simplify the implementation of the above code, as well as make it much more understandable.  We do this via the Task.ContinueWith method.  This method will schedule a new Task upon completion of the original task, and provide the original Task (including its Result if it’s a Task<T>) as an argument.  Using Task, we can eliminate the delegate, and rewrite this code like so: var background = Task.Factory.StartNew( () => { int primeFactor1 = 0; int primeFactor2 = 0; bool result = Factorize(10298312, ref primeFactor1, ref primeFactor2); return new { Result = result, Factor1 = primeFactor1, Factor2 = primeFactor2 }; }); background.ContinueWith(task => Console.WriteLine("{0}/{1} [Succeeded {2}]", task.Result.Factor1, task.Result.Factor2, task.Result.Result)); This is much simpler to understand, in my opinion.  Here, we’re explicitly asking to start a new task, then continue the task with a resulting task.  In our case, our method used ref parameters (this was from the MSDN Sample), so there is a little bit of extra boiler plate involved, but the code is at least easy to understand. That being said, this isn’t dramatically shorter when compared with our C# 3 port of the MSDN code above.  However, if we were to extend our requirements a bit, we can start to see more advantages to the Task based approach.  For example, supposed we need to report the results in a user interface control instead of reporting it to the Console.  This would be a common operation, but now, we have to think about marshaling our calls back to the user interface.  This is probably going to require calling Control.Invoke or Dispatcher.Invoke within our callback, forcing us to specify a delegate within the delegate.  The maintainability and ease of understanding drops.  However, just as a standard Task can be created with a TaskScheduler that uses the UI synchronization context, so too can we continue a task with a specific context.  There are Task.ContinueWith method overloads which allow you to provide a TaskScheduler.  This means you can schedule the continuation to run on the UI thread, by simply doing: Task.Factory.StartNew( () => { int primeFactor1 = 0; int primeFactor2 = 0; bool result = Factorize(10298312, ref primeFactor1, ref primeFactor2); return new { Result = result, Factor1 = primeFactor1, Factor2 = primeFactor2 }; }).ContinueWith(task => textBox1.Text = string.Format("{0}/{1} [Succeeded {2}]", task.Result.Factor1, task.Result.Factor2, task.Result.Result), TaskScheduler.FromCurrentSynchronizationContext()); This is far more understandable than the alternative.  By using Task.ContinueWith in conjunction with TaskScheduler.FromCurrentSynchronizationContext(), we get a simple way to push any work onto a background thread, and update the user interface on the proper UI thread.  This technique works with Windows Presentation Foundation as well as Windows Forms, with no change in methodology.

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  • SQL SERVER – Quiz and Video – Introduction to SQL Server Security

    - by pinaldave
    This blog post is inspired from Beginning SQL Joes 2 Pros: The SQL Hands-On Guide for Beginners – SQL Exam Prep Series 70-433 – Volume 1. [Amazon] | [Flipkart] | [Kindle] | [IndiaPlaza] This is follow up blog post of my earlier blog post on the same subject - SQL SERVER – Introduction to SQL Server Security – A Primer. In the article we discussed various basics terminology of the security. The article further covers following important concepts of security. Granting Permissions Denying Permissions Revoking Permissions Above three are the most important concepts related to security and SQL Server.  There are many more things one has to learn but without beginners fundamentals one can’t learn the advanced  concepts. Let us have small quiz and check how many of you get the fundamentals right. Quiz 1) If you granted Phil control to the server, but denied his ability to create databases, what would his effective permissions be? Phil can do everything. Phil can do nothing. Phil can do everything except create databases. 2) If you granted Phil control to the server and revoked his ability to create databases, what would his effective permissions be? Phil can do everything. Phil can do nothing. Phil can do everything except create databases. 3) You have a login named James who has Control Server permission. You want to elimintate his ability to create databases without affecting any other permissions. What SQL statement would you use? ALTER LOGIN James DISABLE DROP LOGIN James DENY CREATE DATABASE To James REVOKE CREATE DATABASE To James GRANT CREATE DATABASE To James Now make sure that you write down all the answers on the piece of paper. Watch following video and read earlier article over here. If you want to change the answer you still have chance. Solution 1) 3 2) 1 3) 3 Now compare let us check the answers and compare your answers to following answers. I am very confident you will get them correct. Available at USA: Amazon India: Flipkart | IndiaPlaza Volume: 1, 2, 3, 4, 5 Please leave your feedback in the comment area for the quiz and video. Did you know all the answers of the quiz? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Joes 2 Pros, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • 2010 Visual C# MVP Award

