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  • Parallelism in .NET – Part 14, The Different Forms of Task

    - by Reed
    Before discussing Task creation and actual usage in concurrent environments, I will briefly expand upon my introduction of the Task class and provide a short explanation of the distinct forms of Task.  The Task Parallel Library includes four distinct, though related, variations on the Task class. In my introduction to the Task class, I focused on the most basic version of Task.  This version of Task, the standard Task class, is most often used with an Action delegate.  This allows you to implement for each task within the task decomposition as a single delegate. Typically, when using the new threading constructs in .NET 4 and the Task Parallel Library, we use lambda expressions to define anonymous methods.  The advantage of using a lambda expression is that it allows the Action delegate to directly use variables in the calling scope.  This eliminates the need to make separate Task classes for Action<T>, Action<T1,T2>, and all of the other Action<…> delegate types.  As an example, suppose we wanted to make a Task to handle the ”Show Splash” task from our earlier decomposition.  Even if this task required parameters, such as a message to display, we could still use an Action delegate specified via a lambda: // Store this as a local variable string messageForSplashScreen = GetSplashScreenMessage(); // Create our task Task showSplashTask = new Task( () => { // We can use variables in our outer scope, // as well as methods scoped to our class! this.DisplaySplashScreen(messageForSplashScreen); }); .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 a huge amount of flexibility.  We can use this single form of task for any task which performs an operation, provided the only information we need to track is whether the task has completed successfully or not.  This leads to my first observation: Use a Task with a System.Action delegate for any task for which no result is generated. This observation leads to an obvious corollary: we also need a way to define a task which generates a result.  The Task Parallel Library provides this via the Task<TResult> class. Task<TResult> subclasses the standard Task class, providing one additional feature – the ability to return a value back to the user of the task.  This is done by switching from providing an Action delegate to providing a Func<TResult> delegate.  If we decompose our problem, and we realize we have one task where its result is required by a future operation, this can be handled via Task<TResult>.  For example, suppose we want to make a task for our “Check for Update” task, we could do: Task<bool> checkForUpdateTask = new Task<bool>( () => { return this.CheckWebsiteForUpdate(); }); Later, we would start this task, and perform some other work.  At any point in the future, we could get the value from the Task<TResult>.Result property, which will cause our thread to block until the task has finished processing: // This uses Task<bool> checkForUpdateTask generated above... // Start the task, typically on a background thread checkForUpdateTask.Start(); // Do some other work on our current thread this.DoSomeWork(); // Discover, from our background task, whether an update is available // This will block until our task completes bool updateAvailable = checkForUpdateTask.Result; This leads me to my second observation: Use a Task<TResult> with a System.Func<TResult> delegate for any task which generates a result. Task and Task<TResult> provide a much cleaner alternative to the previous Asynchronous Programming design patterns in the .NET framework.  Instead of trying to implement IAsyncResult, and providing BeginXXX() and EndXXX() methods, implementing an asynchronous programming API can be as simple as creating a method that returns a Task or Task<TResult>.  The client side of the pattern also is dramatically simplified – the client can call a method, then either choose to call task.Wait() or use task.Result when it needs to wait for the operation’s completion. While this provides a much cleaner model for future APIs, there is quite a bit of infrastructure built around the current Asynchronous Programming design patterns.  In order to provide a model to work with existing APIs, two other forms of Task exist.  There is a constructor for Task which takes an Action<Object> and a state parameter.  In addition, there is a constructor for creating a Task<TResult> which takes a Func<Object, TResult> as well as a state parameter.  When using these constructors, the state parameter is stored in the Task.AsyncState property. While these two overloads exist, and are usable directly, I strongly recommend avoiding this for new development.  The two forms of Task which take an object state parameter exist primarily for interoperability with traditional .NET Asynchronous Programming methodologies.  Using lambda expressions to capture variables from the scope of the creator is a much cleaner approach than using the untyped state parameters, since lambda expressions provide full type safety without introducing new variables.

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  • SQLAuthority News – SQL SERVER 2008 R2 Pricing

    - by pinaldave
    I was recently asked question about SQL Server 2008 pricing. I have bookmarked official site here which lists the pricing. Official site: What’s New in SQL Server 2008 R2 Editions Editions Per Processor PricingRetail Per Server Plus CAL PricingRetail Parallel Data Warehouse $57,498 Not offered via Server CAL Datacenter $57,498 Not offered via Server CAL Enterprise $28,749 $13,969 with 25 CALs Standard $7,499 $1,849 with 5 CALs However, I have [...]

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  • Regression testing with Selenium GRID

