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  • Parallelism in .NET – Part 3, Imperative Data Parallelism: Early Termination

    - by Reed
    Although simple data parallelism allows us to easily parallelize many of our iteration statements, there are cases that it does not handle well.  In my previous discussion, I focused on data parallelism with no shared state, and where every element is being processed exactly the same. Unfortunately, there are many common cases where this does not happen.  If we are dealing with a loop that requires early termination, extra care is required when parallelizing. Often, while processing in a loop, once a certain condition is met, it is no longer necessary to continue processing.  This may be a matter of finding a specific element within the collection, or reaching some error case.  The important distinction here is that, it is often impossible to know until runtime, what set of elements needs to be processed. In my initial discussion of data parallelism, I mentioned that this technique is a candidate when you can decompose the problem based on the data involved, and you wish to apply a single operation concurrently on all of the elements of a collection.  This covers many of the potential cases, but sometimes, after processing some of the elements, we need to stop processing. As an example, lets go back to our previous Parallel.ForEach example with contacting a customer.  However, this time, we’ll change the requirements slightly.  In this case, we’ll add an extra condition – if the store is unable to email the customer, we will exit gracefully.  The thinking here, of course, is that if the store is currently unable to email, the next time this operation runs, it will handle the same situation, so we can just skip our processing entirely.  The original, serial case, with this extra condition, might look something like the following: foreach(var customer in customers) { // Run some process that takes some time... DateTime lastContact = theStore.GetLastContact(customer); TimeSpan timeSinceContact = DateTime.Now - lastContact; // If it's been more than two weeks, send an email, and update... if (timeSinceContact.Days > 14) { // Exit gracefully if we fail to email, since this // entire process can be repeated later without issue. if (theStore.EmailCustomer(customer) == false) break; customer.LastEmailContact = DateTime.Now; } } .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 processing our loop, but at any point, if we fail to send our email successfully, we just abandon this process, and assume that it will get handled correctly the next time our routine is run.  If we try to parallelize this using Parallel.ForEach, as we did previously, we’ll run into an error almost immediately: the break statement we’re using is only valid when enclosed within an iteration statement, such as foreach.  When we switch to Parallel.ForEach, we’re no longer within an iteration statement – we’re a delegate running in a method. This needs to be handled slightly differently when parallelized.  Instead of using the break statement, we need to utilize a new class in the Task Parallel Library: ParallelLoopState.  The ParallelLoopState class is intended to allow concurrently running loop bodies a way to interact with each other, and provides us with a way to break out of a loop.  In order to use this, we will use a different overload of Parallel.ForEach which takes an IEnumerable<T> and an Action<T, ParallelLoopState> instead of an Action<T>.  Using this, we can parallelize the above operation by doing: Parallel.ForEach(customers, (customer, parallelLoopState) => { // Run some process that takes some time... DateTime lastContact = theStore.GetLastContact(customer); TimeSpan timeSinceContact = DateTime.Now - lastContact; // If it's been more than two weeks, send an email, and update... if (timeSinceContact.Days > 14) { // Exit gracefully if we fail to email, since this // entire process can be repeated later without issue. if (theStore.EmailCustomer(customer) == false) parallelLoopState.Break(); else customer.LastEmailContact = DateTime.Now; } }); There are a couple of important points here.  First, we didn’t actually instantiate the ParallelLoopState instance.  It was provided directly to us via the Parallel class.  All we needed to do was change our lambda expression to reflect that we want to use the loop state, and the Parallel class creates an instance for our use.  We also needed to change our logic slightly when we call Break().  Since Break() doesn’t stop the program flow within our block, we needed to add an else case to only set the property in customer when we succeeded.  This same technique can be used to break out of a Parallel.For loop. That being said, there is a huge difference between using ParallelLoopState to cause early termination and to use break in a standard iteration statement.  When dealing with a loop serially, break will immediately terminate the processing within the closest enclosing loop statement.  Calling ParallelLoopState.Break(), however, has a very different behavior. The issue is that, now, we’re no longer processing one element at a time.  If we break in one of our threads, there are other threads that will likely still be executing.  This leads to an important observation about termination of parallel code: Early termination in parallel routines is not immediate.  Code will continue to run after you request a termination. This may seem problematic at first, but it is something you just need to keep in mind while designing your routine.  ParallelLoopState.Break() should be thought of as a request.  We are telling the runtime that no elements that were in the collection past the element we’re currently processing need to be processed, and leaving it up to the runtime to decide how to handle this as gracefully as possible.  Although this may seem problematic at first, it is a good thing.  If the runtime tried to immediately stop processing, many of our elements would be partially processed.  It would be like putting a return statement in a random location throughout our loop body – which could have horrific consequences to our code’s maintainability. In order to understand and effectively write parallel routines, we, as developers, need a subtle, but profound shift in our thinking.  We can no longer think in terms of sequential processes, but rather need to think in terms of requests to the system that may be handled differently than we’d first expect.  This is more natural to developers who have dealt with asynchronous models previously, but is an important distinction when moving to concurrent programming models. As an example, I’ll discuss the Break() method.  ParallelLoopState.Break() functions in a way that may be unexpected at first.  When you call Break() from a loop body, the runtime will continue to process all elements of the collection that were found prior to the element that was being processed when the Break() method was called.  This is done to keep the behavior of the Break() method as close to the behavior of the break statement as possible. We can see the behavior in this simple code: var collection = Enumerable.Range(0, 20); var pResult = Parallel.ForEach(collection, (element, state) => { if (element > 10) { Console.WriteLine("Breaking on {0}", element); state.Break(); } Console.WriteLine(element); }); If we run this, we get a result that may seem unexpected at first: 0 2 1 5 6 3 4 10 Breaking on 11 11 Breaking on 12 12 9 Breaking on 13 13 7 8 Breaking on 15 15 What is occurring here is that we loop until we find the first element where the element is greater than 10.  In this case, this was found, the first time, when one of our threads reached element 11.  It requested that the loop stop by calling Break() at this point.  However, the loop continued processing until all of the elements less than 11 were completed, then terminated.  This means that it will guarantee that elements 9, 7, and 8 are completed before it stops processing.  You can see our other threads that were running each tried to break as well, but since Break() was called on the element with a value of 11, it decides which elements (0-10) must be processed. If this behavior is not desirable, there is another option.  Instead of calling ParallelLoopState.Break(), you can call ParallelLoopState.Stop().  The Stop() method requests that the runtime terminate as soon as possible , without guaranteeing that any other elements are processed.  Stop() will not stop the processing within an element, so elements already being processed will continue to be processed.  It will prevent new elements, even ones found earlier in the collection, from being processed.  Also, when Stop() is called, the ParallelLoopState’s IsStopped property will return true.  This lets longer running processes poll for this value, and return after performing any necessary cleanup. The basic rule of thumb for choosing between Break() and Stop() is the following. Use ParallelLoopState.Stop() when possible, since it terminates more quickly.  This is particularly useful in situations where you are searching for an element or a condition in the collection.  Once you’ve found it, you do not need to do any other processing, so Stop() is more appropriate. Use ParallelLoopState.Break() if you need to more closely match the behavior of the C# break statement. Both methods behave differently than our C# break statement.  Unfortunately, when parallelizing a routine, more thought and care needs to be put into every aspect of your routine than you may otherwise expect.  This is due to my second observation: Parallelizing a routine will almost always change its behavior. This sounds crazy at first, but it’s a concept that’s so simple its easy to forget.  We’re purposely telling the system to process more than one thing at the same time, which means that the sequence in which things get processed is no longer deterministic.  It is easy to change the behavior of your routine in very subtle ways by introducing parallelism.  Often, the changes are not avoidable, even if they don’t have any adverse side effects.  This leads to my final observation for this post: Parallelization is something that should be handled with care and forethought, added by design, and not just introduced casually.