    - by Reed
    I received a pleasant surprise today.  I was presented this morning with the 2010 Microsoft® MVP Award for Visual C#.  According to the award email, this “award is given to exceptional technical community leaders who actively share their high quality, real world expertise with others.” I feel honored and proud to receive this award, and hope that I can continue to be a valuable member of the community in the future.  Thank you to everyone who nominated me!

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  • Reflections on GiveCamp

    - by Reed
    I participated in the Seattle GiveCamp over the weekend, and am entirely impressed.  GiveCamp is a great event – I especially like how rewarding it is for everybody involved.  I strongly encourage any and all developers to watch for future GiveCamp events, and consider participating, for many reasons… GiveCamp provides real value to organizations that truly need help.  The Seattle event alone succeeded in helping sixteen non-profit organizations in many different ways.  The projects involved varied dramatically, including website redesigns, SEO, reworking data management workflows, and even game development.  Many non-profits have a strong need for good, quality technical help.  However, nearly every non-profit organization has an incredibly limited budget.  GiveCamp is a way to really give back, and provide incredibly valuable help to organizations that truly benefit. My experience has shown many developers to be incredibly generous – this is a chance to dedicate your energy to helping others in a way that really takes advantage of your expertise.  Your time as a developer is incredibly valuable, and this puts something of incredible value directly into the hands of places its needed. First, and foremost, GiveCamp is about providing technical help to non-profit organizations in need. GiveCamp can make you a better developer.  This is a fantastic opportunity for us, as developers, to work with new people, in a new setting.  The incredibly short time frame (one weekend for a deliverable project) and intense motivation to succeed provides a huge opportunity for learning from peers.  I’d personally like to thank off the developers with whom I worked – I learned something from each and every one of you.  I hope to see and work with all of you again someday. GiveCamp provides an opportunity for you to work outside of your comfort zone. While it’s always nice to be an expert, it’s also valuable to work on a project where you have little or no direct experience.  My team focused on a complete reworking of our organizations message and a complete new website redesign and deployment using WordPress.  While I’d used WordPress for my blog, and had some experience, this is completely unrelated to my professional work.  In fact, nobody on our team normally worked directly with the technologies involved – yet together we managed to succeed in delivering our goals.  As developers, it’s easy to want to stay abreast of new technology surrounding our expertise, but its rare that we get a chance to sit down and work on something practical that is completely outside of our normal realm of work.  I’m a desktop developer by trade, and spent much of the weekend working with CSS and Photoshop.  Many of the projects organizations need don’t match perfectly with the skill set in the room – yet all of the software professionals rose to the occasion and delivered practical, usable applications. GiveCamp is a short term, known commitment. While this seems obvious, I think it’s an important aspect to remember.  This is a huge part of what makes it successful – you can work, completely focused, on a project, then walk away completely when you’re done.  There is no expectation of continued involvement.  While many of the professionals I’ve talked to are willing to contribute some amount of their time beyond the camp, this is not expected. The freedom this provides is immense.  In addition, the motivation this brings is incredibly valuable.  Every developer in the room was very focused on delivering in time – you have one shot to get it as good as possible, and leave it with the organization in a way that can be maintained by them.  This is a rare experience – and excellent practice at time management for everyone involved. GiveCamp provides a great way to meet and network with your peers. Not only do you get to network with other software professionals in your area – you get to network with amazing people.  Every single person in the room is there to try to help people.  The balance of altruism, intelligence, and expertise in the room is something I’ve never before experienced. During the presentations of what was accomplished, I felt blessed to participate.  I know many people in the room were incredibly touched by the level of dedication and accomplishment over the weekend. GiveCamp is fun. At the end of the experience, I would have signed up again, even if it was a painful, tedious weekend – merely due to the amazing accomplishments achieved throughout the event.  However, the event is fun.  Everybody I talked to, the entire weekend, was having a good time.  While there were many faces focused into a near grimace at times (including mine, I’ll admit), this was always in response to a particularly challenging problem or task.  The challenges just added to the overall enjoyment of the weekend – part of why I became a developer in the first place is my love for challenge and puzzles, and a short deadline using unfamiliar technology provided plenty of opportunity for puzzles.  As soon as people would stand up, it was another smile.   If you’re a developer, I’d recommend looking at GiveCamp more closely.  Watch for an event in your area.  If there isn’t one, consider building a team and organizing an event.  The experience is worth the commitment. 