    - by Ben Adderson
    A lot of software teams out there are tasked with supporting and maintaining systems that have grown organically over time, and the web team here at Red Gate is no exception. We're about to embark on our first significant refactoring endeavour for some time, and as such its clearly paramount that the code be tested thoroughly for regressions. Unfortunately we currently find ourselves with a codebase that isn't very testable - the three layers (database, business logic and UI) are currently tightly coupled. This leaves us with the unfortunate problem that, in order to confidently refactor the code, we need unit tests. But in order to write unit tests, we need to refactor the code :S To try and ease the initial pain of decoupling these layers, I've been looking into the idea of using UI automation to provide a sort of system-level regression test suite. The idea being that these tests can help us identify regressions whilst we work towards a more testable codebase, at which point the more traditional combination of unit and integration tests can take over. Ending up with a strong battery of UI tests is also a nice bonus :) Following on from my previous posts (here, here and here) I knew I wanted to use Selenium. I also figured that this would be a good excuse to put my xUnit [Browser] attribute to good use. Pretty quickly, I had a raft of tests that looked like the following (this particular example uses Reflector Pro). In a nut shell the test traverses our shopping cart and, for a particular combination of number of users and months of support, checks that the price calculations all come up with the correct values. [BrowserTheory] [Browser(Browsers.Firefox3_6, "http://www.red-gate.com")] public void Purchase1UserLicenceNoSupport(SeleniumProvider seleniumProvider) {     //Arrange     _browser = seleniumProvider.GetBrowser();     _browser.Open("http://www.red-gate.com/dynamic/shoppingCart/ProductOption.aspx?Product=ReflectorPro");                  //Act     _browser = ShoppingCartHelpers.TraverseShoppingCart(_browser, 1, 0, ".NET Reflector Pro");     //Assert     var priceResult = PriceHelpers.GetNewPurchasePrice(db, "ReflectorPro", 1, 0, Currencies.Euros);         Assert.Equal(priceResult.Price, _browser.GetText("ctl00_content_InvoiceShoppingItemRepeater_ctl01_Price"));     Assert.Equal(priceResult.Tax, _browser.GetText("ctl00_content_InvoiceShoppingItemRepeater_ctl02_Tax"));     Assert.Equal(priceResult.Total, _browser.GetText("ctl00_content_InvoiceShoppingItemRepeater_ctl02_Total")); } These tests are pretty concise, with much of the common code in the TraverseShoppingCart() and GetNewPurchasePrice() methods. The (inevitable) problem arose when it came to execute these tests en masse. Selenium is a very slick tool, but it can't mask the fact that UI automation is very slow. To give you an idea, the set of cases that covers all of our products, for all combinations of users and support, came to 372 tests (for now only considering purchases in dollars). In the world of automated integration tests, that's a very manageable number. For unit tests, it's a trifle. However for UI automation, those 372 tests were taking just over two hours to run. Two hours may not sound like a lot, but those cases only cover one of the three currencies we deal with, and only one of the many different ways our systems can be asked to calculate a price. It was already pretty clear at this point that in order for this approach to be viable, I was going to have to find a way to speed things up. Up to this point I had been using Selenium Remote Control to automate Firefox, as this was the approach I had used previously and it had worked well. Fortunately,  the guys at SeleniumHQ also maintain a tool for executing multiple Selenium RC tests in parallel: Selenium Grid. Selenium Grid uses a central 'hub' to handle allocation of Selenium tests to individual RCs. The Remote Controls simply register themselves with the hub when they start, and then wait to be assigned work. The (for me) really clever part is that, as far as the client driver library is concerned, the grid hub looks exactly the same as a vanilla remote control. To create a new browser session against Selenium RC, the following C# code suffices: new DefaultSelenium("localhost", 4444, "*firefox", "http://www.red-gate.com"); This assumes that the RC is running on the local machine, and is listening on port 4444 (the default). Assuming the hub is running on your local machine, then to create a browser session in Selenium Grid, via the hub rather than directly against the control, the code is exactly the same! Behind the scenes, the hub will take this request and hand it off to one of the registered RCs that provides the "*firefox" execution environment. It will then pass all communications back and forth between the test runner and the remote control transparently. This makes running existing RC tests on a Selenium Grid a piece of cake, as the developers intended. For a more detailed description of exactly how Selenium Grid works, see this page. Once I had a test environment capable of running multiple tests in parallel, I needed a test runner capable of doing the same. Unfortunately, this does not currently exist for xUnit (boo!). MbUnit on the other hand, has the concept of concurrent execution baked right into the framework. So after swapping out my assembly references, and fixing up the resulting mismatches in assertions, my example test now looks like this: [Test] public void Purchase1UserLicenceNoSupport() {    //Arrange    ISelenium browser = BrowserHelpers.GetBrowser();    var db = DbHelpers.GetWebsiteDBDataContext();    browser.Start();    browser.Open("http://www.red-gate.com/dynamic/shoppingCart/ProductOption.aspx?Product=ReflectorPro");                 //Act     browser = ShoppingCartHelpers.TraverseShoppingCart(browser, 1, 0, ".NET Reflector Pro");    var priceResult = PriceHelpers.GetNewPurchasePrice(db, "ReflectorPro", 1, 0, Currencies.Euros);    //Assert     Assert.AreEqual(priceResult.Price, browser.GetText("ctl00_content_InvoiceShoppingItemRepeater_ctl01_Price"));     Assert.AreEqual(priceResult.Tax, browser.GetText("ctl00_content_InvoiceShoppingItemRepeater_ctl02_Tax"));     Assert.AreEqual(priceResult.Total, browser.GetText("ctl00_content_InvoiceShoppingItemRepeater_ctl02_Total")); } This is pretty much the same as the xUnit version. The exceptions are that the attributes have changed,  the //Arrange phase now has to handle setting up the ISelenium object, as the attribute that previously did this has gone away, and the test now sets up its own database connection. Previously I was using a shared database connection, but this approach becomes more complicated when tests are being executed concurrently. To avoid complexity each test has its own connection, which it is responsible for closing. For the sake of readability, I snipped out the code that closes the browser session and the db connection at the end of the test. With all that done, there was only one more step required before the tests would execute concurrently. It is necessary to tell the test runner which tests are eligible to run in parallel, via the [Parallelizable] attribute. This can be done at the test, fixture or assembly level. Since I wanted to run all tests concurrently, I marked mine at the assembly level in the AssemblyInfo.cs using the following: [assembly: DegreeOfParallelism(3)] [assembly: Parallelizable(TestScope.All)] The second attribute marks all tests in the assembly as [Parallelizable], whilst the first tells the test runner how many concurrent threads to use when executing the tests. I set mine to three since I was using 3 RCs in separate VMs. With everything now in place, I fired up the Icarus* test runner that comes with MbUnit. Executing my 372 tests three at a time instead of one at a time reduced the running time from 2 hours 10 minutes, to 55 minutes, that's an improvement of about 58%! I'd like to have seen an improvement of 66%, but I can understand that either inefficiencies in the hub code, my test environment or the test runner code (or some combination of all three most likely) contributes to a slightly diminished improvement. That said, I'd love to hear about any experience you have in upping this efficiency. Ultimately though, it was a saving that was most definitely worth having. It makes regression testing via UI automation a far more plausible prospect. The other obvious point to make is that this approach scales far better than executing tests serially. So if ever we need to improve performance, we just register additional RC's with the hub, and up the DegreeOfParallelism. *This was just my personal preference for a GUI runner. The MbUnit/Gallio installer also provides a command line runner, a TestDriven.net runner, and a Resharper 4.5 runner. For now at least, Resharper 5 isn't supported.