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  • Parallelism in .NET – Part 7, Some Differences between PLINQ and LINQ to Objects

    - by Reed
    In my previous post on Declarative Data Parallelism, I mentioned that PLINQ extends LINQ to Objects to support parallel operations.  Although nearly all of the same operations are supported, there are some differences between PLINQ and LINQ to Objects.  By introducing Parallelism to our declarative model, we add some extra complexity.  This, in turn, adds some extra requirements that must be addressed. In order to illustrate the main differences, and why they exist, let’s begin by discussing some differences in how the two technologies operate, and look at the underlying types involved in LINQ to Objects and PLINQ . LINQ to Objects is mainly built upon a single class: Enumerable.  The Enumerable class is a static class that defines a large set of extension methods, nearly all of which work upon an IEnumerable<T>.  Many of these methods return a new IEnumerable<T>, allowing the methods to be chained together into a fluent style interface.  This is what allows us to write statements that chain together, and lead to the nice declarative programming model of LINQ: double min = collection .Where(item => item.SomeProperty > 6 && item.SomeProperty < 24) .Min(item => item.PerformComputation()); .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; } Other LINQ variants work in a similar fashion.  For example, most data-oriented LINQ providers are built upon an implementation of IQueryable<T>, which allows the database provider to turn a LINQ statement into an underlying SQL query, to be performed directly on the remote database. PLINQ is similar, but instead of being built upon the Enumerable class, most of PLINQ is built upon a new static class: ParallelEnumerable.  When using PLINQ, you typically begin with any collection which implements IEnumerable<T>, and convert it to a new type using an extension method defined on ParallelEnumerable: AsParallel().  This method takes any IEnumerable<T>, and converts it into a ParallelQuery<T>, the core class for PLINQ.  There is a similar ParallelQuery class for working with non-generic IEnumerable implementations. This brings us to our first subtle, but important difference between PLINQ and LINQ – PLINQ always works upon specific types, which must be explicitly created. Typically, the type you’ll use with PLINQ is ParallelQuery<T>, but it can sometimes be a ParallelQuery or an OrderedParallelQuery<T>.  Instead of dealing with an interface, implemented by an unknown class, we’re dealing with a specific class type.  This works seamlessly from a usage standpoint – ParallelQuery<T> implements IEnumerable<T>, so you can always “switch back” to an IEnumerable<T>.  The difference only arises at the beginning of our parallelization.  When we’re using LINQ, and we want to process a normal collection via PLINQ, we need to explicitly convert the collection into a ParallelQuery<T> by calling AsParallel().  There is an important consideration here – AsParallel() does not need to be called on your specific collection, but rather any IEnumerable<T>.  This allows you to place it anywhere in the chain of methods involved in a LINQ statement, not just at the beginning.  This can be useful if you have an operation which will not parallelize well or is not thread safe.  For example, the following is perfectly valid, and similar to our previous examples: double min = collection .AsParallel() .Select(item => item.SomeOperation()) .Where(item => item.SomeProperty > 6 && item.SomeProperty < 24) .Min(item => item.PerformComputation()); However, if SomeOperation() is not thread safe, we could just as easily do: double min = collection .Select(item => item.SomeOperation()) .AsParallel() .Where(item => item.SomeProperty > 6 && item.SomeProperty < 24) .Min(item => item.PerformComputation()); In this case, we’re using standard LINQ to Objects for the Select(…) method, then converting the results of that map routine to a ParallelQuery<T>, and processing our filter (the Where method) and our aggregation (the Min method) in parallel. PLINQ also provides us with a way to convert a ParallelQuery<T> back into a standard IEnumerable<T>, forcing sequential processing via standard LINQ to Objects.  If SomeOperation() was thread-safe, but PerformComputation() was not thread-safe, we would need to handle this by using the AsEnumerable() method: double min = collection .AsParallel() .Select(item => item.SomeOperation()) .Where(item => item.SomeProperty > 6 && item.SomeProperty < 24) .AsEnumerable() .Min(item => item.PerformComputation()); Here, we’re converting our collection into a ParallelQuery<T>, doing our map operation (the Select(…) method) and our filtering in parallel, then converting the collection back into a standard IEnumerable<T>, which causes our aggregation via Min() to be performed sequentially. This could also be written as two statements, as well, which would allow us to use the language integrated syntax for the first portion: var tempCollection = from item in collection.AsParallel() let e = item.SomeOperation() where (e.SomeProperty > 6 && e.SomeProperty < 24) select e; double min = tempCollection.AsEnumerable().Min(item => item.PerformComputation()); This allows us to use the standard LINQ style language integrated query syntax, but control whether it’s performed in parallel or serial by adding AsParallel() and AsEnumerable() appropriately. The second important difference between PLINQ and LINQ deals with order preservation.  PLINQ, by default, does not preserve the order of of source collection. This is by design.  In order to process a collection in parallel, the system needs to naturally deal with multiple elements at the same time.  Maintaining the original ordering of the sequence adds overhead, which is, in many cases, unnecessary.  Therefore, by default, the system is allowed to completely change the order of your sequence during processing.  If you are doing a standard query operation, this is usually not an issue.  However, there are times when keeping a specific ordering in place is important.  If this is required, you can explicitly request the ordering be preserved throughout all operations done on a ParallelQuery<T> by using the AsOrdered() extension method.  This will cause our sequence ordering to be preserved. For example, suppose we wanted to take a collection, perform an expensive operation which converts it to a new type, and display the first 100 elements.  In LINQ to Objects, our code might look something like: // Using IEnumerable<SourceClass> collection IEnumerable<ResultClass> results = collection .Select(e => e.CreateResult()) .Take(100); If we just converted this to a parallel query naively, like so: IEnumerable<ResultClass> results = collection .AsParallel() .Select(e => e.CreateResult()) .Take(100); We could very easily get a very different, and non-reproducable, set of results, since the ordering of elements in the input collection is not preserved.  To get the same results as our original query, we need to use: IEnumerable<ResultClass> results = collection .AsParallel() .AsOrdered() .Select(e => e.CreateResult()) .Take(100); This requests that PLINQ process our sequence in a way that verifies that our resulting collection is ordered as if it were processed serially.  This will cause our query to run slower, since there is overhead involved in maintaining the ordering.  However, in this case, it is required, since the ordering is required for correctness. PLINQ is incredibly useful.  It allows us to easily take nearly any LINQ to Objects query and run it in parallel, using the same methods and syntax we’ve used previously.  There are some important differences in operation that must be considered, however – it is not a free pass to parallelize everything.  When using PLINQ in order to parallelize your routines declaratively, the same guideline I mentioned before still applies: Parallelization is something that should be handled with care and forethought, added by design, and not just introduced casually.