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  • C# Performance Pitfall – Interop Scenarios Change the Rules

    - by Reed
    C# and .NET, overall, really do have fantastic performance in my opinion.  That being said, the performance characteristics dramatically differ from native programming, and take some relearning if you’re used to doing performance optimization in most other languages, especially C, C++, and similar.  However, there are times when revisiting tricks learned in native code play a critical role in performance optimization in C#. I recently ran across a nasty scenario that illustrated to me how dangerous following any fixed rules for optimization can be… The rules in C# when optimizing code are very different than C or C++.  Often, they’re exactly backwards.  For example, in C and C++, lifting a variable out of loops in order to avoid memory allocations often can have huge advantages.  If some function within a call graph is allocating memory dynamically, and that gets called in a loop, it can dramatically slow down a routine. This can be a tricky bottleneck to track down, even with a profiler.  Looking at the memory allocation graph is usually the key for spotting this routine, as it’s often “hidden” deep in call graph.  For example, while optimizing some of my scientific routines, I ran into a situation where I had a loop similar to: for (i=0; i<numberToProcess; ++i) { // Do some work ProcessElement(element[i]); } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } This loop was at a fairly high level in the call graph, and often could take many hours to complete, depending on the input data.  As such, any performance optimization we could achieve would be greatly appreciated by our users. After a fair bit of profiling, I noticed that a couple of function calls down the call graph (inside of ProcessElement), there was some code that effectively was doing: // Allocate some data required DataStructure* data = new DataStructure(num); // Call into a subroutine that passed around and manipulated this data highly CallSubroutine(data); // Read and use some values from here double values = data->Foo; // Cleanup delete data; // ... return bar; Normally, if “DataStructure” was a simple data type, I could just allocate it on the stack.  However, it’s constructor, internally, allocated it’s own memory using new, so this wouldn’t eliminate the problem.  In this case, however, I could change the call signatures to allow the pointer to the data structure to be passed into ProcessElement and through the call graph, allowing the inner routine to reuse the same “data” memory instead of allocating.  At the highest level, my code effectively changed to something like: DataStructure* data = new DataStructure(numberToProcess); for (i=0; i<numberToProcess; ++i) { // Do some work ProcessElement(element[i], data); } delete data; Granted, this dramatically reduced the maintainability of the code, so it wasn’t something I wanted to do unless there was a significant benefit.  In this case, after profiling the new version, I found that it increased the overall performance dramatically – my main test case went from 35 minutes runtime down to 21 minutes.  This was such a significant improvement, I felt it was worth the reduction in maintainability. In C and C++, it’s generally a good idea (for performance) to: Reduce the number of memory allocations as much as possible, Use fewer, larger memory allocations instead of many smaller ones, and Allocate as high up the call stack as possible, and reuse memory I’ve seen many people try to make similar optimizations in C# code.  For good or bad, this is typically not a good idea.  The garbage collector in .NET completely changes the rules here. In C#, reallocating memory in a loop is not always a bad idea.  In this scenario, for example, I may have been much better off leaving the original code alone.  The reason for this is the garbage collector.  The GC in .NET is incredibly effective, and leaving the allocation deep inside the call stack has some huge advantages.  First and foremost, it tends to make the code more maintainable – passing around object references tends to couple the methods together more than necessary, and overall increase the complexity of the code.  This is something that should be avoided unless there is a significant reason.  Second, (unlike C and C++) memory allocation of a single object in C# is normally cheap and fast.  Finally, and most critically, there is a large advantage to having short lived objects.  If you lift a variable out of the loop and reuse the memory, its much more likely that object will get promoted to Gen1 (or worse, Gen2).  This can cause expensive compaction operations to be required, and also lead to (at least temporary) memory fragmentation as well as more costly collections later. As such, I’ve found that it’s often (though not always) faster to leave memory allocations where you’d naturally place them – deep inside of the call graph, inside of the loops.  This causes the objects to stay very short lived, which in turn increases the efficiency of the garbage collector, and can dramatically improve the overall performance of the routine as a whole. In C#, I tend to: Keep variable declarations in the tightest scope possible Declare and allocate objects at usage While this tends to cause some of the same goals (reducing unnecessary allocations, etc), the goal here is a bit different – it’s about keeping the objects rooted for as little time as possible in order to (attempt) to keep them completely in Gen0, or worst case, Gen1.  It also has the huge advantage of keeping the code very maintainable – objects are used and “released” as soon as possible, which keeps the code very clean.  It does, however, often have the side effect of causing more allocations to occur, but keeping the objects rooted for a much shorter time. Now – nowhere here am I suggesting that these rules are hard, fast rules that are always true.  That being said, my time spent optimizing over the years encourages me to naturally write code that follows the above guidelines, then profile and adjust as necessary.  In my current project, however, I ran across one of those nasty little pitfalls that’s something to keep in mind – interop changes the rules. In this case, I was dealing with an API that, internally, used some COM objects.  In this case, these COM objects were leading to native allocations (most likely C++) occurring in a loop deep in my call graph.  Even though I was writing nice, clean managed code, the normal managed code rules for performance no longer apply.  After profiling to find the bottleneck in my code, I realized that my inner loop, a innocuous looking block of C# code, was effectively causing a set of native memory allocations in every iteration.  This required going back to a “native programming” mindset for optimization.  Lifting these variables and reusing them took a 1:10 routine down to 0:20 – again, a very worthwhile improvement. Overall, the lessons here are: Always profile if you suspect a performance problem – don’t assume any rule is correct, or any code is efficient just because it looks like it should be Remember to check memory allocations when profiling, not just CPU cycles Interop scenarios often cause managed code to act very differently than “normal” managed code. Native code can be hidden very cleverly inside of managed wrappers

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  • Setting useLegacyV2RuntimeActivationPolicy At Runtime

