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  • Unknown margin being added to a footer in IE6 + 7

    - by Qwibble
    So yeah, like I said, I've spent a few hours trying to fix this bug in the footer that add's an extra 20-30px on to the bottom of the page in IE6 and 7. I've currently set all bottom margins to 0 so as to find what's causing it, I then scoured ie developer tools but came up empty. Here's the homepage design hosted on my web design playground - Link Can anyone see the remedy?

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  • How does one create an elegant iPhone GUI?

    - by jrtc27
    This is just one of those things where you feel like your own design is utterly terrible, and that all of the other apps have a beautiful design. This question is just about how you would go about creating a user interface that a user would actually want to use?

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  • Correct term for PSD to HTML to CMS

    - by John Magnolia
    Hi, I have heard a lot of different terms to describe the process of turning a website design into a editable CMS. Currently I take the design and "slice" this up into HTML and CSS then I "plug" this into a CMS. I would class this as frontend development depending on the level of customisation required for the CMS. The reason I ask is I am currently writing up my CV and have become stuck on the correct term for this. Kind Regards

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  • counting sub rows in mysql

    - by moustafa
    i have 2 table ok catgories and artilces i have this structure catgories web > design > photoshop > layers web > design > photoshop > effects and each one is a catgory and layers catgories has 100 article and effects catgories has 50 article now i want when count the articles 'web' catgory it show 150 article how i can do this give me an example

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  • Component Level Documentation

    - by Jason Summers
    I'm trying to make good on a promise I've made to provide a decent set of documentation for a C# component that I've written. I've done some googling and found templates for software design at high and low level. The problem is that all of the templates seem to be geared towards a complete system design as opposed to individual components and are consequently overkill. Can anyone please point me in the direction of a template geared towards component documentation? Many thanks

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  • Mutiple FK columns all pointing to the same parent table - a good idea?

    - by Randy Minder
    For those of you who live and breath database design, have you ever found compelling reasons to have multiple FK's in a table that all point to the same parent table? We recently had to deal with a situation where we had a table that contained six columns which were all FK columns to the same parent table. We're debating whether this indicates a poor design on our part or whether this is more common than we think. Thanks very much.

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  • Where's the best place to find good senior web developers?

    - by bokani
    We are looking for a senior web developer for a business start up based in London Mayfair? • Demonstrable experience developing Web 2.0 projects • Complete fluency in HTML, Javascript, CSS, php and MySQL • Experience of jQuery, AJAX and php interaction • Ability to develop applications making use of APIs (Google Maps, Facebook, bespoke CRMs and similar) • Good design aesthetic, including familiarity with Photoshop and CSS • Substantial experience hand-coding • Familiarity with server administration including cPanel • Ability to design HTML newsletters • Progressive enhancement • AJAX application state-memory Salary : £30,000 to £40,000

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  • magento show categories on left sidebar on a page

    - by misulicus
    I cant manage to show on a page, on the left side the categories. I selected for the page under Design - layout to 3 columns, Right side shows fine but nothing on left side. New to magento so i`m not sure in wich file in the template i have to look for. Its a custom template installed so i got so far to: app/design/frontend/default/f001/template/ but not sure now if to look under catalog or paeg folders

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  • Intermediate values in C++

    - by sterh
    Hello. I can not find how to implement a design in C++. In the language of Delphi in case the operator can write the following design: case s[j] of '0'..'9','A'..'Z','a'..'z','_': doSomeThing(); How can i do the same in c++. Attracts me is the construction type 'a' .. 'z' and etc... Thank you

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  • displaying python's autodoc to the user (python 3.3)

    - by Plotinus
    I'm writing a simple command line math game, and I'm using python's autodoc for my math algorithms to help me remember, for example, what a proth number is while i'm writing the algorithm, but later on I'll want to tell that information to the user as well, so they'll know what the answer was. So, for example I have: def is_proth(): """Proth numbers and numbers that fit the formula k×2^n + 1, where k are odd positive integers, and 2^n > k.""" [snip] return proths and then I tried to make a dictionary, like so: definitions = {"proths" : help(is_proth)} But it doesn't work. It prints this when I start the program, one for each item in the dictionary, and then it errors out on one of them that returns a set. And anyway, I don't want it displayed to the user until after they've played the game. Help on function is_proth in module __main__: is_proth() Proth numbers and numbers that fit the formula k×2^n + 1, where k are odd positive integers, and 2^n > k. (END) I understand the purpose of autodoc is more for helping programmers who are calling a function than for generating userdoc, but it seems inefficient to have to type out the definition of what a proth number is twice, once in a comment to help me remember what an algorithm does and then once to tell the user the answer to the game they were playing after they've won or lost.

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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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  • A Nondeterministic Engine written in VB.NET 2010