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  • Visual Studio 2010 released!

    - by Daniel Moth
    Visual Studio 2010 releases to the word today. Get the full story from Soma's blog post (inc. links for buy, try etc). Our team is very proud of what we have contributed to this release and you can learn more about it through our content on the Parallel Computing MSDN home. Comments about this post welcome at the original blog.

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  • HPC Server Dynamic Job Scheduling: when jobs spawn jobs

    - by JoshReuben
    HPC Job Types HPC has 3 types of jobs http://technet.microsoft.com/en-us/library/cc972750(v=ws.10).aspx · Task Flow – vanilla sequence · Parametric Sweep – concurrently run multiple instances of the same program, each with a different work unit input · MPI – message passing between master & slave tasks But when you try go outside the box – job tasks that spawn jobs, blocking the parent task – you run the risk of resource starvation, deadlocks, and recursive, non-converging or exponential blow-up. The solution to this is to write some performance monitoring and job scheduling code. You can do this in 2 ways: manually control scheduling - allocate/ de-allocate resources, change job priorities, pause & resume tasks , restrict long running tasks to specific compute clusters Semi-automatically - set threshold params for scheduling. How – Control Job Scheduling In order to manage the tasks and resources that are associated with a job, you will need to access the ISchedulerJob interface - http://msdn.microsoft.com/en-us/library/microsoft.hpc.scheduler.ischedulerjob_members(v=vs.85).aspx This really allows you to control how a job is run – you can access & tweak the following features: max / min resource values whether job resources can grow / shrink, and whether jobs can be pre-empted, whether the job is exclusive per node the creator process id & the job pool timestamp of job creation & completion job priority, hold time & run time limit Re-queue count Job progress Max/ min Number of cores, nodes, sockets, RAM Dynamic task list – can add / cancel jobs on the fly Job counters When – poll perf counters Tweaking the job scheduler should be done on the basis of resource utilization according to PerfMon counters – HPC exposes 2 Perf objects: Compute Clusters, Compute Nodes http://technet.microsoft.com/en-us/library/cc720058(v=ws.10).aspx You can monitor running jobs according to dynamic thresholds – use your own discretion: Percentage processor time Number of running jobs Number of running tasks Total number of processors Number of processors in use Number of processors idle Number of serial tasks Number of parallel tasks Design Your algorithms correctly Finally , don’t assume you have unlimited compute resources in your cluster – design your algorithms with the following factors in mind: · Branching factor - http://en.wikipedia.org/wiki/Branching_factor - dynamically optimize the number of children per node · cutoffs to prevent explosions - http://en.wikipedia.org/wiki/Limit_of_a_sequence - not all functions converge after n attempts. You also need a threshold of good enough, diminishing returns · heuristic shortcuts - http://en.wikipedia.org/wiki/Heuristic - sometimes an exhaustive search is impractical and short cuts are suitable · Pruning http://en.wikipedia.org/wiki/Pruning_(algorithm) – remove / de-prioritize unnecessary tree branches · avoid local minima / maxima - http://en.wikipedia.org/wiki/Local_minima - sometimes an algorithm cant converge because it gets stuck in a local saddle – try simulated annealing, hill climbing or genetic algorithms to get out of these ruts   watch out for rounding errors – http://en.wikipedia.org/wiki/Round-off_error - multiple iterations can in parallel can quickly amplify & blow up your algo ! Use an epsilon, avoid floating point errors,  truncations, approximations Happy Coding !