    - by Reed
    Version 4.0 of the .NET Framework included a new CLR which is almost entirely backwards compatible with the 2.0 version of the CLR.  However, by default, mixed-mode assemblies targeting .NET 3.5sp1 and earlier will fail to load in a .NET 4 application.  Fixing this requires setting useLegacyV2RuntimeActivationPolicy in your app.Config for the application.  While there are many good reasons for this decision, there are times when this is extremely frustrating, especially when writing a library.  As such, there are (rare) times when it would be beneficial to set this in code, at runtime, as well as verify that it’s running correctly prior to receiving a FileLoadException. Typically, loading a pre-.NET 4 mixed mode assembly is handled simply by changing your app.Config file, and including the relevant attribute in the startup element: <?xml version="1.0" encoding="utf-8" ?> <configuration> <startup useLegacyV2RuntimeActivationPolicy="true"> <supportedRuntime version="v4.0"/> </startup> </configuration> .csharpcode { background-color: #ffffff; font-family: consolas, "Courier New", courier, monospace; color: black; font-size: small } .csharpcode pre { background-color: #ffffff; font-family: consolas, "Courier New", courier, monospace; color: black; font-size: small } .csharpcode pre { margin: 0em } .csharpcode .rem { color: #008000 } .csharpcode .kwrd { color: #0000ff } .csharpcode .str { color: #006080 } .csharpcode .op { color: #0000c0 } .csharpcode .preproc { color: #cc6633 } .csharpcode .asp { background-color: #ffff00 } .csharpcode .html { color: #800000 } .csharpcode .attr { color: #ff0000 } .csharpcode .alt { background-color: #f4f4f4; margin: 0em; width: 100% } .csharpcode .lnum { color: #606060 } This causes your application to run correctly, and load the older, mixed-mode assembly without issues. For full details on what’s happening here and why, I recommend reading Mark Miller’s detailed explanation of this attribute and the reasoning behind it. Before I show any code, let me say: I strongly recommend using the official approach of using app.config to set this policy. That being said, there are (rare) times when, for one reason or another, changing the application configuration file is less than ideal. While this is the supported approach to handling this issue, the CLR Hosting API includes a means of setting this programmatically via the ICLRRuntimeInfo interface.  Normally, this is used if you’re hosting the CLR in a native application in order to set this, at runtime, prior to loading the assemblies.  However, the F# Samples include a nice trick showing how to load this API and bind this policy, at runtime.  This was required in order to host the Managed DirectX API, which is built against an older version of the CLR. This is fairly easy to port to C#.  Instead of a direct port, I also added a little addition – by trapping the COM exception received if unable to bind (which will occur if the 2.0 CLR is already bound), I also allow a runtime check of whether this property was setup properly: public static class RuntimePolicyHelper { public static bool LegacyV2RuntimeEnabledSuccessfully { get; private set; } static RuntimePolicyHelper() { ICLRRuntimeInfo clrRuntimeInfo = (ICLRRuntimeInfo)RuntimeEnvironment.GetRuntimeInterfaceAsObject( Guid.Empty, typeof(ICLRRuntimeInfo).GUID); try { clrRuntimeInfo.BindAsLegacyV2Runtime(); LegacyV2RuntimeEnabledSuccessfully = true; } catch (COMException) { // This occurs with an HRESULT meaning // "A different runtime was already bound to the legacy CLR version 2 activation policy." LegacyV2RuntimeEnabledSuccessfully = false; } } [ComImport] [InterfaceType(ComInterfaceType.InterfaceIsIUnknown)] [Guid("BD39D1D2-BA2F-486A-89B0-B4B0CB466891")] private interface ICLRRuntimeInfo { void xGetVersionString(); void xGetRuntimeDirectory(); void xIsLoaded(); void xIsLoadable(); void xLoadErrorString(); void xLoadLibrary(); void xGetProcAddress(); void xGetInterface(); void xSetDefaultStartupFlags(); void xGetDefaultStartupFlags(); [MethodImpl(MethodImplOptions.InternalCall, MethodCodeType = MethodCodeType.Runtime)] void BindAsLegacyV2Runtime(); } } Using this, it’s possible to not only set this at runtime, but also verify, prior to loading your mixed mode assembly, whether this will succeed. In my case, this was quite useful – I am working on a library purely for internal use which uses a numerical package that is supplied with both a completely managed as well as a native solver.  The native solver uses a CLR 2 mixed-mode assembly, but is dramatically faster than the pure managed approach.  By checking RuntimePolicyHelper.LegacyV2RuntimeEnabledSuccessfully at runtime, I can decide whether to enable the native solver, and only do so if I successfully bound this policy. There are some tricks required here – To enable this sort of fallback behavior, you must make these checks in a type that doesn’t cause the mixed mode assembly to be loaded.  In my case, this forced me to encapsulate the library I was using entirely in a separate class, perform the check, then pass through the required calls to that class.  Otherwise, the library will load before the hosting process gets enabled, which in turn will fail. This code will also, of course, try to enable the runtime policy before the first time you use this class – which typically means just before the first time you check the boolean value.  As a result, checking this early on in the application is more likely to allow it to work. Finally, if you’re using a library, this has to be called prior to the 2.0 CLR loading.  This will cause it to fail if you try to use it to enable this policy in a plugin for most third party applications that don’t have their app.config setup properly, as they will likely have already loaded the 2.0 runtime. As an example, take a simple audio player.  The code below shows how this can be used to properly, at runtime, only use the “native” API if this will succeed, and fallback (or raise a nicer exception) if this will fail: public class AudioPlayer { private IAudioEngine audioEngine; public AudioPlayer() { if (RuntimePolicyHelper.LegacyV2RuntimeEnabledSuccessfully) { // This will load a CLR 2 mixed mode assembly this.audioEngine = new AudioEngineNative(); } else { this.audioEngine = new AudioEngineManaged(); } } public void Play(string filename) { this.audioEngine.Play(filename); } } Now – the warning: This approach works, but I would be very hesitant to use it in public facing production code, especially for anything other than initializing your own application.  While this should work in a library, using it has a very nasty side effect: you change the runtime policy of the executing application in a way that is very hidden and non-obvious.