    - by neil chen
    When I'm reading SICP (Structure and Interpretation of Computer Programs) recently, I'm very interested in the concept of an "Nondeterministic Algorithm". According to wikipedia:  In computer science, a nondeterministic algorithm is an algorithm with one or more choice points where multiple different continuations are possible, without any specification of which one will be taken. For example, here is an puzzle came from the SICP: Baker, Cooper, Fletcher, Miller, and Smith live on different floors of an apartment housethat contains only five floors. Baker does not live on the top floor. Cooper does not live onthe bottom floor. Fletcher does not live on either the top or the bottom floor. Miller lives ona higher floor than does Cooper. Smith does not live on a floor adjacent to Fletcher's.Fletcher does not live on a floor adjacent to Cooper's. Where does everyone live? After reading this I decided to build a simple nondeterministic calculation engine with .NET. The rough idea is that we can use an iterator to track each set of possible values of the parameters, and then we implement some logic inside the engine to automate the statemachine, so that we can try one combination of the values, then test it, and then move to the next. We also used a backtracking algorithm to go back when we are running out of choices at some point. Following is the core code of the engine itself: Code highlighting produced by Actipro CodeHighlighter (freeware)http://www.CodeHighlighter.com/--Public Class NonDeterministicEngine Private _paramDict As New List(Of Tuple(Of String, IEnumerator)) 'Private _predicateDict As New List(Of Tuple(Of Func(Of Object, Boolean), IEnumerable(Of String))) Private _predicateDict As New List(Of Tuple(Of Object, IList(Of String))) Public Sub AddParam(ByVal name As String, ByVal values As IEnumerable) _paramDict.Add(New Tuple(Of String, IEnumerator)(name, values.GetEnumerator())) End Sub Public Sub AddRequire(ByVal predicate As Func(Of Object, Boolean), ByVal paramNames As IList(Of String)) CheckParamCount(1, paramNames) _predicateDict.Add(New Tuple(Of Object, IList(Of String))(predicate, paramNames)) End Sub Public Sub AddRequire(ByVal predicate As Func(Of Object, Object, Boolean), ByVal paramNames As IList(Of String)) CheckParamCount(2, paramNames) _predicateDict.Add(New Tuple(Of Object, IList(Of String))(predicate, paramNames)) End Sub Public Sub AddRequire(ByVal predicate As Func(Of Object, Object, Object, Boolean), ByVal paramNames As IList(Of String)) CheckParamCount(3, paramNames) _predicateDict.Add(New Tuple(Of Object, IList(Of String))(predicate, paramNames)) End Sub Public Sub AddRequire(ByVal predicate As Func(Of Object, Object, Object, Object, Boolean), ByVal paramNames As IList(Of String)) CheckParamCount(4, paramNames) _predicateDict.Add(New Tuple(Of Object, IList(Of String))(predicate, paramNames)) End Sub Public Sub AddRequire(ByVal predicate As Func(Of Object, Object, Object, Object, Object, Boolean), ByVal paramNames As IList(Of String)) CheckParamCount(5, paramNames) _predicateDict.Add(New Tuple(Of Object, IList(Of String))(predicate, paramNames)) End Sub Public Sub AddRequire(ByVal predicate As Func(Of Object, Object, Object, Object, Object, Object, Boolean), ByVal paramNames As IList(Of String)) CheckParamCount(6, paramNames) _predicateDict.Add(New Tuple(Of Object, IList(Of String))(predicate, paramNames)) End Sub Public Sub AddRequire(ByVal predicate As Func(Of Object, Object, Object, Object, Object, Object, Object, Boolean), ByVal paramNames As IList(Of String)) CheckParamCount(7, paramNames) _predicateDict.Add(New Tuple(Of Object, IList(Of String))(predicate, paramNames)) End Sub Public Sub AddRequire(ByVal predicate As Func(Of Object, Object, Object, Object, Object, Object, Object, Object, Boolean), ByVal paramNames As IList(Of String)) CheckParamCount(8, paramNames) _predicateDict.Add(New Tuple(Of Object, IList(Of String))(predicate, paramNames)) End Sub Sub CheckParamCount(ByVal count As Integer, ByVal paramNames As IList(Of String)) If paramNames.Count <> count Then Throw New Exception("Parameter count does not match.") End If End Sub Public Property IterationOver As Boolean Private _firstTime As Boolean = True Public ReadOnly Property Current As Dictionary(Of String, Object) Get If IterationOver Then Return Nothing Else Dim _nextResult = New Dictionary(Of String, Object) For Each item In _paramDict Dim iter = item.Item2 _nextResult.Add(item.Item1, iter.Current) Next Return _nextResult End If End Get End Property Function MoveNext() As Boolean If IterationOver Then Return False End If If _firstTime Then For Each item In _paramDict Dim iter = item.Item2 iter.MoveNext() Next _firstTime = False Return True Else Dim canMoveNext = False Dim iterIndex = _paramDict.Count - 1 canMoveNext = _paramDict(iterIndex).Item2.MoveNext If canMoveNext Then Return True End If Do While Not canMoveNext iterIndex = iterIndex - 1 If iterIndex = -1 Then Return False IterationOver = True End If canMoveNext = _paramDict(iterIndex).Item2.MoveNext If canMoveNext Then For i = iterIndex + 1 To _paramDict.Count - 1 Dim iter = _paramDict(i).Item2 iter.Reset() iter.MoveNext() Next Return True End If Loop End If End Function Function GetNextResult() As Dictionary(Of String, Object) While MoveNext() Dim result = Current If Satisfy(result) Then Return result End If End While Return Nothing End Function Function Satisfy(ByVal result As Dictionary(Of String, Object)) As Boolean For Each item In _predicateDict Dim pred = item.Item1 Select Case item.Item2.Count Case 1 Dim p1 = DirectCast(pred, Func(Of Object, Boolean)) Dim v1 = result(item.Item2(0)) If Not p1(v1) Then Return False End If Case 2 Dim p2 = DirectCast(pred, Func(Of Object, Object, Boolean)) Dim v1 = result(item.Item2(0)) Dim v2 = result(item.Item2(1)) If Not p2(v1, v2) Then Return False End If Case 3 Dim p3 = DirectCast(pred, Func(Of Object, Object, Object, Boolean)) Dim v1 = result(item.Item2(0)) Dim v2 = result(item.Item2(1)) Dim v3 = result(item.Item2(2)) If Not p3(v1, v2, v3) Then Return False End If Case 4 Dim p4 = DirectCast(pred, Func(Of Object, Object, Object, Object, Boolean)) Dim v1 = result(item.Item2(0)) Dim v2 = result(item.Item2(1)) Dim v3 = result(item.Item2(2)) Dim v4 = result(item.Item2(3)) If Not p4(v1, v2, v3, v4) Then Return False End If Case 5 Dim p5 = DirectCast(pred, Func(Of Object, Object, Object, Object, Object, Boolean)) Dim v1 = result(item.Item2(0)) Dim v2 = result(item.Item2(1)) Dim v3 = result(item.Item2(2)) Dim v4 = result(item.Item2(3)) Dim v5 = result(item.Item2(4)) If Not p5(v1, v2, v3, v4, v5) Then Return False End If Case 6 Dim p6 = DirectCast(pred, Func(Of Object, Object, Object, Object, Object, Object, Boolean)) Dim v1 = result(item.Item2(0)) Dim v2 = result(item.Item2(1)) Dim v3 = result(item.Item2(2)) Dim v4 = result(item.Item2(3)) Dim v5 = result(item.Item2(4)) Dim v6 = result(item.Item2(5)) If Not p6(v1, v2, v3, v4, v5, v6) Then Return False End If Case 7 Dim p7 = DirectCast(pred, Func(Of Object, Object, Object, Object, Object, Object, Object, Boolean)) Dim v1 = result(item.Item2(0)) Dim v2 = result(item.Item2(1)) Dim v3 = result(item.Item2(2)) Dim v4 = result(item.Item2(3)) Dim v5 = result(item.Item2(4)) Dim v6 = result(item.Item2(5)) Dim v7 = result(item.Item2(6)) If Not p7(v1, v2, v3, v4, v5, v6, v7) Then Return False End If Case 8 Dim p8 = DirectCast(pred, Func(Of Object, Object, Object, Object, Object, Object, Object, Object, Boolean)) Dim v1 = result(item.Item2(0)) Dim v2 = result(item.Item2(1)) Dim v3 = result(item.Item2(2)) Dim v4 = result(item.Item2(3)) Dim v5 = result(item.Item2(4)) Dim v6 = result(item.Item2(5)) Dim v7 = result(item.Item2(6)) Dim v8 = result(item.Item2(7)) If Not p8(v1, v2, v3, v4, v5, v6, v7, v8) Then Return False End If Case Else Throw New NotSupportedException End Select Next Return True End FunctionEnd Class    And now we can use the engine to solve the problem we mentioned above:   Code highlighting produced by Actipro CodeHighlighter (freeware)http://www.CodeHighlighter.com/--Sub Test2() Dim engine = New NonDeterministicEngine() engine.AddParam("baker", {1, 2, 3, 4, 5}) engine.AddParam("cooper", {1, 2, 3, 4, 5}) engine.AddParam("fletcher", {1, 2, 3, 4, 5}) engine.AddParam("miller", {1, 2, 3, 4, 5}) engine.AddParam("smith", {1, 2, 3, 4, 5}) engine.AddRequire(Function(baker) As Boolean Return baker <> 5 End Function, {"baker"}) engine.AddRequire(Function(cooper) As Boolean Return cooper <> 1 End Function, {"cooper"}) engine.AddRequire(Function(fletcher) As Boolean Return fletcher <> 1 And fletcher <> 5 End Function, {"fletcher"}) engine.AddRequire(Function(miller, cooper) As Boolean 'Return miller = cooper + 1 Return miller > cooper End Function, {"miller", "cooper"}) engine.AddRequire(Function(smith, fletcher) As Boolean Return smith <> fletcher + 1 And smith <> fletcher - 1 End Function, {"smith", "fletcher"}) engine.AddRequire(Function(fletcher, cooper) As Boolean Return fletcher <> cooper + 1 And fletcher <> cooper - 1 End Function, {"fletcher", "cooper"}) engine.AddRequire(Function(a, b, c, d, e) As Boolean Return a <> b And a <> c And a <> d And a <> e And b <> c And b <> d And b <> e And c <> d And c <> e And d <> e End Function, {"baker", "cooper", "fletcher", "miller", "smith"}) Dim result = engine.GetNextResult() While Not result Is Nothing Console.WriteLine(String.Format("baker: {0}, cooper: {1}, fletcher: {2}, miller: {3}, smith: {4}", result("baker"), result("cooper"), result("fletcher"), result("miller"), result("smith"))) result = engine.GetNextResult() End While Console.WriteLine("Calculation ended.")End Sub   Also, this engine can solve the classic 8 queens puzzle and find out all 92 results for me.   Code highlighting produced by Actipro CodeHighlighter (freeware)http://www.CodeHighlighter.com/--Sub Test3() ' The 8-Queens problem. Dim engine = New NonDeterministicEngine() ' Let's assume that a - h represents the queens in row 1 to 8, then we just need to find out the column number for each of them. engine.AddParam("a", {1, 2, 3, 4, 5, 6, 7, 8}) engine.AddParam("b", {1, 2, 3, 4, 5, 6, 7, 8}) engine.AddParam("c", {1, 2, 3, 4, 5, 6, 7, 8}) engine.AddParam("d", {1, 2, 3, 4, 5, 6, 7, 8}) engine.AddParam("e", {1, 2, 3, 4, 5, 6, 7, 8}) engine.AddParam("f", {1, 2, 3, 4, 5, 6, 7, 8}) engine.AddParam("g", {1, 2, 3, 4, 5, 6, 7, 8}) engine.AddParam("h", {1, 2, 3, 4, 5, 6, 7, 8}) Dim NotInTheSameDiagonalLine = Function(cols As IList) As Boolean For i = 0 To cols.Count - 2 For j = i + 1 To cols.Count - 1 If j - i = Math.Abs(cols(j) - cols(i)) Then Return False End If Next Next Return True End Function engine.AddRequire(Function(a, b, c, d, e, f, g, h) As Boolean Return a <> b AndAlso a <> c AndAlso a <> d AndAlso a <> e AndAlso a <> f AndAlso a <> g AndAlso a <> h AndAlso b <> c AndAlso b <> d AndAlso b <> e AndAlso b <> f AndAlso b <> g AndAlso b <> h AndAlso c <> d AndAlso c <> e AndAlso c <> f AndAlso c <> g AndAlso c <> h AndAlso d <> e AndAlso d <> f AndAlso d <> g AndAlso d <> h AndAlso e <> f AndAlso e <> g AndAlso e <> h AndAlso f <> g AndAlso f <> h AndAlso g <> h AndAlso NotInTheSameDiagonalLine({a, b, c, d, e, f, g, h}) End Function, {"a", "b", "c", "d", "e", "f", "g", "h"}) Dim result = engine.GetNextResult() While Not result Is Nothing Console.WriteLine("(1,{0}), (2,{1}), (3,{2}), (4,{3}), (5,{4}), (6,{5}), (7,{6}), (8,{7})", result("a"), result("b"), result("c"), result("d"), result("e"), result("f"), result("g"), result("h")) result = engine.GetNextResult() End While Console.WriteLine("Calculation ended.")End Sub (Chinese version of the post: http://www.cnblogs.com/RChen/archive/2010/05/17/1737587.html) Cheers,  