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  • Parallelism in .NET – Part 9, Configuration in PLINQ and TPL

    - by Reed
    Parallel LINQ and the Task Parallel Library contain many options for configuration.  Although the default configuration options are often ideal, there are times when customizing the behavior is desirable.  Both frameworks provide full configuration support. When working with Data Parallelism, there is one primary configuration option we often need to control – the number of threads we want the system to use when parallelizing our routine.  By default, PLINQ and the TPL both use the ThreadPool to schedule tasks.  Given the major improvements in the ThreadPool in CLR 4, this default behavior is often ideal.  However, there are times that the default behavior is not appropriate.  For example, if you are working on multiple threads simultaneously, and want to schedule parallel operations from within both threads, you might want to consider restricting each parallel operation to using a subset of the processing cores of the system.  Not doing this might over-parallelize your routine, which leads to inefficiencies from having too many context switches. In the Task Parallel Library, configuration is handled via the ParallelOptions class.  All of the methods of the Parallel class have an overload which accepts a ParallelOptions argument. We configure the Parallel class by setting the ParallelOptions.MaxDegreeOfParallelism property.  For example, let’s revisit one of the simple data parallel examples from Part 2: Parallel.For(0, pixelData.GetUpperBound(0), row => { for (int col=0; col < pixelData.GetUpperBound(1); ++col) { pixelData[row, col] = AdjustContrast(pixelData[row, col], minPixel, maxPixel); } }); .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 looping through an image, and calling a method on each pixel in the image.  If this was being done on a separate thread, and we knew another thread within our system was going to be doing a similar operation, we likely would want to restrict this to using half of the cores on the system.  This could be accomplished easily by doing: var options = new ParallelOptions(); options.MaxDegreeOfParallelism = Math.Max(Environment.ProcessorCount / 2, 1); Parallel.For(0, pixelData.GetUpperBound(0), options, row => { for (int col=0; col < pixelData.GetUpperBound(1); ++col) { pixelData[row, col] = AdjustContrast(pixelData[row, col], minPixel, maxPixel); } }); Now, we’re restricting this routine to using no more than half the cores in our system.  Note that I included a check to prevent a single core system from supplying zero; without this check, we’d potentially cause an exception.  I also did not hard code a specific value for the MaxDegreeOfParallelism property.  One of our goals when parallelizing a routine is allowing it to scale on better hardware.  Specifying a hard-coded value would contradict that goal. Parallel LINQ also supports configuration, and in fact, has quite a few more options for configuring the system.  The main configuration option we most often need is the same as our TPL option: we need to supply the maximum number of processing threads.  In PLINQ, this is done via a new extension method on ParallelQuery<T>: ParallelEnumerable.WithDegreeOfParallelism. Let’s revisit our declarative data parallelism sample from Part 6: double min = collection.AsParallel().Min(item => item.PerformComputation()); Here, we’re performing a computation on each element in the collection, and saving the minimum value of this operation.  If we wanted to restrict this to a limited number of threads, we would add our new extension method: int maxThreads = Math.Max(Environment.ProcessorCount / 2, 1); double min = collection .AsParallel() .WithDegreeOfParallelism(maxThreads) .Min(item => item.PerformComputation()); This automatically restricts the PLINQ query to half of the threads on the system. PLINQ provides some additional configuration options.  By default, PLINQ will occasionally revert to processing a query in parallel.  This occurs because many queries, if parallelized, typically actually cause an overall slowdown compared to a serial processing equivalent.  By analyzing the “shape” of the query, PLINQ often decides to run a query serially instead of in parallel.  This can occur for (taken from MSDN): Queries that contain a Select, indexed Where, indexed SelectMany, or ElementAt clause after an ordering or filtering operator that has removed or rearranged original indices. Queries that contain a Take, TakeWhile, Skip, SkipWhile operator and where indices in the source sequence are not in the original order. Queries that contain Zip or SequenceEquals, unless one of the data sources has an originally ordered index and the other data source is indexable (i.e. an array or IList(T)). Queries that contain Concat, unless it is applied to indexable data sources. Queries that contain Reverse, unless applied to an indexable data source. If the specific query follows these rules, PLINQ will run the query on a single thread.  However, none of these rules look at the specific work being done in the delegates, only at the “shape” of the query.  There are cases where running in parallel may still be beneficial, even if the shape is one where it typically parallelizes poorly.  In these cases, you can override the default behavior by using the WithExecutionMode extension method.  This would be done like so: var reversed = collection .AsParallel() .WithExecutionMode(ParallelExecutionMode.ForceParallelism) .Select(i => i.PerformComputation()) .Reverse(); Here, the default behavior would be to not parallelize the query unless collection implemented IList<T>.  We can force this to run in parallel by adding the WithExecutionMode extension method in the method chain. Finally, PLINQ has the ability to configure how results are returned.  When a query is filtering or selecting an input collection, the results will need to be streamed back into a single IEnumerable<T> result.  For example, the method above returns a new, reversed collection.  In this case, the processing of the collection will be done in parallel, but the results need to be streamed back to the caller serially, so they can be enumerated on a single thread. This streaming introduces overhead.  IEnumerable<T> isn’t designed with thread safety in mind, so the system needs to handle merging the parallel processes back into a single stream, which introduces synchronization issues.  There are two extremes of how this could be accomplished, but both extremes have disadvantages. The system could watch each thread, and whenever a thread produces a result, take that result and send it back to the caller.  This would mean that the calling thread would have access to the data as soon as data is available, which is the benefit of this approach.  However, it also means that every item is introducing synchronization overhead, since each item needs to be merged individually. On the other extreme, the system could wait until all of the results from all of the threads were ready, then push all of the results back to the calling thread in one shot.  The advantage here is that the least amount of synchronization is added to the system, which means the query will, on a whole, run the fastest.  However, the calling thread will have to wait for all elements to be processed, so this could introduce a long delay between when a parallel query begins and when results are returned. The default behavior in PLINQ is actually between these two extremes.  By default, PLINQ maintains an internal buffer, and chooses an optimal buffer size to maintain.  Query results are accumulated into the buffer, then returned in the IEnumerable<T> result in chunks.  This provides reasonably fast access to the results, as well as good overall throughput, in most scenarios. However, if we know the nature of our algorithm, we may decide we would prefer one of the other extremes.  This can be done by using the WithMergeOptions extension method.  For example, if we know that our PerformComputation() routine is very slow, but also variable in runtime, we may want to retrieve results as they are available, with no bufferring.  This can be done by changing our above routine to: var reversed = collection .AsParallel() .WithExecutionMode(ParallelExecutionMode.ForceParallelism) .WithMergeOptions(ParallelMergeOptions.NotBuffered) .Select(i => i.PerformComputation()) .Reverse(); On the other hand, if are already on a background thread, and we want to allow the system to maximize its speed, we might want to allow the system to fully buffer the results: var reversed = collection .AsParallel() .WithExecutionMode(ParallelExecutionMode.ForceParallelism) .WithMergeOptions(ParallelMergeOptions.FullyBuffered) .Select(i => i.PerformComputation()) .Reverse(); Notice, also, that you can specify multiple configuration options in a parallel query.  By chaining these extension methods together, we generate a query that will always run in parallel, and will always complete before making the results available in our IEnumerable<T>.

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  • Parallelism in .NET – Part 12, More on Task Decomposition