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  • C# 5 Async, Part 2: Asynchrony Today

    - by Reed
    The .NET Framework has always supported asynchronous operations.  However, different mechanisms for supporting exist throughout the framework.  While there are at least three separate asynchronous patterns used through the framework, only the latest is directly usable with the new Visual Studio Async CTP.  Before delving into details on the new features, I will talk about existing asynchronous code, and demonstrate how to adapt it for use with the new pattern. The first asynchronous pattern used in the .NET framework was the Asynchronous Programming Model (APM).  This pattern was based around callbacks.  A method is used to start the operation.  It typically is named as BeginSomeOperation.  This method is passed a callback defined as an AsyncCallback, and returns an object that implements IAsyncResult.  Later, the IAsyncResult is used in a call to a method named EndSomeOperation, which blocks until completion and returns the value normally directly returned from the synchronous version of the operation.  Often, the EndSomeOperation call would be called from the callback function passed, which allows you to write code that never blocks. While this pattern works perfectly to prevent blocking, it can make quite confusing code, and be difficult to implement.  For example, the sample code provided for FileStream’s BeginRead/EndRead methods is not simple to understand.  In addition, implementing your own asynchronous methods requires creating an entire class just to implement the IAsyncResult. Given the complexity of the APM, other options have been introduced in later versions of the framework.  The next major pattern introduced was the Event-based Asynchronous Pattern (EAP).  This provides a simpler pattern for asynchronous operations.  It works by providing a method typically named SomeOperationAsync, which signals its completion via an event typically named SomeOperationCompleted. The EAP provides a simpler model for asynchronous programming.  It is much easier to understand and use, and far simpler to implement.  Instead of requiring a custom class and callbacks, the standard event mechanism in C# is used directly.  For example, the WebClient class uses this extensively.  A method is used, such as DownloadDataAsync, and the results are returned via the DownloadDataCompleted event. While the EAP is far simpler to understand and use than the APM, it is still not ideal.  By separating your code into method calls and event handlers, the logic of your program gets more complex.  It also typically loses the ability to block until the result is received, which is often useful.  Blocking often requires writing the code to block by hand, which is error prone and adds complexity. As a result, .NET 4 introduced a third major pattern for asynchronous programming.  The Task<T> class introduced a new, simpler concept for asynchrony.  Task and Task<T> effectively represent an operation that will complete at some point in the future.  This is a perfect model for thinking about asynchronous code, and is the preferred model for all new code going forward.  Task and Task<T> provide all of the advantages of both the APM and the EAP models – you have the ability to block on results (via Task.Wait() or Task<T>.Result), and you can stay completely asynchronous via the use of Task Continuations.  In addition, the Task class provides a new model for task composition and error and cancelation handling.  This is a far superior option to the previous asynchronous patterns. The Visual Studio Async CTP extends the Task based asynchronous model, allowing it to be used in a much simpler manner.  However, it requires the use of Task and Task<T> for all operations.