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  • Multi-Threaded Application vs. Single Threaded Application

    Why would we use a multi threaded application vs. a single threaded application? First we must define multithreading. Multithreading is a feature of an operating system that allows programs to run subcomponents or threads in parallel. Typically most applications only need to use one thread because they do not perform time consuming tasks. The use of multiple threads allows an application to distribute long running tasks so that they can be executed in parallel. This gives the user the perceived appearance that the application is working faster due to the fact that while one thread is waiting on an IO process the remaining tasks can make use of the available CPU. The allows working threads to execute in tandem so that they can be competed sooner. Multithreading Benefits Improved responsiveness — Users usually report improved responsiveness compared to single thread applications. Faster applications — Multiple threads can lead to improved application performance. Prioritization — Threads can be assigned a priority which would allow higher priority tasks to take precedence over lower priority tasks. Single Threading Benefits Programming and debugging —These activities are easier compared to multithreaded applications due to the reduced complexity Less Overhead — Threads add overhead to an application When developing multi-threaded applications, the following must be considered. Deadlocks occur when two threads hold a monitor that the other one requires. In essence each task is blocking the other and both tasks are waiting for the other monitor to be released. This forces an application to hang or deadlock. Resource allocation is used to prevent deadlocks because the system determines if approving the resource request will render the system in an unsafe state. An unsafe state could result in a deadlock. The system only approves requests that will lead to safe states. Thread Synchronization is used when multiple threads use the same instance of an object. The threads accessing the object can then be locked and then synchronized so that each task can interact with the static object on at a time.

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  • New DMV… not yet

    - by Michael Zilberstein
    Downloaded and installed new toy: And while reading BOL, stumbled upon new extremely useful DMV: sys.dm_exec_query_profiles . This DMV enables DBA to monitor query progress while it is being executed. Counters in the DMV are per operation per thread. So we’ll be able to monitor in real time which thread (even for parallel processing) processes which node in the plan. Or find heavy operations “post mortem”. We all know the uncomfortable feeling when some heavy query runs and the boss starts asking...(read more)

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  • MAXDOP in SQL Azure

    - by Herve Roggero
    In my search of better understanding the scalability options of SQL Azure I stumbled on an interesting aspect: Query Hints in SQL Azure. More specifically, the MAXDOP hint. A few years ago I did a lot of analysis on this query hint (see article on SQL Server Central:  http://www.sqlservercentral.com/articles/Configuring/managingmaxdegreeofparallelism/1029/).  Here is a quick synopsis of MAXDOP: It is a query hint you use when issuing a SQL statement that provides you control with how many processors SQL Server will use to execute the query. For complex queries with lots of I/O requirements, more CPUs can mean faster parallel searches. However the impact can be drastic on other running threads/processes. If your query takes all available processors at 100% for 5 minutes... guess what... nothing else works. The bottom line is that more is not always better. The use of MAXDOP is more art than science... and a whole lot of testing; it depends on two things: the underlying hardware architecture and the application design. So there isn't a magic number that will work for everyone... except 1... :) Let me explain. The rules of engagements are different. SQL Azure is about sharing. Yep... you are forced to nice with your neighbors.  To achieve this goal SQL Azure sets the MAXDOP to 1 by default, and ignores the use of the MAXDOP hint altogether. That means that all you queries will use one and only one processor.  It really isn't such a bad thing however. Keep in mind that in some of the largest SQL Server implementations MAXDOP is usually also set to 1. It is a well known configuration setting for large scale implementations. The reason is precisely to prevent rogue statements (like a SELECT * FROM HISTORY) from bringing down your systems (like a report that should have been running on a different in the first place) and to avoid the overhead generated by executing too many parallel queries that could cause internal memory management nightmares to the host Operating System. Is summary, forcing the MAXDOP to 1 in SQL Azure makes sense; it ensures that your database will continue to function normally even if one of the other tenants on the same server is running massive queries that would otherwise bring you down. Last but not least, keep in mind as well that when you test your database code for performance on-premise, make sure to set the DOP to 1 on your SQL Server databases to simulate SQL Azure conditions.

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  • SQLAuthority News – Presented at Bangalore DevCon August 4, 2012

    - by pinaldave
    Bangalore Devcon 2012 was a great fun. Earlier this month I was fortunate to be invited to present at Dev Con. The event was very well planned and had excellent response. There were more than 140 attendees at any time in the sessions. There were two tracks and both tracks were running parallel to each other in the Microsoft Bangalore building. The venue is fantastic and the enthusiasm of the community is impeccable. We had a total of 12 sessions during the day. I had decided to attend each session if I can. We have so many fantastic speakers and I did not want to miss any of the sessions. As sessions were running parallel, I attended every session for 30 minutes and switched to another session. I had fun doing this experiment as tit gave me a good idea about every session. I presented personally on the session of SQL Tips and Tricks for Web Developer. DBA is a very common word and every time when we say SQL Server – lots of people think of DBA in their mind, however, SQL Server is used by many developers as well. In this session I tried to cover a few of the simple concepts where developers must pay special attention while writing T-SQL code. Sometimes a very small mistake can be very fatal on performance in the future. Here are few of the photos of the event. Btw, the two sessions which clearly stand out were Vinod Kumar‘s session on Leadership and Lohith‘s session on Visual Studio Tips and Tricks. Additional Read: Following are the blog posts by community on the Bangalore DevCon Experience. I encourage you to read them all and leave a comment which one you liked the most. http://abhishekbhat.wordpress.com/2012/08/07/devcon-2012-experience/ http://praveenprajapati.wordpress.com/2012/08/07/devcon-2012-part-2/ http://tomsblogsspot.blogspot.in/2012/08/devcon-2012.html?view=classic https://manasdash.wordpress.com/2012/08/06/devcon-2012-by-bdotnet-4th-august-2012/ http://www.jagan-bhathri.com/2012/08/bangalore-developer-conference-2012-by.html Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority Author Visit, SQLAuthority News, T SQL, Technology

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  • SPARC T4-4 Beats 8-CPU IBM POWER7 on TPC-H @3000GB Benchmark