    - by Reed
    Many tasks can be decomposed using a Data Decomposition approach, but often, this is not appropriate.  Frequently, decomposing the problem into distinctive tasks that must be performed is a more natural abstraction. However, as I mentioned in Part 1, Task Decomposition tends to be a bit more difficult than data decomposition, and can require a bit more effort.  Before we being parallelizing our algorithm based on the tasks being performed, we need to decompose our problem, and take special care of certain considerations such as ordering and grouping of tasks. Up to this point in this series, I’ve focused on parallelization techniques which are most appropriate when a problem space can be decomposed by data.  Using PLINQ and the Parallel class, I’ve shown how problem spaces where there is a collection of data, and each element needs to be processed, can potentially be parallelized. However, there are many other routines where this is not appropriate.  Often, instead of working on a collection of data, there is a single piece of data which must be processed using an algorithm or series of algorithms.  Here, there is no collection of data, but there may still be opportunities for parallelism. As I mentioned before, in cases like this, the approach is to look at your overall routine, and decompose your problem space based on tasks.  The idea here is to look for discrete “tasks,” individual pieces of work which can be conceptually thought of as a single operation. Let’s revisit the example I used in Part 1, an application startup path.  Say we want our program, at startup, to do a bunch of individual actions, or “tasks”.  The following is our list of duties we must perform right at startup: Display a splash screen Request a license from our license manager Check for an update to the software from our web server If an update is available, download it Setup our menu structure based on our current license Open and display our main, welcome Window Hide the splash screen The first step in Task Decomposition is breaking up the problem space into discrete tasks. This, naturally, can be abstracted as seven discrete tasks.  In the serial version of our program, if we were to diagram this, the general process would appear as: These tasks, obviously, provide some opportunities for parallelism.  Before we can parallelize this routine, we need to analyze these tasks, and find any dependencies between tasks.  In this case, our dependencies include: The splash screen must be displayed first, and as quickly as possible. We can’t download an update before we see whether one exists. Our menu structure depends on our license, so we must check for the license before setting up the menus. Since our welcome screen will notify the user of an update, we can’t show it until we’ve downloaded the update. Since our welcome screen includes menus that are customized based off the licensing, we can’t display it until we’ve received a license. We can’t hide the splash until our welcome screen is displayed. By listing our dependencies, we start to see the natural ordering that must occur for the tasks to be processed correctly. The second step in Task Decomposition is determining the dependencies between tasks, and ordering tasks based on their dependencies. Looking at these tasks, and looking at all the dependencies, we quickly see that even a simple decomposition such as this one can get quite complicated.  In order to simplify the problem of defining the dependencies, it’s often a useful practice to group our tasks into larger, discrete tasks.  The goal when grouping tasks is that you want to make each task “group” have as few dependencies as possible to other tasks or groups, and then work out the dependencies within that group.  Typically, this works best when any external dependency is based on the “last” task within the group when it’s ordered, although that is not a firm requirement.  This process is often called Grouping Tasks.  In our case, we can easily group together tasks, effectively turning this into four discrete task groups: 1. Show our splash screen – This needs to be left as its own task.  First, multiple things depend on this task, mainly because we want this to start before any other action, and start as quickly as possible. 2. Check for Update and Download the Update if it Exists - These two tasks logically group together.  We know we only download an update if the update exists, so that naturally follows.  This task has one dependency as an input, and other tasks only rely on the final task within this group. 3. Request a License, and then Setup the Menus – Here, we can group these two tasks together.  Although we mentioned that our welcome screen depends on the license returned, it also depends on setting up the menu, which is the final task here.  Setting up our menus cannot happen until after our license is requested.  By grouping these together, we further reduce our problem space. 4. Display welcome and hide splash - Finally, we can display our welcome window and hide our splash screen.  This task group depends on all three previous task groups – it cannot happen until all three of the previous groups have completed. By grouping the tasks together, we reduce our problem space, and can naturally see a pattern for how this process can be parallelized.  The diagram below shows one approach: The orange boxes show each task group, with each task represented within.  We can, now, effectively take these tasks, and run a large portion of this process in parallel, including the portions which may be the most time consuming.  We’ve now created two parallel paths which our process execution can follow, hopefully speeding up the application startup time dramatically. The main point to remember here is that, when decomposing your problem space by tasks, you need to: Define each discrete action as an individual Task Discover dependencies between your tasks Group tasks based on their dependencies Order the tasks and groups of tasks

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  • Parallelism in .NET – Part 4, Imperative Data Parallelism: Aggregation

    - by Reed