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  • Need some advice regarding collision detection with the sprite changing its width and height

    - by Frank Scott
    So I'm messing around with collision detection in my tile-based game and everything works fine and dandy using this method. However, now I am trying to implement sprite sheets so my character can have a walking and jumping animation. For one, I'd like to to be able to have each frame of variable size, I think. I want collision detection to be accurate and during a jumping animation the sprite's height will be shorter (because of the calves meeting the hamstrings). Again, this also works fine at the moment. I can get the character to animate properly each frame and cycle through animations. The problems arise when the width and height of the character change. Often times its position will be corrected by the collision detection system and the character will be rubber-banded to random parts of the map or even go outside the map bounds. For some reason with the linked collision detection algorithm, when the width or height of the sprite is changed on the fly, the entire algorithm breaks down. The solution I found so far is to have a single width and height of the sprite that remains constant, and only adjust the source rectangle for drawing. However, I'm not sure exactly what to set as the sprite's constant bounding box because it varies so much with the different animations. So now I'm not sure what to do. I'm toying with the idea of pixel-perfect collision detection but I'm not sure if it would really be worth it. Does anyone know how Braid does their collision detection? My game is also a 2D sidescroller and I was quite impressed with how it was handled in that game. Thanks for reading.

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  • Does a site's bounce rate influence Google rankings?

    - by Joel Spolsky
    Does Google consider bounce rate or something similar in ranking sites? Background: here at Stack Exchange we noticed that the latest Google algorithm changes resulted in about a 20% dip in traffic to Server Fault (and a much smaller dip in traffic to Super User). Stack Overflow traffic was not affected. There was an article on WebProNews which hypothesized that bounce rate might be a ranking signal in Google's latest Panda update. According to Google Analytics, these are our bounce rates over the last month: Site Bounce Rate Avg Time on Site ------------- ----------- ---------------- SuperUser 84.67% 01:16 ServerFault 83.76% 00:53 Stack Overflow 63.63% 04:12 Now, technically, Google has no way to know the bounce rate. If you go to Google, search for something, and click on the first result, Google can't tell the difference between: a user who turns off their computer a user who goes to a completely different web site a user who spends hours clicking around on the website they landed on What Google does know is how long it takes the user to come back to Google and do another search. According to the book In The Plex (page 47), Google distinguishes between what they call "short clicks" and "long clicks": A short click is a search where the user quickly comes back to Google and does another search. Google interprets this as a signal that the first search results were unsatisfactory. A long click is a search where the user doesn't search again for a long time. The book says that Google uses this information internally, to judge the quality of their own algorithms. It also said that short click data in which someone retypes a slight variation of the search is used to fuel the "Did you mean...?" spell checking algorithm. So, my hypothesis is that Google has recently decided to use long click rates as a signal of a high quality site. Does anyone have any evidence of this? Have you seen any high-bounce-rate sites which lost traffic (or vice-versa)?