    - by Brian
    Oracle's SPARC T4-4 server delivered a world record TPC-H @3000GB benchmark result for systems with four processors. This result beats eight processor results from IBM (POWER7) and HP (x86). The SPARC T4-4 server also delivered better performance per core than these eight processor systems from IBM and HP. Comparisons below are based upon system to system comparisons, highlighting Oracle's complete software and hardware solution. This database world record result used Oracle's Sun Storage 2540-M2 arrays (rotating disk) connected to a SPARC T4-4 server running Oracle Solaris 11 and Oracle Database 11g Release 2 demonstrating the power of Oracle's integrated hardware and software solution. The SPARC T4-4 server based configuration achieved a TPC-H scale factor 3000 world record for four processor systems of 205,792 QphH@3000GB with price/performance of $4.10/QphH@3000GB. The SPARC T4-4 server with four SPARC T4 processors (total of 32 cores) is 7% faster than the IBM Power 780 server with eight POWER7 processors (total of 32 cores) on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 36% better in price performance compared to the IBM Power 780 server on the TPC-H @3000GB Benchmark. The SPARC T4-4 server is 29% faster than the IBM Power 780 for data loading. The SPARC T4-4 server is up to 3.4 times faster than the IBM Power 780 server for the Refresh Function. The SPARC T4-4 server with four SPARC T4 processors is 27% faster than the HP ProLiant DL980 G7 server with eight x86 processors on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 52% faster than the HP ProLiant DL980 G7 server for data loading. The SPARC T4-4 server is up to 3.2 times faster than the HP ProLiant DL980 G7 for the Refresh Function. The SPARC T4-4 server achieved a peak IO rate from the Oracle database of 17 GB/sec. This rate was independent of the storage used, as demonstrated by the TPC-H @3000TB benchmark which used twelve Sun Storage 2540-M2 arrays (rotating disk) and the TPC-H @1000TB benchmark which used four Sun Storage F5100 Flash Array devices (flash storage). [*] The SPARC T4-4 server showed linear scaling from TPC-H @1000GB to TPC-H @3000GB. This demonstrates that the SPARC T4-4 server can handle the increasingly larger databases required of DSS systems. [*] The SPARC T4-4 server benchmark results demonstrate a complete solution of building Decision Support Systems including data loading, business questions and refreshing data. Each phase usually has a time constraint and the SPARC T4-4 server shows superior performance during each phase. [*] The TPC believes that comparisons of results published with different scale factors are misleading and discourages such comparisons. Performance Landscape The table lists the leading TPC-H @3000GB results for non-clustered systems. TPC-H @3000GB, Non-Clustered Systems System Processor P/C/T – Memory Composite(QphH) $/perf($/QphH) Power(QppH) Throughput(QthH) Database Available SPARC Enterprise M9000 3.0 GHz SPARC64 VII+ 64/256/256 – 1024 GB 386,478.3 $18.19 316,835.8 471,428.6 Oracle 11g R2 09/22/11 SPARC T4-4 3.0 GHz SPARC T4 4/32/256 – 1024 GB 205,792.0 $4.10 190,325.1 222,515.9 Oracle 11g R2 05/31/12 SPARC Enterprise M9000 2.88 GHz SPARC64 VII 32/128/256 – 512 GB 198,907.5 $15.27 182,350.7 216,967.7 Oracle 11g R2 12/09/10 IBM Power 780 4.1 GHz POWER7 8/32/128 – 1024 GB 192,001.1 $6.37 210,368.4 175,237.4 Sybase 15.4 11/30/11 HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 8/64/128 – 512 GB 162,601.7 $2.68 185,297.7 142,685.6 SQL Server 2008 10/13/10 P/C/T = Processors, Cores, Threads QphH = the Composite Metric (bigger is better) $/QphH = the Price/Performance metric in USD (smaller is better) QppH = the Power Numerical Quantity QthH = the Throughput Numerical Quantity The following table lists data load times and refresh function times during the power run. TPC-H @3000GB, Non-Clustered Systems Database Load & Database Refresh System Processor Data Loading(h:m:s) T4Advan RF1(sec) T4Advan RF2(sec) T4Advan SPARC T4-4 3.0 GHz SPARC T4 04:08:29 1.0x 67.1 1.0x 39.5 1.0x IBM Power 780 4.1 GHz POWER7 05:51:50 1.5x 147.3 2.2x 133.2 3.4x HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 08:35:17 2.1x 173.0 2.6x 126.3 3.2x Data Loading = database load time RF1 = power test first refresh transaction RF2 = power test second refresh transaction T4 Advan = the ratio of time to T4 time Complete benchmark results found at the TPC benchmark website http://www.tpc.org. Configuration Summary and Results Hardware Configuration: SPARC T4-4 server 4 x SPARC T4 3.0 GHz processors (total of 32 cores, 128 threads) 1024 GB memory 8 x internal SAS (8 x 300 GB) disk drives External Storage: 12 x Sun Storage 2540-M2 array storage, each with 12 x 15K RPM 300 GB drives, 2 controllers, 2 GB cache Software Configuration: Oracle Solaris 11 11/11 Oracle Database 11g Release 2 Enterprise Edition Audited Results: Database Size: 3000 GB (Scale Factor 3000) TPC-H Composite: 205,792.0 QphH@3000GB Price/performance: $4.10/QphH@3000GB Available: 05/31/2012 Total 3 year Cost: $843,656 TPC-H Power: 190,325.1 TPC-H Throughput: 222,515.9 Database Load Time: 4:08:29 Benchmark Description The TPC-H benchmark is a performance benchmark established by the Transaction Processing Council (TPC) to demonstrate Data Warehousing/Decision Support Systems (DSS). TPC-H measurements are produced for customers to evaluate the performance of various DSS systems. These queries and updates are executed against a standard database under controlled conditions. Performance projections and comparisons between different TPC-H Database sizes (100GB, 300GB, 1000GB, 3000GB, 10000GB, 30000GB and 100000GB) are not allowed by the TPC. TPC-H is a data warehousing-oriented, non-industry-specific benchmark that consists of a large number of complex queries typical of decision support applications. It also includes some insert and delete activity that is intended to simulate loading and purging data from a warehouse. TPC-H measures the combined performance of a particular database manager on a specific computer system. The main performance metric reported by TPC-H is called the TPC-H Composite Query-per-Hour Performance Metric (QphH@SF, where SF is the number of GB of raw data, referred to as the scale factor). QphH@SF is intended to summarize the ability of the system to process queries in both single and multiple user modes. The benchmark requires reporting of price/performance, which is the ratio of the total HW/SW cost plus 3 years maintenance to the QphH. A secondary metric is the storage efficiency, which is the ratio of total configured disk space in GB to the scale factor. Key Points and Best Practices Twelve Sun Storage 2540-M2 arrays were used for the benchmark. Each Sun Storage 2540-M2 array contains 12 15K RPM drives and is connected to a single dual port 8Gb FC HBA using 2 ports. Each Sun Storage 2540-M2 array showed 1.5 GB/sec for sequential read operations and showed linear scaling, achieving 18 GB/sec with twelve Sun Storage 2540-M2 arrays. These were stand alone IO tests. The peak IO rate measured from the Oracle database was 17 GB/sec. Oracle Solaris 11 11/11 required very little system tuning. Some vendors try to make the point that storage ratios are of customer concern. However, storage ratio size has more to do with disk layout and the increasing capacities of disks – so this is not an important metric in which to compare systems. The SPARC T4-4 server and Oracle Solaris efficiently managed the system load of over one thousand Oracle Database parallel processes. Six Sun Storage 2540-M2 arrays were mirrored to another six Sun Storage 2540-M2 arrays on which all of the Oracle database files were placed. IO performance was high and balanced across all the arrays. The TPC-H Refresh Function (RF) simulates periodical refresh portion of Data Warehouse by adding new sales and deleting old sales data. Parallel DML (parallel insert and delete in this case) and database log performance are a key for this function and the SPARC T4-4 server outperformed both the IBM POWER7 server and HP ProLiant DL980 G7 server. (See the RF columns above.) See Also Transaction Processing Performance Council (TPC) Home Page Ideas International Benchmark Page SPARC T4-4 Server oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Sun Storage 2540-M2 Array oracle.com OTN Disclosure Statement TPC-H, QphH, $/QphH are trademarks of Transaction Processing Performance Council (TPC). For more information, see www.tpc.org. SPARC T4-4 205,792.0 QphH@3000GB, $4.10/QphH@3000GB, available 5/31/12, 4 processors, 32 cores, 256 threads; IBM Power 780 QphH@3000GB, 192,001.1 QphH@3000GB, $6.37/QphH@3000GB, available 11/30/11, 8 processors, 32 cores, 128 threads; HP ProLiant DL980 G7 162,601.7 QphH@3000GB, $2.68/QphH@3000GB available 10/13/10, 8 processors, 64 cores, 128 threads.