    In the article on simple data parallelism, I described how to perform an operation on an entire collection of elements in parallel.  Often, this is not adequate, as the parallel operation is going to be performing some form of aggregation. Simple examples of this might include taking the sum of the results of processing a function on each element in the collection, or finding the minimum of the collection given some criteria.  This can be done using the techniques described in simple data parallelism, however, special care needs to be taken into account to synchronize the shared data appropriately.  The Task Parallel Library has tools to assist in this synchronization. The main issue with aggregation when parallelizing a routine is that you need to handle synchronization of data.  Since multiple threads will need to write to a shared portion of data.  Suppose, for example, that we wanted to parallelize a simple loop that looked for the minimum value within a dataset: 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; } This seems like a good candidate for parallelization, but there is a problem here.  If we just wrap this into a call to Parallel.ForEach, we’ll introduce a critical race condition, and get the wrong answer.  Let’s look at what happens here: // Buggy code! Do not use! double min = double.MaxValue; Parallel.ForEach(collection, item => { double value = item.PerformComputation(); min = System.Math.Min(min, value); }); This code has a fatal flaw: min will be checked, then set, by multiple threads simultaneously.  Two threads may perform the check at the same time, and set the wrong value for min.  Say we get a value of 1 in thread 1, and a value of 2 in thread 2, and these two elements are the first two to run.  If both hit the min check line at the same time, both will determine that min should change, to 1 and 2 respectively.  If element 1 happens to set the variable first, then element 2 sets the min variable, we’ll detect a min value of 2 instead of 1.  This can lead to wrong answers. Unfortunately, fixing this, with the Parallel.ForEach call we’re using, would require adding locking.  We would need to rewrite this like: // Safe, but slow double min = double.MaxValue; // Make a "lock" object object syncObject = new object(); Parallel.ForEach(collection, item => { double value = item.PerformComputation(); lock(syncObject) min = System.Math.Min(min, value); }); This will potentially add a huge amount of overhead to our calculation.  Since we can potentially block while waiting on the lock for every single iteration, we will most likely slow this down to where it is actually quite a bit slower than our serial implementation.  The problem is the lock statement – any time you use lock(object), you’re almost assuring reduced performance in a parallel situation.  This leads to two observations I’ll make: When parallelizing a routine, try to avoid locks. That being said: Always add any and all required synchronization to avoid race conditions. These two observations tend to be opposing forces – we often need to synchronize our algorithms, but we also want to avoid the synchronization when possible.  Looking at our routine, there is no way to directly avoid this lock, since each element is potentially being run on a separate thread, and this lock is necessary in order for our routine to function correctly every time. However, this isn’t the only way to design this routine to implement this algorithm.  Realize that, although our collection may have thousands or even millions of elements, we have a limited number of Processing Elements (PE).  Processing Element is the standard term for a hardware element which can process and execute instructions.  This typically is a core in your processor, but many modern systems have multiple hardware execution threads per core.  The Task Parallel Library will not execute the work for each item in the collection as a separate work item. Instead, when Parallel.ForEach executes, it will partition the collection into larger “chunks” which get processed on different threads via the ThreadPool.  This helps reduce the threading overhead, and help the overall speed.  In general, the Parallel class will only use one thread per PE in the system. Given the fact that there are typically fewer threads than work items, we can rethink our algorithm design.  We can parallelize our algorithm more effectively by approaching it differently.  Because the basic aggregation we are doing here (Min) is communitive, we do not need to perform this in a given order.  We knew this to be true already – otherwise, we wouldn’t have been able to parallelize this routine in the first place.  With this in mind, we can treat each thread’s work independently, allowing each thread to serially process many elements with no locking, then, after all the threads are complete, “merge” together the results. This can be accomplished via a different set of overloads in the Parallel class: Parallel.ForEach<TSource,TLocal>.  The idea behind these overloads is to allow each thread to begin by initializing some local state (TLocal).  The thread will then process an entire set of items in the source collection, providing that state to the delegate which processes an individual item.  Finally, at the end, a separate delegate is run which allows you to handle merging that local state into your final results. To rewriting our routine using Parallel.ForEach<TSource,TLocal>, we need to provide three delegates instead of one.  The most basic version of this function is declared as: public static ParallelLoopResult ForEach<TSource, TLocal>( IEnumerable<TSource> source, Func<TLocal> localInit, Func<TSource, ParallelLoopState, TLocal, TLocal> body, Action<TLocal> localFinally ) The first delegate (the localInit argument) is defined as Func<TLocal>.  This delegate initializes our local state.  It should return some object we can use to track the results of a single thread’s operations. The second delegate (the body argument) is where our main processing occurs, although now, instead of being an Action<T>, we actually provide a Func<TSource, ParallelLoopState, TLocal, TLocal> delegate.  This delegate will receive three arguments: our original element from the collection (TSource), a ParallelLoopState which we can use for early termination, and the instance of our local state we created (TLocal).  It should do whatever processing you wish to occur per element, then return the value of the local state after processing is completed. The third delegate (the localFinally argument) is defined as Action<TLocal>.  This delegate is passed our local state after it’s been processed by all of the elements this thread will handle.  This is where you can merge your final results together.  This may require synchronization, but now, instead of synchronizing once per element (potentially millions of times), you’ll only have to synchronize once per thread, which is an ideal situation. Now that I’ve explained how this works, lets look at the code: // 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); } ); Although this is a bit more complicated than the previous version, it is now both thread-safe, and has minimal locking.  This same approach can be used by Parallel.For, although now, it’s Parallel.For<TLocal>.  When working with Parallel.For<TLocal>, you use the same triplet of delegates, with the same purpose and results. Also, many times, you can completely avoid locking by using a method of the Interlocked class to perform the final aggregation in an atomic operation.  The MSDN example demonstrating this same technique using Parallel.For uses the Interlocked class instead of a lock, since they are doing a sum operation on a long variable, which is possible via Interlocked.Add. By taking advantage of local state, we can use the Parallel class methods to parallelize algorithms such as aggregation, which, at first, may seem like poor candidates for parallelization.  Doing so requires careful consideration, and often requires a slight redesign of the algorithm, but the performance gains can be significant if handled in a way to avoid excessive synchronization.