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  • value types in the vm

    - by john.rose
    value types in the vm p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} p.p2 {margin: 0.0px 0.0px 14.0px 0.0px; font: 14.0px Times} p.p3 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times} p.p4 {margin: 0.0px 0.0px 15.0px 0.0px; font: 14.0px Times} p.p5 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier} p.p6 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier; min-height: 17.0px} p.p7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p8 {margin: 0.0px 0.0px 0.0px 36.0px; text-indent: -36.0px; font: 14.0px Times; min-height: 18.0px} p.p9 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p10 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; color: #000000} li.li1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} li.li7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} span.s1 {font: 14.0px Courier} span.s2 {color: #000000} span.s3 {font: 14.0px Courier; color: #000000} ol.ol1 {list-style-type: decimal} Or, enduring values for a changing world. Introduction A value type is a data type which, generally speaking, is designed for being passed by value in and out of methods, and stored by value in data structures. The only value types which the Java language directly supports are the eight primitive types. Java indirectly and approximately supports value types, if they are implemented in terms of classes. For example, both Integer and String may be viewed as value types, especially if their usage is restricted to avoid operations appropriate to Object. In this note, we propose a definition of value types in terms of a design pattern for Java classes, accompanied by a set of usage restrictions. We also sketch the relation of such value types to tuple types (which are a JVM-level notion), and point out JVM optimizations that can apply to value types. This note is a thought experiment to extend the JVM’s performance model in support of value types. The demonstration has two phases.  Initially the extension can simply use design patterns, within the current bytecode architecture, and in today’s Java language. But if the performance model is to be realized in practice, it will probably require new JVM bytecode features, changes to the Java language, or both.  We will look at a few possibilities for these new features. An Axiom of Value In the context of the JVM, a value type is a data type equipped with construction, assignment, and equality operations, and a set of typed components, such that, whenever two variables of the value type produce equal corresponding values for their components, the values of the two variables cannot be distinguished by any JVM operation. Here are some corollaries: A value type is immutable, since otherwise a copy could be constructed and the original could be modified in one of its components, allowing the copies to be distinguished. Changing the component of a value type requires construction of a new value. The equals and hashCode operations are strictly component-wise. If a value type is represented by a JVM reference, that reference cannot be successfully synchronized on, and cannot be usefully compared for reference equality. A value type can be viewed in terms of what it doesn’t do. We can say that a value type omits all value-unsafe operations, which could violate the constraints on value types.  These operations, which are ordinarily allowed for Java object types, are pointer equality comparison (the acmp instruction), synchronization (the monitor instructions), all the wait and notify methods of class Object, and non-trivial finalize methods. The clone method is also value-unsafe, although for value types it could be treated as the identity function. Finally, and most importantly, any side effect on an object (however visible) also counts as an value-unsafe operation. A value type may have methods, but such methods must not change the components of the value. It is reasonable and useful to define methods like toString, equals, and hashCode on value types, and also methods which are specifically valuable to users of the value type. Representations of Value Value types have two natural representations in the JVM, unboxed and boxed. An unboxed value consists of the components, as simple variables. For example, the complex number x=(1+2i), in rectangular coordinate form, may be represented in unboxed form by the following pair of variables: /*Complex x = Complex.valueOf(1.0, 2.0):*/ double x_re = 1.0, x_im = 2.0; These variables might be locals, parameters, or fields. Their association as components of a single value is not defined to the JVM. Here is a sample computation which computes the norm of the difference between two complex numbers: double distance(/*Complex x:*/ double x_re, double x_im,         /*Complex y:*/ double y_re, double y_im) {     /*Complex z = x.minus(y):*/     double z_re = x_re - y_re, z_im = x_im - y_im;     /*return z.abs():*/     return Math.sqrt(z_re*z_re + z_im*z_im); } A boxed representation groups component values under a single object reference. The reference is to a ‘wrapper class’ that carries the component values in its fields. (A primitive type can naturally be equated with a trivial value type with just one component of that type. In that view, the wrapper class Integer can serve as a boxed representation of value type int.) The unboxed representation of complex numbers is practical for many uses, but it fails to cover several major use cases: return values, array elements, and generic APIs. The two components of a complex number cannot be directly returned from a Java function, since Java does not support multiple return values. The same story applies to array elements: Java has no ’array of structs’ feature. (Double-length arrays are a possible workaround for complex numbers, but not for value types with heterogeneous components.) By generic APIs I mean both those which use generic types, like Arrays.asList and those which have special case support for primitive types, like String.valueOf and PrintStream.println. Those APIs do not support unboxed values, and offer some problems to boxed values. Any ’real’ JVM type should have a story for returns, arrays, and API interoperability. The basic problem here is that value types fall between primitive types and object types. Value types are clearly more complex than primitive types, and object types are slightly too complicated. Objects are a little bit dangerous to use as value carriers, since object references can be compared for pointer equality, and can be synchronized on. Also, as many Java programmers have observed, there is often a performance cost to using wrapper objects, even on modern JVMs. Even so, wrapper classes are a good starting point for talking about value types. If there were a set of structural rules and restrictions which would prevent value-unsafe operations on value types, wrapper classes would provide a good notation for defining value types. This note attempts to define such rules and restrictions. Let’s Start Coding Now it is time to look at some real code. Here is a definition, written in Java, of a complex number value type. @ValueSafe public final class Complex implements java.io.Serializable {     // immutable component structure:     public final double re, im;     private Complex(double re, double im) {         this.re = re; this.im = im;     }     // interoperability methods:     public String toString() { return "Complex("+re+","+im+")"; }     public List<Double> asList() { return Arrays.asList(re, im); }     public boolean equals(Complex c) {         return re == c.re && im == c.im;     }     public boolean equals(@ValueSafe Object x) {         return x instanceof Complex && equals((Complex) x);     }     public int hashCode() {         return 31*Double.valueOf(re).hashCode()                 + Double.valueOf(im).hashCode();     }     // factory methods:     public static Complex valueOf(double re, double im) {         return new Complex(re, im);     }     public Complex changeRe(double re2) { return valueOf(re2, im); }     public Complex changeIm(double im2) { return valueOf(re, im2); }     public static Complex cast(@ValueSafe Object x) {         return x == null ? ZERO : (Complex) x;     }     // utility methods and constants:     public Complex plus(Complex c)  { return new Complex(re+c.re, im+c.im); }     public Complex minus(Complex c) { return new Complex(re-c.re, im-c.im); }     public double abs() { return Math.sqrt(re*re + im*im); }     public static final Complex PI = valueOf(Math.PI, 0.0);     public static final Complex ZERO = valueOf(0.0, 0.0); } This is not a minimal definition, because it includes some utility methods and other optional parts.  The essential elements are as follows: The class is marked as a value type with an annotation. The class is final, because it does not make sense to create subclasses of value types. The fields of the class are all non-private and final.  (I.e., the type is immutable and structurally transparent.) From the supertype Object, all public non-final methods are overridden. The constructor is private. Beyond these bare essentials, we can observe the following features in this example, which are likely to be typical of all value types: One or more factory methods are responsible for value creation, including a component-wise valueOf method. There are utility methods for complex arithmetic and instance creation, such as plus and changeIm. There are static utility constants, such as PI. The type is serializable, using the default mechanisms. There are methods for converting to and from dynamically typed references, such as asList and cast. The Rules In order to use value types properly, the programmer must avoid value-unsafe operations.  A helpful Java compiler should issue errors (or at least warnings) for code which provably applies value-unsafe operations, and should issue warnings for code which might be correct but does not provably avoid value-unsafe operations.  No such compilers exist today, but to simplify our account here, we will pretend that they do exist. A value-safe type is any class, interface, or type parameter marked with the @ValueSafe annotation, or any subtype of a value-safe type.  If a value-safe class is marked final, it is in fact a value type.  All other value-safe classes must be abstract.  The non-static fields of a value class must be non-public and final, and all its constructors must be private. Under the above rules, a standard interface could be helpful to define value types like Complex.  Here is an example: @ValueSafe public interface ValueType extends java.io.Serializable {     // All methods listed here must get redefined.     // Definitions must be value-safe, which means     // they may depend on component values only.     