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  • Attached Property port of my Window Close Behavior

    - by Reed
    Nishant Sivakumar just posted a nice article on The Code Project.  It is a port of the MVVM-friendly Blend Behavior I wrote about in a previous article to WPF using Attached Properties. While similar to the WindowCloseBehavior code I posted on the Expression Code Gallery, Nishant Sivakumar’s version works in WPF without taking a dependency on the Expression Blend SDK. I highly recommend reading this article: Handling a Window’s Closed and Closing Events in the View-Model.  It is a very nice alternative approach to this common problem in MVVM.

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  • Async CTP Refresh for Visual Studio 2010 SP1 Released

    - by Reed
    The Visual Studio team today released an update to the Visual Studio Async CTP which allows it to be used with Visual Studio SP1.  This new CTP includes some very nice new additions over the previous CTP.  The main highlights of this release include: Compatibility with Visual Studio SP1 APIs for Windows Phone 7 Compatibility with non-English installations Compatibility with Visual Studio Express Edition More efficient Async methods due to a change in the API Numerous bug fixes New EULA which allows distribution in production environments Anybody using the Async CTP should consider upgrading to the new version immediately.  For details, visit the Visual Studio Asynchronous Programming page on MSDN.

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  • C# 5 Async, Part 3: Preparing Existing code For Await

    - by Reed
    While the Visual Studio Async CTP provides a fantastic model for asynchronous programming, it requires code to be implemented in terms of Task and Task<T>.  The CTP adds support for Task-based asynchrony to the .NET Framework methods, and promises to have these implemented directly in the framework in the future.  However, existing code outside the framework will need to be converted to using the Task class prior to being usable via the CTP. Wrapping existing asynchronous code into a Task or Task<T> is, thankfully, fairly straightforward.  There are two main approaches to this. Code written using the Asynchronous Programming Model (APM) is very easy to convert to using Task<T>.  The TaskFactory class provides the tools to directly convert APM code into a method returning a Task<T>.  This is done via the FromAsync method.  This method takes the BeginOperation and EndOperation methods, as well as any parameters and state objects as arguments, and returns a Task<T> directly. For example, we could easily convert the WebRequest BeginGetResponse and EndGetResponse methods into a method which returns a Task<WebResponse> via: Task<WebResponse> task = Task.Factory .FromAsync<WebResponse>( request.BeginGetResponse, request.EndGetResponse, null); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Event-based Asynchronous Pattern (EAP) code can also be wrapped into a Task<T>, though this requires a bit more effort than the one line of code above.  This is handled via the TaskCompletionSource<T> class.  MSDN provides a detailed example of using this to wrap an EAP operation into a method returning Task<T>.  It demonstrates handling cancellation and exception handling as well as the basic operation of the asynchronous method itself. The basic form of this operation is typically: Task<YourResult> GetResultAsync() { var tcs = new TaskCompletionSource<YourResult>(); // Handle the event, and setup the task results... this.GetResultCompleted += (o,e) => { if (e.Error != null) tcs.TrySetException(e.Error); else if (e.Cancelled) tcs.TrySetCanceled(); else tcs.TrySetResult(e.Result); }; // Call the asynchronous method this.GetResult(); // Return the task from the TaskCompletionSource return tcs.Task; } We can easily use these methods to wrap our own code into a method that returns a Task<T>.  Existing libraries which cannot be edited can be extended via Extension methods.  The CTP uses this technique to add appropriate methods throughout the framework. The suggested naming for these methods is to define these methods as “Task<YourResult> YourClass.YourOperationAsync(…)”.  However, this naming often conflicts with the default naming of the EAP.  If this is the case, the CTP has standardized on using “Task<YourResult> YourClass.YourOperationTaskAsync(…)”. Once we’ve wrapped all of our existing code into operations that return Task<T>, we can begin investigating how the Async CTP can be used with our own code.