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  • Should a c# dev switch to VB.net when the team language base is mixed?

    - by jjr2527
    I recently joined a new development team where the language preferences are mixed on the .net platform. Dev 1: Knows VB.net, does not know c# Dev 2: Knows VB.net, does not know c# Dev 3: Knows c# and VB.net, prefers c# Dev 4: Knows c# and VB6(VB.net should be pretty easy to pick up), prefers c# It seems to me that the thought leaders in the .net space are c# devs almost universally. I also thought that some 3rd party tools didn't support VB.net but when I started looking into it I didn't find any good examples. I would prefer to get the whole team on c# but if there isn't any good reason to force the issue aside from preference then I don't think that is the right choice. Are there any reasons I should lead folks away from VB.net?

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  • .NET Weak Event Handlers – Part II

    - by João Angelo
    On the first part of this article I showed two possible ways to create weak event handlers. One using reflection and the other using a delegate. For this performance analysis we will further differentiate between creating a delegate by providing the type of the listener at compile time (Explicit Delegate) vs creating the delegate with the type of the listener being only obtained at runtime (Implicit Delegate). As expected, the performance between reflection/delegate differ significantly. With the reflection based approach, creating a weak event handler is just storing a MethodInfo reference while with the delegate based approach there is the need to create the delegate which will be invoked later. So, at creating the weak event handler reflection clearly wins, but what about when the handler is invoked. No surprises there, performing a call through reflection every time a handler is invoked is costly. In conclusion, if you want good performance when creating handlers that only sporadically get triggered use reflection, otherwise use the delegate based approach. The explicit delegate approach always wins against the implicit delegate, but I find the syntax for the latter much more intuitive. // Implicit delegate - The listener type is inferred at runtime from the handler parameter public static EventHandler WrapInDelegateCall(EventHandler handler); public static EventHandler<TArgs> WrapInDelegateCall<TArgs>(EventHandler<TArgs> handler) where TArgs : EventArgs; // Explicite delegate - TListener is the type that defines the handler public static EventHandler WrapInDelegateCall<TListener>(EventHandler handler); public static EventHandler<TArgs> WrapInDelegateCall<TArgs, TListener>(EventHandler<TArgs> handler) where TArgs : EventArgs;

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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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  • Building a better .NET Application Configuration Class - revisited

    - by Rick Strahl
    Managing configuration settings is an important part of successful applications. It should be easy to ensure that you can easily access and modify configuration values within your applications. If it's not - well things don't get parameterized as much as they should. In this post I discuss a custom Application Configuration class that makes it super easy to create reusable configuration objects in your applications using a code-first approach and the ability to persist configuration information into various types of configuration stores.

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  • .NET Properties - Use Private Set or ReadOnly Property?