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  • Plan Operator Tuesday round-up

    - by Rob Farley
    Eighteen posts for T-SQL Tuesday #43 this month, discussing Plan Operators. I put them together and made the following clickable plan. It’s 1000px wide, so I hope you have a monitor wide enough. Let me explain this plan for you (people’s names are the links to the articles on their blogs – the same links as in the plan above). It was clearly a SELECT statement. Wayne Sheffield (@dbawayne) wrote about that, so we start with a SELECT physical operator, leveraging the logical operator Wayne Sheffield. The SELECT operator calls the Paul White operator, discussed by Jason Brimhall (@sqlrnnr) in his post. The Paul White operator is quite remarkable, and can consume three streams of data. Let’s look at those streams. The first pulls data from a Table Scan – Boris Hristov (@borishristov)’s post – using parallel threads (Bradley Ball – @sqlballs) that pull the data eagerly through a Table Spool (Oliver Asmus – @oliverasmus). A scalar operation is also performed on it, thanks to Jeffrey Verheul (@devjef)’s Compute Scalar operator. The second stream of data applies Evil (I figured that must mean a procedural TVF, but could’ve been anything), courtesy of Jason Strate (@stratesql). It performs this Evil on the merging of parallel streams (Steve Jones – @way0utwest), which suck data out of a Switch (Paul White – @sql_kiwi). This Switch operator is consuming data from up to four lookups, thanks to Kalen Delaney (@sqlqueen), Rick Krueger (@dataogre), Mickey Stuewe (@sqlmickey) and Kathi Kellenberger (@auntkathi). Unfortunately Kathi’s name is a bit long and has been truncated, just like in real plans. The last stream performs a join of two others via a Nested Loop (Matan Yungman – @matanyungman). One pulls data from a Spool (my post – @rob_farley) populated from a Table Scan (Jon Morisi). The other applies a catchall operator (the catchall is because Tamera Clark (@tameraclark) didn’t specify any particular operator, and a catchall is what gets shown when SSMS doesn’t know what to show. Surprisingly, it’s showing the yellow one, which is about cursors. Hopefully that’s not what Tamera planned, but anyway...) to the output from an Index Seek operator (Sebastian Meine – @sqlity). Lastly, I think everyone put in 110% effort, so that’s what all the operators cost. That didn’t leave anything for me, unfortunately, but that’s okay. Also, because he decided to use the Paul White operator, Jason Brimhall gets 0%, and his 110% was given to Paul’s Switch operator post. I hope you’ve enjoyed this T-SQL Tuesday, and have learned something extra about Plan Operators. Keep your eye out for next month’s one by watching the Twitter Hashtag #tsql2sday, and why not contribute a post to the party? Big thanks to Adam Machanic as usual for starting all this. @rob_farley