List<? extends Object> asList();     int hashCode();     boolean equals(@ValueSafe Object c);     String toString(); } //@ValueSafe inherited from supertype: public final class Complex implements ValueType { … The main advantage of such a conventional interface is that (unlike an annotation) it is reified in the runtime type system.  It could appear as an element type or parameter bound, for facilities which are designed to work on value types only.  More broadly, it might assist the JVM to perform dynamic enforcement of the rules for value types. Besides types, the annotation @ValueSafe can mark fields, parameters, local variables, and methods.  (This is redundant when the type is also value-safe, but may be useful when the type is Object or another supertype of a value type.)  Working forward from these annotations, an expression E is defined as value-safe if it satisfies one or more of the following: The type of E is a value-safe type. E names a field, parameter, or local variable whose declaration is marked @ValueSafe. E is a call to a method whose declaration is marked @ValueSafe. E is an assignment to a value-safe variable, field reference, or array reference. E is a cast to a value-safe type from a value-safe expression. E is a conditional expression E0 ? E1 : E2, and both E1 and E2 are value-safe. Assignments to value-safe expressions and initializations of value-safe names must take their values from value-safe expressions. A value-safe expression may not be the subject of a value-unsafe operation.  In particular, it cannot be synchronized on, nor can it be compared with the “==” operator, not even with a null or with another value-safe type. In a program where all of these rules are followed, no value-type value will be subject to a value-unsafe operation.  Thus, the prime axiom of value types will be satisfied, that no two value type will be distinguishable as long as their component values are equal. More Code To illustrate these rules, here are some usage examples for Complex: Complex pi = Complex.valueOf(Math.PI, 0); Complex zero = pi.changeRe(0);  //zero = pi; zero.re = 0; ValueType vtype = pi; @SuppressWarnings("value-unsafe")   Object obj = pi; @ValueSafe Object obj2 = pi; obj2 = new Object();  // ok List<Complex> clist = new ArrayList<Complex>(); clist.add(pi);  // (ok assuming List.add param is @ValueSafe) List<ValueType> vlist = new ArrayList<ValueType>(); vlist.add(pi);  // (ok) List<Object> olist = new ArrayList<Object>(); olist.add(pi);  // warning: "value-unsafe" boolean z = pi.equals(zero); boolean z1 = (pi == zero);  // error: reference comparison on value type boolean z2 = (pi == null);  // error: reference comparison on value type boolean z3 = (pi == obj2);  // error: reference comparison on value type synchronized (pi) { }  // error: synch of value, unpredictable result synchronized (obj2) { }  // unpredictable result Complex qq = pi; qq = null;  // possible NPE; warning: “null-unsafe" qq = (Complex) obj;  // warning: “null-unsafe" qq = Complex.cast(obj);  // OK @SuppressWarnings("null-unsafe")   Complex empty = null;  // possible NPE qq = empty;  // possible NPE (null pollution) The Payoffs It follows from this that either the JVM or the java compiler can replace boxed value-type values with unboxed ones, without affecting normal computations.  Fields and variables of value types can be split into their unboxed components.  Non-static methods on value types can be transformed into static methods which take the components as value parameters. Some common questions arise around this point in any discussion of value types. Why burden the programmer with all these extra rules?  Why not detect programs automagically and perform unboxing transparently?  The answer is that it is easy to break the rules accidently unless they are agreed to by the programmer and enforced.  Automatic unboxing optimizations are tantalizing but (so far) unreachable ideal.  In the current state of the art, it is possible exhibit benchmarks in which automatic unboxing provides the desired effects, but it is not possible to provide a JVM with a performance model that assures the programmer when unboxing will occur.  This is why I’m writing this note, to enlist help from, and provide assurances to, the programmer.  Basically, I’m shooting for a good set of user-supplied “pragmas” to frame the desired optimization. Again, the important thing is that the unboxing must be done reliably, or else programmers will have no reason to work with the extra complexity of the value-safety rules.  There must be a reasonably stable performance model, wherein using a value type has approximately the same performance characteristics as writing the unboxed components as separate Java variables. There are some rough corners to the present scheme.  Since Java fields and array elements are initialized to null, value-type computations which incorporate uninitialized variables can produce null pointer exceptions.  One workaround for this is to require such variables to be null-tested, and the result replaced with a suitable all-zero value of the value type.  That is what the “cast” method does above. Generically typed APIs like List<T> will continue to manipulate boxed values always, at least until we figure out how to do reification of generic type instances.  Use of such APIs will elicit warnings until their type parameters (and/or relevant members) are annotated or typed as value-safe.  Retrofitting List<T> is likely to expose flaws in the present scheme, which we will need to engineer around.  Here are a couple of first approaches: public interface java.util.List<@ValueSafe T> extends Collection<T> { … public interface java.util.List<T extends Object|ValueType> extends Collection<T> { … (The second approach would require disjunctive types, in which value-safety is “contagious” from the constituent types.) With more transformations, the return value types of methods can also be unboxed.  This may require significant bytecode-level transformations, and would work best in the presence of a bytecode representation for multiple value groups, which I have proposed elsewhere under the title “Tuples in the VM”. But for starters, the JVM can apply this transformation under the covers, to internally compiled methods.  This would give a way to express multiple return values and structured return values, which is a significant pain-point for Java programmers, especially those who work with low-level structure types favored by modern vector and graphics processors.  The lack of multiple return values has a strong distorting effect on many Java APIs. Even if the JVM fails to unbox a value, there is still potential benefit to the value type.  Clustered computing systems something have copy operations (serialization or something similar) which apply implicitly to command operands.  When copying JVM objects, it is extremely helpful to know when an object’s identity is important or not.  If an object reference is a copied operand, the system may have to create a proxy handle which points back to the original object, so that side effects are visible.  Proxies must be managed carefully, and this can be expensive.  On the other hand, value types are exactly those types which a JVM can “copy and forget” with no downside. Array types are crucial to bulk data interfaces.  (As data sizes and rates increase, bulk data becomes more important than scalar data, so arrays are definitely accompanying us into the future of computing.)  Value types are very helpful for adding structure to bulk data, so a successful value type mechanism will make it easier for us to express richer forms of bulk data. Unboxing arrays (i.e., arrays containing unboxed values) will provide better cache and memory density, and more direct data movement within clustered or heterogeneous computing systems.  They require the deepest transformations, relative to today’s JVM.  There is an impedance mismatch between value-type arrays and Java’s covariant array typing, so compromises will need to be struck with existing Java semantics.  It is probably worth the effort, since arrays of unboxed value types are inherently more memory-efficient than standard Java arrays, which rely on dependent pointer chains. It may be sufficient to extend the “value-safe” concept to array declarations, and allow low-level transformations to change value-safe array declarations from the standard boxed form into an unboxed tuple-based form.  Such value-safe arrays would not be convertible to Object[] arrays.  Certain connection points, such as Arrays.copyOf and System.arraycopy might need additional input/output combinations, to allow smooth conversion between arrays with boxed and unboxed elements. Alternatively, the correct solution may have to wait until we have enough reification of generic types, and enough operator overloading, to enable an overhaul of Java arrays. Implicit Method Definitions The example of class Complex above may be unattractively complex.  I believe most or all of the elements of the example class are required by the logic of value types. If this is true, a programmer who writes a value type will have to write lots of error-prone boilerplate code.  On the other hand, I think nearly all of the code (except for the domain-specific parts like plus and minus) can be implicitly generated. Java has a rule for implicitly defining a class’s constructor, if no it defines no constructors explicitly.  Likewise, there are rules for providing default access modifiers for interface members.  Because of the highly regular structure of value types, it might be reasonable to perform similar implicit transformations on value types.  Here’s an example of a “highly implicit” definition of a complex number type: public class Complex implements ValueType {  // implicitly final     public double re, im;  // implicitly public final     //implicit methods are defined elementwise from te fields:     //  toString, asList, equals(2), hashCode, valueOf, cast     //optionally, explicit methods (plus, abs, etc.) would go here } In other words, with the right defaults, a simple value type definition can be a one-liner.  The observant reader will have noticed the similarities (and suitable differences) between the explicit methods above and the corresponding methods for List<T>. Another way to abbreviate such a class would be to make an annotation the primary trigger of the functionality, and to add the interface(s) implicitly: public @ValueType class Complex { … // implicitly final, implements ValueType (But to me it seems better to communicate the “magic” via an interface, even if it is rooted in an annotation.) Implicitly Defined Value Types So far we have been working with nominal value types, which is to say that the sequence of typed components is associated with a name and additional methods that convey the intention of the programmer.  A simple ordered pair of floating point numbers can be variously interpreted as (to name a few possibilities) a rectangular or polar complex number or Cartesian point.  