    - by tgxiii
    In what situation should I use a Private Set on a property versus making it a ReadOnly property? Take into consideration the two very simplistic examples below. First example: Public Class Person Private _name As String Public Property Name As String Get Return _name End Get Private Set(ByVal value As String) _name = value End Set End Property Public Sub WorkOnName() Dim txtInfo As TextInfo = _ Threading.Thread.CurrentThread.CurrentCulture.TextInfo Me.Name = txtInfo.ToTitleCase(Me.Name) End Sub End Class // ---------- public class Person { private string _name; public string Name { get { return _name; } private set { _name = value; } } public void WorkOnName() { TextInfo txtInfo = System.Threading.Thread.CurrentThread.CurrentCulture.TextInfo; this.Name = txtInfo.ToTitleCase(this.Name); } } Second example: Public Class AnotherPerson Private _name As String Public ReadOnly Property Name As String Get Return _name End Get End Property Public Sub WorkOnName() Dim txtInfo As TextInfo = _ Threading.Thread.CurrentThread.CurrentCulture.TextInfo _name = txtInfo.ToTitleCase(_name) End Sub End Class // --------------- public class AnotherPerson { private string _name; public string Name { get { return _name; } } public void WorkOnName() { TextInfo txtInfo = System.Threading.Thread.CurrentThread.CurrentCulture.TextInfo; _name = txtInfo.ToTitleCase(_name); } } They both yield the same results. Is this a situation where there's no right and wrong, and it's just a matter of preference?

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  • ASP.NET ViewState Tips and Tricks #1

    - by João Angelo
    In User Controls or Custom Controls DO NOT use ViewState to store non public properties. Persisting non public properties in ViewState results in loss of functionality if the Page hosting the controls has ViewState disabled since it can no longer reset values of non public properties on page load. Example: public class ExampleControl : WebControl { private const string PublicViewStateKey = "Example_Public"; private const string NonPublicViewStateKey = "Example_NonPublic"; // DO public int Public { get { object o = this.ViewState[PublicViewStateKey]; if (o == null) return default(int); return (int)o; } set { this.ViewState[PublicViewStateKey] = value; } } // DO NOT private int NonPublic { get { object o = this.ViewState[NonPublicViewStateKey]; if (o == null) return default(int); return (int)o; } set { this.ViewState[NonPublicViewStateKey] = value; } } } // Page with ViewState disabled public partial class ExamplePage : Page { protected override void OnLoad(EventArgs e) { base.OnLoad(e); this.Example.Public = 10; // Restore Public value this.Example.NonPublic = 20; // Compile Error! } }

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  • Hosting a web application on discountasp.net using sql ce 5

    - by David Stanley
    I am hoping that someone may have experience with this, since the discountasp site is very lacking in straightforward answers. I am building a lightweight web application and have decided to have sql ce as the database for it. Two questions regarding this: Do i need to get an actual database hosted as well as the site, in order for it to work? Do you know if discountasp supports the use of sql ce (not with webmatrix or any cms builds, completely custom)? If they don't, do you have any experience/recommendations with getting this done?

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  • Recommended solutions for integrating iOS with .NET, at the service tier

    - by George
    I'm developing an application, in iOS, that is required to connect to my Windows Server to poll for new data, update, etc. As a seasoned C# developer, my first instinct is to start a new project in Visual Studio and select Web Service, letting my bias (and comfort level) dictate the service layer of my application. However, I don't want to be biased, and I don't base my decision on a service which I am very familiar with, at the cost of performance. I would like to know what other developers have had success using, and if there is a default standard for iOS service layer development? Are there protocols that are easier to consume than others within iOS? Better ones for the size and/or compression of data? Is there anything wrong with using SOAP? I know it's "big" in comparison to protocols like JSON.

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  • Are there any advantages to using ASP.Net MVC 3 over Ruby On Rails for existing businesses? [closed]

    - by user786621
    Possible Duplicate: What ASP.NET MVC can do and Ruby on Rails can't? I've been hearing a lot of good press about Ruby On Rails but I'm having a hard time finding much information on the advantages of using ASP.Net MVC 3 over RoR, yet I see many existing businesses migrating over to ASP.Net MVC. Does ASP.Net MVC 3 have any advantages over Ruby On Rails for existing businesses such as possibly tying into old databases better or allowing for more complex business logic? Or is it most likely the case that they are transferring simply because they were already using ASP.Net for Winforms?

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  • MVC2 and MVC Futures causing RedirectToAction issues