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  • ConcurrentDictionary<TKey,TValue> used with Lazy<T>

    - by Reed
    In a recent thread on the MSDN forum for the TPL, Stephen Toub suggested mixing ConcurrentDictionary<T,U> with Lazy<T>.  This provides a fantastic model for creating a thread safe dictionary of values where the construction of the value type is expensive.  This is an incredibly useful pattern for many operations, such as value caches. The ConcurrentDictionary<TKey, TValue> class was added in .NET 4, and provides a thread-safe, lock free collection of key value pairs.  While this is a fantastic replacement for Dictionary<TKey, TValue>, it has a potential flaw when used with values where construction of the value class is expensive. The typical way this is used is to call a method such as GetOrAdd to fetch or add a value to the dictionary.  It handles all of the thread safety for you, but as a result, if two threads call this simultaneously, two instances of TValue can easily be constructed. If TValue is very expensive to construct, or worse, has side effects if constructed too often, this is less than desirable.  While you can easily work around this with locking, Stephen Toub provided a very clever alternative – using Lazy<TValue> as the value in the dictionary instead. This looks like the following.  Instead of calling: MyValue value = dictionary.GetOrAdd( key, () => new MyValue(key)); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } We would instead use a ConcurrentDictionary<TKey, Lazy<TValue>>, and write: MyValue value = dictionary.GetOrAdd( key, () => new Lazy<MyValue>( () => new MyValue(key))) .Value; This simple change dramatically changes how the operation works.  Now, if two threads call this simultaneously, instead of constructing two MyValue instances, we construct two Lazy<MyValue> instances. However, the Lazy<T> class is very cheap to construct.  Unlike “MyValue”, we can safely afford to construct this twice and “throw away” one of the instances. We then call Lazy<T>.Value at the end to fetch our “MyValue” instance.  At this point, GetOrAdd will always return the same instance of Lazy<MyValue>.  Since Lazy<T> doesn’t construct the MyValue instance until requested, the actual MyClass instance returned is only constructed once.

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  • Slides and Code from “Using C#’s Async Effectively”

    - by Reed
    The slides and code from my talk on the new async language features in C# and VB.Net are now available on https://github.com/ReedCopsey/Effective-Async This includes the complete slide deck, and all 4 projects, including: FakeService: Simple WCF service to run locally and simulate network service calls. AsyncService: Simple WCF service which wraps FakeService to demonstrate converting sync to async SimpleWPFExample: Simplest example of converting a method call to async from a synchronous version AsyncExamples: Windows Store application demonstrating main concepts, pitfalls, tips, and tricks from the slide deck

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  • Query only the first detail record for each master record

    - by Neal S.
    If I have the following master-detail relationship: owner_tbl auto_tbl --------- -------- owner --- owner auto year And I have the following table data: owner_tbl auto_tbl --------- -------- john john, corvette, 1968 john, prius, 2008 james james, f-150, 2004 james, cadillac, 2002 james, accord, 2009 jeff jeff, tesla, 2010 jeff, hyundai, 1996 Now, I want to perform a query that returns the following result: john, corvette, 1968 jeff, hyundai, 1996 james, cadillac, 2002 The query should join the two tables, and sort all the records on the "year" field, but only return the first detail record for each master record. I know how to join the tables and sort on the "year" field, but it's not clear how (or if) I might be able to only retrieve the first joined record for each owner. Three related questions: Can I perform this kind of query using LINQ-to-SQL? Can I perform the query using T-SQL? Would it be best to just create a stored procedure for the query given its likely complexity?

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