The name and the methods convey the intended meaning. But what if we need a truly simple ordered pair of floating point numbers, without any further conceptual baggage?  Perhaps we are writing a method (like “divideAndRemainder”) which naturally returns a pair of numbers instead of a single number.  Wrapping the pair of numbers in a nominal type (like “QuotientAndRemainder”) makes as little sense as wrapping a single return value in a nominal type (like “Quotient”).  What we need here are structural value types commonly known as tuples. For the present discussion, let us assign a conventional, JVM-friendly name to tuples, roughly as follows: public class java.lang.tuple.$DD extends java.lang.tuple.Tuple {      double $1, $2; } Here the component names are fixed and all the required methods are defined implicitly.  The supertype is an abstract class which has suitable shared declarations.  The name itself mentions a JVM-style method parameter descriptor, which may be “cracked” to determine the number and types of the component fields. The odd thing about such a tuple type (and structural types in general) is it must be instantiated lazily, in response to linkage requests from one or more classes that need it.  The JVM and/or its class loaders must be prepared to spin a tuple type on demand, given a simple name reference, $xyz, where the xyz is cracked into a series of component types.  (Specifics of naming and name mangling need some tasteful engineering.) Tuples also seem to demand, even more than nominal types, some support from the language.  (This is probably because notations for non-nominal types work best as combinations of punctuation and type names, rather than named constructors like Function3 or Tuple2.)  At a minimum, languages with tuples usually (I think) have some sort of simple bracket notation for creating tuples, and a corresponding pattern-matching syntax (or “destructuring bind”) for taking tuples apart, at least when they are parameter lists.  Designing such a syntax is no simple thing, because it ought to play well with nominal value types, and also with pre-existing Java features, such as method parameter lists, implicit conversions, generic types, and reflection.  That is a task for another day. Other Use Cases Besides complex numbers and simple tuples there are many use cases for value types.  Many tuple-like types have natural value-type representations. These include rational numbers, point locations and pixel colors, and various kinds of dates and addresses. Other types have a variable-length ‘tail’ of internal values. The most common example of this is String, which is (mathematically) a sequence of UTF-16 character values. Similarly, bit vectors, multiple-precision numbers, and polynomials are composed of sequences of values. Such types include, in their representation, a reference to a variable-sized data structure (often an array) which (somehow) represents the sequence of values. The value type may also include ’header’ information. Variable-sized values often have a length distribution which favors short lengths. In that case, the design of the value type can make the first few values in the sequence be direct ’header’ fields of the value type. In the common case where the header is enough to represent the whole value, the tail can be a shared null value, or even just a null reference. Note that the tail need not be an immutable object, as long as the header type encapsulates it well enough. This is the case with String, where the tail is a mutable (but never mutated) character array. Field types and their order must be a globally visible part of the API.  The structure of the value type must be transparent enough to have a globally consistent unboxed representation, so that all callers and callees agree about the type and order of components  that appear as parameters, return types, and array elements.  This is a trade-off between efficiency and encapsulation, which is forced on us when we remove an indirection enjoyed by boxed representations.  A JVM-only transformation would not care about such visibility, but a bytecode transformation would need to take care that (say) the components of complex numbers would not get swapped after a redefinition of Complex and a partial recompile.  Perhaps constant pool references to value types need to declare the field order as assumed by each API user. This brings up the delicate status of private fields in a value type.  It must always be possible to load, store, and copy value types as coordinated groups, and the JVM performs those movements by moving individual scalar values between locals and stack.  If a component field is not public, what is to prevent hostile code from plucking it out of the tuple using a rogue aload or astore instruction?  Nothing but the verifier, so we may need to give it more smarts, so that it treats value types as inseparable groups of stack slots or locals (something like long or double). My initial thought was to make the fields always public, which would make the security problem moot.  But public is not always the right answer; consider the case of String, where the underlying mutable character array must be encapsulated to prevent security holes.  I believe we can win back both sides of the tradeoff, by training the verifier never to split up the components in an unboxed value.  Just as the verifier encapsulates the two halves of a 64-bit primitive, it can encapsulate the the header and body of an unboxed String, so that no code other than that of class String itself can take apart the values. Similar to String, we could build an efficient multi-precision decimal type along these lines: public final class DecimalValue extends ValueType {     protected final long header;     protected private final BigInteger digits;     public DecimalValue valueOf(int value, int scale) {         assert(scale >= 0);         return new DecimalValue(((long)value << 32) + scale, null);     }     public DecimalValue valueOf(long value, int scale) {         if (value == (int) value)             return valueOf((int)value, scale);         return new DecimalValue(-scale, new BigInteger(value));     } } Values of this type would be passed between methods as two machine words. Small values (those with a significand which fits into 32 bits) would be represented without any heap data at all, unless the DecimalValue itself were boxed. (Note the tension between encapsulation and unboxing in this case.  It would be better if the header and digits fields were private, but depending on where the unboxing information must “leak”, it is probably safer to make a public revelation of the internal structure.) Note that, although an array of Complex can be faked with a double-length array of double, there is no easy way to fake an array of unboxed DecimalValues.  (Either an array of boxed values or a transposed pair of homogeneous arrays would be reasonable fallbacks, in a current JVM.)  Getting the full benefit of unboxing and arrays will require some new JVM magic. Although the JVM emphasizes portability, system dependent code will benefit from using machine-level types larger than 64 bits.  For example, the back end of a linear algebra package might benefit from value types like Float4 which map to stock vector types.  This is probably only worthwhile if the unboxing arrays can be packed with such values. More Daydreams A more finely-divided design for dynamic enforcement of value safety could feature separate marker interfaces for each invariant.  An empty marker interface Unsynchronizable could cause suitable exceptions for monitor instructions on objects in marked classes.  More radically, a Interchangeable marker interface could cause JVM primitives that are sensitive to object identity to raise exceptions; the strangest result would be that the acmp instruction would have to be specified as raising an exception. @ValueSafe public interface ValueType extends java.io.Serializable,         Unsynchronizable, Interchangeable { … public class Complex implements ValueType {     // inherits Serializable, Unsynchronizable, Interchangeable, @ValueSafe     … It seems possible that Integer and the other wrapper types could be retro-fitted as value-safe types.  This is a major change, since wrapper objects would be unsynchronizable and their references interchangeable.  It is likely that code which violates value-safety for wrapper types exists but is uncommon.  It is less plausible to retro-fit String, since the prominent operation String.intern is often used with value-unsafe code. We should also reconsider the distinction between boxed and unboxed values in code.  The design presented above obscures that distinction.  As another thought experiment, we could imagine making a first class distinction in the type system between boxed and unboxed representations.  Since only primitive types are named with a lower-case initial letter, we could define that the capitalized version of a value type name always refers to the boxed representation, while the initial lower-case variant always refers to boxed.  For example: complex pi = complex.valueOf(Math.PI, 0); Complex boxPi = pi;  // convert to boxed myList.add(boxPi); complex z = myList.get(0);  // unbox Such a convention could perhaps absorb the current difference between int and Integer, double and Double. It might also allow the programmer to express a helpful distinction among array types. As said above, array types are crucial to bulk data interfaces, but are limited in the JVM.  Extending arrays beyond the present limitations is worth thinking about; for example, the Maxine JVM implementation has a hybrid object/array type.  Something like this which can also accommodate value type components seems worthwhile.  On the other hand, does it make sense for value types to contain short arrays?  And why should random-access arrays be the end of our design process, when bulk data is often sequentially accessed, and it might make sense to have heterogeneous streams of data as the natural “jumbo” data structure.  These considerations must wait for another day and another note. More Work It seems to me that a good sequence for introducing such value types would be as follows: Add the value-safety restrictions to an experimental version of javac. Code some sample applications with value types, including Complex and DecimalValue. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. A staggered roll-out like this would decouple language changes from bytecode changes, which is always a convenient thing. A similar investigation should be applied (concurrently) to array types.  In this case, it seems to me that the starting point is in the JVM: Add an experimental unboxing array data structure to a production JVM, perhaps along the lines of Maxine hybrids.  No bytecode or language support is required at first; everything can be done with encapsulated unsafe operations and/or method handles. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. That’s enough musing me for now.  Back to work!