    - by Darragh
    I've been trying to get the strongly typed version of RedirectToAction from the MVC Futures project to work, but I've been getting no where. Below are the steps I've followed, and the errors I've encountered. Any help is much appreciated. I created a new MVC2 app and changed the About action on the HomeController to redirect to the Index page. Return RedirectToAction("Index") However, I wanted to use the strongly typed extensions, so I downloaded the MVC Futures from CodePlex and added a reference to Microsoft.Web.Mvc to my project. I addded the following "import" statement to the top of HomeContoller.vb Imports Microsoft.Web.Mvc I commented out the above RedirectToAction and added the following line: Return RedirectToAction(Of HomeController)(Function(c) c.Index()) So far, so good. However, I noticed if I uncomment out the first (non Generic) RedirectToAction, it was now causing the following compile error: Error 1 Overload resolution failed because no accessible 'RedirectToAction' can be called with these arguments: Extension method 'Public Function RedirectToAction(Of TController)(action As System.Linq.Expressions.Expression(Of System.Action(Of TController))) As System.Web.Mvc.RedirectToRouteResult' defined in 'Microsoft.Web.Mvc.ControllerExtensions': Data type(s) of the type parameter(s) cannot be inferred from these arguments. Specifying the data type(s) explicitly might correct this error. Extension method 'Public Function RedirectToAction(action As System.Linq.Expressions.Expression(Of System.Action(Of HomeController))) As System.Web.Mvc.RedirectToRouteResult' defined in 'Microsoft.Web.Mvc.ControllerExtensions': Value of type 'String' cannot be converted to 'System.Linq.Expressions.Expression(Of System.Action(Of mvc2test1.HomeController))'. Even though intelli-sense was showing 8 overloads (the original 6 non-generic overloads, plus the 2 new generic overloads from the Futures assembly), it seems when trying to complie the code, the compiler would only 'find' the 2 non-gneneric extension methods from the Futures assessmbly. I thought this might be an issue that I was using conflicting versions of the MVC2 assembly, and the futures assembly, so I added MvcDiaganotics.aspx from the Futures download to my project and everytyhing looked correct: ASP.NET MVC Assembly Information (System.Web.Mvc.dll) Assembly version: ASP.NET MVC 2 RTM (2.0.50217.0) Full name: System.Web.Mvc, Version=2.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35 Code base: file:///C:/WINDOWS/assembly/GAC_MSIL/System.Web.Mvc/2.0.0.0__31bf3856ad364e35/System.Web.Mvc.dll Deployment: GAC-deployed ASP.NET MVC Futures Assembly Information (Microsoft.Web.Mvc.dll) Assembly version: ASP.NET MVC 2 RTM Futures (2.0.50217.0) Full name: Microsoft.Web.Mvc, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null Code base: file:///xxxx/bin/Microsoft.Web.Mvc.DLL Deployment: bin-deployed This is driving me crazy! Becuase I thought this might be some VB issue, I created a new MVC2 project using C# and tried the same as above. I added the following "using" statement to the top of HomeController.cs using Microsoft.Web.Mvc; This time, in the About action method, I could only manage to call the non-generic RedirectToAction by typing the full commmand as follows: return Microsoft.Web.Mvc.ControllerExtensions.RedirectToAction<HomeController>(this, c => c.Index()); Even though I had a "using" statement at the top of the class, if I tried to call the non-generic RedirectToAction as follows: return RedirectToAction<HomeController>(c => c.Index()); I would get the following compile error: Error 1 The non-generic method 'System.Web.Mvc.Controller.RedirectToAction(string)' cannot be used with type arguments What gives? It's not like I'm trying to do anything out of the ordinary. It's a simple vanilla MVC2 project with only a reference to the Futures assembly. I'm hoping that I've missed out something obvious, but I've been scratching my head for too long, so I figured I'd seek some assisstance. If anyone's managed to get this simple scenario working (in VB and/or C#) could they please let me know what, if anything, they did differently? Thanks!

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  • ASP.NET MVC Create dynamic navigation sub-menu on the master page

    - by Michael Narinsky
    I'm trying to create an ASP.NET MVC master page so the site navigation on it will look like this: Main Menu:Home | About | News Sub Menu: Home_Page1 | Home_Page2 The Sub Menu section should always show sub-menu for the currently selected Main Menu page (on the example above 'Home' page is selected) unless a user hovers the mouse on another Main Menu item (then it shows that item's sub-menu instead). What is the best way to get such functionality in ASP.NET MVC?

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  • Can we run MVC 2.0 on .Net 2.0

    - by Vinni
    Hello guys, I have an asp.net website which is already developed in .net 3.5, Now I asked to develop few pages in MVC 2.0 and few pages in DynamicData. Now Can I Run the MVC 2.0 and Dynamic Data in 3.5. When I run this i am getting lot of errors in web.config..

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  • SFTP with .net 3.5?

    - by nrk
    I need to connect to sftp server to download & upload file using C# in .net 3.5. Is Microsoft/.net 3.5 framework providing any inbuilt tools/mechanism/library to connect to sftp server to download & upload files?

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  • SFTP in C# with .net 3.5?

    - by nrk
    Hi, I need to connect to sftp server to download & upload file using C# in .net 3.5. Is Microsoft/.net 3.5 framework providing any inbuilt tools/mechanism/library to connect to sftp server to download & upload files? Thanks nrk

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  • Auto update the content in ASP.NET

    - by Zerotoinfinite
    I have to design a website where user can update their status, just like facebook and twitter and other social networking sites. Now my requirement is to refresh the feed with new user updates. Ex: when the new status comes facebook automatically add that on the top of the feed. on the other hand twitter shows the number of updates which is ready to be load. both ways are acceptable to me Now, I have to decide what is the best way to achieve this functionality. I am open to use ASP.NET. So I am confused that regular repeater control with timer and auto refresh or any other way? (I am wondering that if I set repeater for auto update and meanwhile if user is performing some action on any status it will lost). or do I need to change my framework from ASP.NET to ASP.NET MVC (I am little afraid with MVC as I have very less knowledge regarding it and I know it has a learning curve to master ajax/Jquery things) Any suggestion how I can I achieve it in a better and feasible way? EDIT1 I am not looking for a code but I want advice to achieve this. Supporting URL's would be appreciated. EDIT2 I am open to JQuery which can regularly check the database and fill the section. But my concern is this that if user is updating any comment and want to load/feed is automatically generated. his textbox text shouldn't be disappear (just like facebook, twitter or Linkedin) EDIT3 I have seen that on Stack overflow when any other user has modified the question/answer, I got notification like this question/answer is modified. and when I clicked on that notification only that section got reloaded. I am curious to know how to achieve this functionality. So that when user is commenting on a status/post and if meanwhile someone has updated the content then it would show the other user comment. Edit4 Could someone please recommend me an example of ASP.NET MVC 3+ which can do similar kind of activity (i.e. one input box and once user insert an text it will add the item in the list (with JQuery).

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  • Why use VB.Net instead C#?

    - by HasanGursoy
    A big company says "Minimal knowledge not to ask why don't you use C#" in its job requirements. And as a C# coder I wonder why do they prefer vb.net instead of C#. Also a Microsoft MVP uses vb.net in his Silverlight applications. Is there something Microsoft won't tell us?

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  • Why use VB.Net instead of C#?

    - by HasanGursoy
    A big company says "Minimal knowledge not to ask why don't you use C#" in its job requirements. And as a C# coder I wonder why do they prefer vb.net instead of C#. Also a Microsoft MVP uses vb.net in his Silverlight applications. Is there something Microsoft won't tell us?

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