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  • Slow Chat with Industry Experts: Developing Multithreaded Applications

    Sponsored by Intel Join the experts who created The Intel Guide for Developing Multithreaded Applications for a slow chat about multithreaded application development. Bring your questions about application threading, memory management, synchronization, programming tools and more and get answers from the parallel programming experts. Post your questions here

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  • Slow Chat with Industry Experts: Developing Multithreaded Applications

    Sponsored by Intel Join the experts who created The Intel Guide for Developing Multithreaded Applications for a slow chat about multithreaded application development. Bring your questions about application threading, memory management, synchronization, programming tools and more and get answers from the parallel programming experts. Post your questions here

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  • SSAS Native v .net Provider

    - by ACALVETT
    Recently I was investigating why a new server which is in its parallel running phase was taking significantly longer to process the daily data than the server its due to replace. The server has SQL & SSAS installed so the problem was not likely to be in the network transfer as its using shared memory. As i dug around the SQL dmv’s i noticed in sys.dm_exec_connections that the SSAS connection had a packet size of 8000 bytes instead of the usual 4096 bytes and from there i found that the datasource...(read more)

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  • Innovation and the Role of Social Media

    - by Brian Dirking
    A very interesting post by Andy Mulholland of CAP Gemini this week – “The CIO is trapped between the CEO wanting innovation and the CFO needing compliance” – had many interesting points: “A successful move in one area won’t be recognized and rapidly implemented in other areas to multiply the benefits, or worse unsuccessful ideas will get repeated adding to the cost and time wasted. That’s where the need to really address the combination of social networking, collaboration, knowledge management and business information is required.” Without communicating what works and what doesn’t, the innovations of our organization may be lost, and the failures repeated. That makes sense. If you liked Andy Mulholland’s blog post, you need to hear Howard Beader’s presentation at Enterprise 2.0 Conference on innovation and the role of social media. (Howard will be speaking in the Market Leaders Session at 1 PM on Wednesday June 22nd). Some of the thoughts Howard will share include: • Innovation is more than just ideas, it’s getting ideas to market, and removing the obstacles that stand in the way • Innovation is about parallel processing – you can’t remove the obstacles one by one because you will get to market too late • Innovation can be about product innovation, but it can also be about process innovation This brings us to Andy’s second issue he raises: "..the need for integration with, and visibility of, processes to understand exactly how the enterprise functions and delivers on its policies…" Andy goes on to talk about this from the perspective of compliance and the CFO’s concerns. And it’s true: innovation can come both in product innovation, but also internal process innovation. And process innovation can have as much impact as product innovation.  New supply chain models can disrupt an industry overnight. Many people ignore process innovation as a benefit of social business, because it is perceived as a bottom line rather than top line impact. But it can actually impact your top line by changing your entire business model. Oracle WebCenter sits at this crossroads between product innovation and process innovation, enabling you to drive go-to-market innovations through internal social media tools, removing obstacles in parallel, and also providing you deep insight into your processes so you can identify bottlenecks and realize whole new ways of doing business. Learn more about how at the Enterprise 2.0 Conference, where Oracle will be in booth #213 showing Oracle WebCenter and Oracle Fusion Applications.

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  • Code for Parallelism Features Tour

    Last year I linked to a screencast that shows off many VS2010 features delivered by the Parallel Computing team.There have been requests for the code used to demonstrate the features. Like with all my screencasts, you can see all the code in action, so you could simply type it in. To save you doing that though, you may download the two files with the demo code here: MM.cs and Program.cs. HTH. Comments about this post welcome at the original blog.

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  • Files for .NET Montreal and VTCC4 conference

    - by Vincent Grondin
    Hi,  here are the files for both the .NET Montreal presentation made Sept the 24th and at the Vermont Code Camp #4 on Sept the 22nd regarding Architecture problems and solutions linked to EF4.0, Async-await keywords and the Task Parallel Library. This zip file includes both power points in french and english and the DemoApplication which is I REMIND YOU VERY DEMO-WARE and doesn't handle task level exception and context switching.  ZipFile Enjoy

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  • How Parallelism Works in SQL Server

    - by Paul White
    You might have noticed that January was a quiet blogging month for me.  Part of the reason was that I was working on a series of articles for Simple Talk, examining how parallel query execution really works.  The first part is published today at: http://www.simple-talk.com/sql/learn-sql-server/understanding-and-using-parallelism-in-sql-server/ . This introductory piece is not quite as deeply technical as my SQLblog posts tend to be, but I hope there be enough interesting material to make...(read more)

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  • Visual Studio 2010 and .NET Framework 4 Training Kit April 2010 Release