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  • Optimizing Solaris 11 SHA-1 on Intel Processors

    - by danx
    SHA-1 is a "hash" or "digest" operation that produces a 160 bit (20 byte) checksum value on arbitrary data, such as a file. It is intended to uniquely identify text and to verify it hasn't been modified. Max Locktyukhin and others at Intel have improved the performance of the SHA-1 digest algorithm using multiple techniques. This code has been incorporated into Solaris 11 and is available in the Solaris Crypto Framework via the libmd(3LIB), the industry-standard libpkcs11(3LIB) library, and Solaris kernel module sha1. The optimized code is used automatically on systems with a x86 CPU supporting SSSE3 (Intel Supplemental SSSE3). Intel microprocessor architectures that support SSSE3 include Nehalem, Westmere, Sandy Bridge microprocessor families. Further optimizations are available for microprocessors that support AVX (such as Sandy Bridge). Although SHA-1 is considered obsolete because of weaknesses found in the SHA-1 algorithm—NIST recommends using at least SHA-256, SHA-1 is still widely used and will be with us for awhile more. Collisions (the same SHA-1 result for two different inputs) can be found with moderate effort. SHA-1 is used heavily though in SSL/TLS, for example. And SHA-1 is stronger than the older MD5 digest algorithm, another digest option defined in SSL/TLS. Optimizations Review SHA-1 operates by reading an arbitrary amount of data. The data is read in 512 bit (64 byte) blocks (the last block is padded in a specific way to ensure it's a full 64 bytes). Each 64 byte block has 80 "rounds" of calculations (consisting of a mixture of "ROTATE-LEFT", "AND", and "XOR") applied to the block. Each round produces a 32-bit intermediate result, called W[i]. Here's what each round operates: The first 16 rounds, rounds 0 to 15, read the 512 bit block 32 bits at-a-time. These 32 bits is used as input to the round. The remaining rounds, rounds 16 to 79, use the results from the previous rounds as input. Specifically for round i it XORs the results of rounds i-3, i-8, i-14, and i-16 and rotates the result left 1 bit. The remaining calculations for the round is a series of AND, XOR, and ROTATE-LEFT operators on the 32-bit input and some constants. The 32-bit result is saved as W[i] for round i. The 32-bit result of the final round, W[79], is the SHA-1 checksum. Optimization: Vectorization The first 16 rounds can be vectorized (computed in parallel) because they don't depend on the output of a previous round. As for the remaining rounds, because of step 2 above, computing round i depends on the results of round i-3, W[i-3], one can vectorize 3 rounds at-a-time. Max Locktyukhin found through simple factoring, explained in detail in his article referenced below, that the dependencies of round i on the results of rounds i-3, i-8, i-14, and i-16 can be replaced instead with dependencies on the results of rounds i-6, i-16, i-28, and i-32. That is, instead of initializing intermediate result W[i] with: W[i] = (W[i-3] XOR W[i-8] XOR W[i-14] XOR W[i-16]) ROTATE-LEFT 1 Initialize W[i] as follows: W[i] = (W[i-6] XOR W[i-16] XOR W[i-28] XOR W[i-32]) ROTATE-LEFT 2 That means that 6 rounds could be vectorized at once, with no additional calculations, instead of just 3! This optimization is independent of Intel or any other microprocessor architecture, although the microprocessor has to support vectorization to use it, and exploits one of the weaknesses of SHA-1. Optimization: SSSE3 Intel SSSE3 makes use of 16 %xmm registers, each 128 bits wide. The 4 32-bit inputs to a round, W[i-6], W[i-16], W[i-28], W[i-32], all fit in one %xmm register. The following code snippet, from Max Locktyukhin's article, converted to ATT assembly syntax, computes 4 rounds in parallel with just a dozen or so SSSE3 instructions: movdqa W_minus_04, W_TMP pxor W_minus_28, W // W equals W[i-32:i-29] before XOR // W = W[i-32:i-29] ^ W[i-28:i-25] palignr $8, W_minus_08, W_TMP // W_TMP = W[i-6:i-3], combined from // W[i-4:i-1] and W[i-8:i-5] vectors pxor W_minus_16, W // W = (W[i-32:i-29] ^ W[i-28:i-25]) ^ W[i-16:i-13] pxor W_TMP, W // W = (W[i-32:i-29] ^ W[i-28:i-25] ^ W[i-16:i-13]) ^ W[i-6:i-3]) movdqa W, W_TMP // 4 dwords in W are rotated left by 2 psrld $30, W // rotate left by 2 W = (W >> 30) | (W << 2) pslld $2, W_TMP por W, W_TMP movdqa W_TMP, W // four new W values W[i:i+3] are now calculated paddd (K_XMM), W_TMP // adding 4 current round's values of K movdqa W_TMP, (WK(i)) // storing for downstream GPR instructions to read A window of the 32 previous results, W[i-1] to W[i-32] is saved in memory on the stack. This is best illustrated with a chart. Without vectorization, computing the rounds is like this (each "R" represents 1 round of SHA-1 computation): RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR With vectorization, 4 rounds can be computed in parallel: RRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRR Optimization: AVX The new "Sandy Bridge" microprocessor architecture, which supports AVX, allows another interesting optimization. SSSE3 instructions have two operands, a input and an output. AVX allows three operands, two inputs and an output. In many cases two SSSE3 instructions can be combined into one AVX instruction. The difference is best illustrated with an example. Consider these two instructions from the snippet above: pxor W_minus_16, W // W = (W[i-32:i-29] ^ W[i-28:i-25]) ^ W[i-16:i-13] pxor W_TMP, W // W = (W[i-32:i-29] ^ W[i-28:i-25] ^ W[i-16:i-13]) ^ W[i-6:i-3]) With AVX they can be combined in one instruction: vpxor W_minus_16, W, W_TMP // W = (W[i-32:i-29] ^ W[i-28:i-25] ^ W[i-16:i-13]) ^ W[i-6:i-3]) This optimization is also in Solaris, although Sandy Bridge-based systems aren't widely available yet. As an exercise for the reader, AVX also has 256-bit media registers, %ymm0 - %ymm15 (a superset of 128-bit %xmm0 - %xmm15). Can %ymm registers be used to parallelize the code even more? Optimization: Solaris-specific In addition to using the Intel code described above, I performed other minor optimizations to the Solaris SHA-1 code: Increased the digest(1) and mac(1) command's buffer size from 4K to 64K, as previously done for decrypt(1) and encrypt(1). This size is well suited for ZFS file systems, but helps for other file systems as well. Optimized encode functions, which byte swap the input and output data, to copy/byte-swap 4 or 8 bytes at-a-time instead of 1 byte-at-a-time. Enhanced the Solaris mdb(1) and kmdb(1) debuggers to display all 16 %xmm and %ymm registers (mdb "$x" command). Previously they only displayed the first 8 that are available in 32-bit mode. Can't optimize if you can't debug :-). Changed the SHA-1 code to allow processing in "chunks" greater than 2 Gigabytes (64-bits) Performance I measured performance on a Sun Ultra 27 (which has a Nehalem-class Xeon 5500 Intel W3570 microprocessor @3.2GHz). Turbo mode is disabled for consistent performance measurement. Graphs are better than words and numbers, so here they are: The first graph shows the Solaris digest(1) command before and after the optimizations discussed here, contained in libmd(3LIB). I ran the digest command on a half GByte file in swapfs (/tmp) and execution time decreased from 1.35 seconds to 0.98 seconds. The second graph shows the the results of an internal microbenchmark that uses the Solaris libpkcs11(3LIB) library. The operations are on a 128 byte buffer with 10,000 iterations. The results show operations increased from 320,000 to 416,000 operations per second. Finally the third graph shows the results of an internal kernel microbenchmark that uses the Solaris /kernel/crypto/amd64/sha1 module. The operations are on a 64Kbyte buffer with 100 iterations. third graph shows the results of an internal kernel microbenchmark that uses the Solaris /kernel/crypto/amd64/sha1 module. The operations are on a 64Kbyte buffer with 100 iterations. The results show for 1 kernel thread, operations increased from 410 to 600 MBytes/second. For 8 kernel threads, operations increase from 1540 to 1940 MBytes/second. Availability This code is in Solaris 11 FCS. It is available in the 64-bit libmd(3LIB) library for 64-bit programs and is in the Solaris kernel. You must be running hardware that supports Intel's SSSE3 instructions (for example, Intel Nehalem, Westmere, or Sandy Bridge microprocessor architectures). The easiest way to determine if SSSE3 is available is with the isainfo(1) command. For example, nehalem $ isainfo -v $ isainfo -v 64-bit amd64 applications sse4.2 sse4.1 ssse3 popcnt tscp ahf cx16 sse3 sse2 sse fxsr mmx cmov amd_sysc cx8 tsc fpu 32-bit i386 applications sse4.2 sse4.1 ssse3 popcnt tscp ahf cx16 sse3 sse2 sse fxsr mmx cmov sep cx8 tsc fpu If the output also shows "avx", the Solaris executes the even-more optimized 3-operand AVX instructions for SHA-1 mentioned above: sandybridge $ isainfo -v 64-bit amd64 applications avx xsave pclmulqdq aes sse4.2 sse4.1 ssse3 popcnt tscp ahf cx16 sse3 sse2 sse fxsr mmx cmov amd_sysc cx8 tsc fpu 32-bit i386 applications avx xsave pclmulqdq aes sse4.2 sse4.1 ssse3 popcnt tscp ahf cx16 sse3 sse2 sse fxsr mmx cmov sep cx8 tsc fpu No special configuration or setup is needed to take advantage of this code. Solaris libraries and kernel automatically determine if it's running on SSSE3 or AVX-capable machines and execute the correctly-tuned code for that microprocessor. Summary The Solaris 11 Crypto Framework, via the sha1 kernel module and libmd(3LIB) and libpkcs11(3LIB) libraries, incorporated a useful SHA-1 optimization from Intel for SSSE3-capable microprocessors. As with other Solaris optimizations, they come automatically "under the hood" with the current Solaris release. References "Improving the Performance of the Secure Hash Algorithm (SHA-1)" by Max Locktyukhin (Intel, March 2010). The source for these SHA-1 optimizations used in Solaris "SHA-1", Wikipedia Good overview of SHA-1 FIPS 180-1 SHA-1 standard (FIPS, 1995) NIST Comments on Cryptanalytic Attacks on SHA-1 (2005, revised 2006)