    - by Harish Pavithran
    The Visual Studio 2010 and .NET Framework 4 Training Kit includes presentations, hands-on labs, and demos. This content is designed to help you learn how to utilize the Visual Studio 2010 features and a variety of framework technologies including: C# 4 Visual Basic 10 F# Parallel Extensions Windows Communication Foundation Windows Workflow Windows Presentation Foundation ASP.NET 4 Windows 7 Entity Framework ADO.NET Data Services Managed Extensibility Framework Visual Studio Team System This version of the Training Kit works with Visual Studio 2010 and .NET Framework 4.  Here is the link enjoy www.microsoft.com/downloads/details.aspx

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  • Téléchargez gratuitement l'ebook sur le développement d'applications 'Threaded' qui utilisent le har

    Téléchargez gratuitement l'ebook sur le développement d'applications ?Threaded' Les logiciels de développement Intel® Parallel Studio accélèrent le développement d'applications ?Threaded' qui utilisent le hardware des utilisateurs finaux, depuis le ?'supercomputer'' jusqu'à l'ordinateur portable ou les mobiles. Optimisez la performance de votre application sur architecture Intel® et obtenez plus des derniers processeurs multi-coeurs d'Intel®. Depuis la manière dont les produits fonctionnent ensemble jusqu'à leurs jeux de fonctionnalités uniques, le Threading est maintenant plus facile et plus viable que jamais. Les outils sont optimisés donc les novices peuvent facilement se former et les développeurs expérimentés peuvent aisément ...

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  • Téléchargez gratuitement l'ebook sur le développement d'applications 'Threaded' qui utilisent le har

    Téléchargez gratuitement l'ebook sur le développement d'applications ?Threaded' Les logiciels de développement Intel® Parallel Studio accélèrent le développement d'applications ?Threaded' qui utilisent le hardware des utilisateurs finaux, depuis le ?'supercomputer'' jusqu'à l'ordinateur portable ou les mobiles. Optimisez la performance de votre application sur architecture Intel® et obtenez plus des derniers processeurs multi-coeurs d'Intel®. Depuis la manière dont les produits fonctionnent ensemble jusqu'à leurs jeux de fonctionnalités uniques, le Threading est maintenant plus facile et plus viable que jamais. Les outils sont optimisés donc les novices peuvent facilement se former et les développeurs expérimentés peuvent aisément ...

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  • Windows Azure Recipe: Big Data

    - by Clint Edmonson
    As the name implies, what we’re talking about here is the explosion of electronic data that comes from huge volumes of transactions, devices, and sensors being captured by businesses today. This data often comes in unstructured formats and/or too fast for us to effectively process in real time. Collectively, we call these the 4 big data V’s: Volume, Velocity, Variety, and Variability. These qualities make this type of data best managed by NoSQL systems like Hadoop, rather than by conventional Relational Database Management System (RDBMS). We know that there are patterns hidden inside this data that might provide competitive insight into market trends.  The key is knowing when and how to leverage these “No SQL” tools combined with traditional business such as SQL-based relational databases and warehouses and other business intelligence tools. Drivers Petabyte scale data collection and storage Business intelligence and insight Solution The sketch below shows one of many big data solutions using Hadoop’s unique highly scalable storage and parallel processing capabilities combined with Microsoft Office’s Business Intelligence Components to access the data in the cluster. Ingredients Hadoop – this big data industry heavyweight provides both large scale data storage infrastructure and a highly parallelized map-reduce processing engine to crunch through the data efficiently. Here are the key pieces of the environment: Pig - a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. Mahout - a machine learning library with algorithms for clustering, classification and batch based collaborative filtering that are implemented on top of Apache Hadoop using the map/reduce paradigm. Hive - data warehouse software built on top of Apache Hadoop that facilitates querying and managing large datasets residing in distributed storage. Directly accessible to Microsoft Office and other consumers via add-ins and the Hive ODBC data driver. Pegasus - a Peta-scale graph mining system that runs in parallel, distributed manner on top of Hadoop and that provides algorithms for important graph mining tasks such as Degree, PageRank, Random Walk with Restart (RWR), Radius, and Connected Components. Sqoop - a tool designed for efficiently transferring bulk data between Apache Hadoop and structured data stores such as relational databases. Flume - a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large log data amounts to HDFS. Database – directly accessible to Hadoop via the Sqoop based Microsoft SQL Server Connector for Apache Hadoop, data can be efficiently transferred to traditional relational data stores for replication, reporting, or other needs. Reporting – provides easily consumable reporting when combined with a database being fed from the Hadoop environment. Training These links point to online Windows Azure training labs where you can learn more about the individual ingredients described above. Hadoop Learning Resources (20+ tutorials and labs) Huge collection of resources for learning about all aspects of Apache Hadoop-based development on Windows Azure and the Hadoop and Windows Azure Ecosystems SQL Azure (7 labs) Microsoft SQL Azure delivers on the Microsoft Data Platform vision of extending the SQL Server capabilities to the cloud as web-based services, enabling you to store structured, semi-structured, and unstructured data. See my Windows Azure Resource Guide for more guidance on how to get started, including links web portals, training kits, samples, and blogs related to Windows Azure.

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  • Visual Basic Book Excerpt: Useful Namespaces

    This chapter provides an overview of some of the most important system namespaces and gives more detailed examples that demonstrate regular expressions, XML, cryptography, reflection, threading, parallel programming, and Direct3D....Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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