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  • Oracle Solaris 11 ZFS Lab for Openworld 2012

    - by user12626122
    Preface This is the content from the Oracle Openworld 2012 ZFS lab. It was well attended - the feedback was that it was a little short - thats probably because in writing it I bacame very time-concious after the ASM/ACFS on Solaris extravaganza I ran last year which was almost too long for mortal man to finish in the 1 hour session. Enjoy. Table of Contents Exercise Z.1: ZFS Pools Exercise Z.2: ZFS File Systems Exercise Z.3: ZFS Compression Exercise Z.4: ZFS Deduplication Exercise Z.5: ZFS Encryption Exercise Z.6: Solaris 11 Shadow Migration Introduction This set of exercises is designed to briefly demonstrate new features in Solaris 11 ZFS file system: Deduplication, Encryption and Shadow Migration. Also included is the creation of zpools and zfs file systems - the basic building blocks of the technology, and also Compression which is the compliment of Deduplication. The exercises are just introductions - you are referred to the ZFS Adminstration Manual for further information. From Solaris 11 onward the online manual pages consist of zpool(1M) and zfs(1M) with further feature-specific information in zfs_allow(1M), zfs_encrypt(1M) and zfs_share(1M). The lab is easily carried out in a VirtualBox running Solaris 11 with 6 virtual 3 Gb disks to play with. Exercise Z.1: ZFS Pools Task: You have several disks to use for your new file system. Create a new zpool and a file system within it. Lab: You will check the status of existing zpools, create your own pool and expand it. Your Solaris 11 installation already has a root ZFS pool. It contains the root file system. Check this: root@solaris:~# zpool list NAME SIZE ALLOC FREE CAP DEDUP HEALTH ALTROOT rpool 15.9G 6.62G 9.25G 41% 1.00x ONLINE - root@solaris:~# zpool status pool: rpool state: ONLINE scan: none requested config: NAME STATE READ WRITE CKSUM rpool ONLINE 0 0 0 c3t0d0s0 ONLINE 0 0 0 errors: No known data errors Note the disk device the root pool is on - c3t0d0s0 Now you will create your own ZFS pool. First you will check what disks are available: root@solaris:~# echo | format Searching for disks...done AVAILABLE DISK SELECTIONS: 0. c3t0d0 <ATA-VBOX HARDDISK-1.0 cyl 2085 alt 2 hd 255 sec 63> /pci@0,0/pci8086,2829@d/disk@0,0 1. c3t2d0 <ATA-VBOX HARDDISK-1.0 cyl 1534 alt 2 hd 128 sec 32> /pci@0,0/pci8086,2829@d/disk@2,0 2. c3t3d0 <ATA-VBOX HARDDISK-1.0 cyl 1534 alt 2 hd 128 sec 32> /pci@0,0/pci8086,2829@d/disk@3,0 3. c3t4d0 <ATA-VBOX HARDDISK-1.0 cyl 1534 alt 2 hd 128 sec 32> /pci@0,0/pci8086,2829@d/disk@4,0 4. c3t5d0 <ATA-VBOX HARDDISK-1.0 cyl 1534 alt 2 hd 128 sec 32> /pci@0,0/pci8086,2829@d/disk@5,0 5. c3t6d0 <ATA-VBOX HARDDISK-1.0 cyl 1534 alt 2 hd 128 sec 32> /pci@0,0/pci8086,2829@d/disk@6,0 6. c3t7d0 <ATA-VBOX HARDDISK-1.0 cyl 1534 alt 2 hd 128 sec 32> /pci@0,0/pci8086,2829@d/disk@7,0 Specify disk (enter its number): Specify disk (enter its number): The root disk is numbered 0. The others are free for use. Try creating a simple pool and observe the error message: root@solaris:~# zpool create mypool c3t2d0 c3t3d0 'mypool' successfully created, but with no redundancy; failure of one device will cause loss of the pool So destroy that pool and create a mirrored pool instead: root@solaris:~# zpool destroy mypool root@solaris:~# zpool create mypool mirror c3t2d0 c3t3d0 root@solaris:~# zpool status mypool pool: mypool state: ONLINE scan: none requested config: NAME STATE READ WRITE CKSUM mypool ONLINE 0 0 0 mirror-0 ONLINE 0 0 0 c3t2d0 ONLINE 0 0 0 c3t3d0 ONLINE 0 0 0 errors: No known data errors Back to topExercise Z.2: ZFS File Systems Task: You have to create file systems for later exercises. You can see that when a pool is created, a file system of the same name is created: root@solaris:~# zfs list NAME USED AVAIL REFER MOUNTPOINT mypool 86.5K 2.94G 31K /mypool Create your filesystems and mountpoints as follows: root@solaris:~# zfs create -o mountpoint=/data1 mypool/mydata1 The -o option sets the mount point and automatically creates the necessary directory. root@solaris:~# zfs list mypool/mydata1 NAME USED AVAIL REFER MOUNTPOINT mypool/mydata1 31K 2.94G 31K /data1 Back to top Exercise Z.3: ZFS Compression Task:Try out different forms of compression available in ZFS Lab:Create 2nd filesystem with compression, fill both file systems with the same data, observe results You can see from the zfs(1) manual page that there are several types of compression available to you, set with the property=value syntax: compression=on | off | lzjb | gzip | gzip-N | zle Controls the compression algorithm used for this dataset. The lzjb compression algorithm is optimized for performance while providing decent data compression. Setting compression to on uses the lzjb compression algorithm. The gzip compression algorithm uses the same compression as the gzip(1) command. You can specify the gzip level by using the value gzip-N where N is an integer from 1 (fastest) to 9 (best compression ratio). Currently, gzip is equivalent to gzip-6 (which is also the default for gzip(1)). Create a second filesystem with compression turned on. Note how you set and get your values separately: root@solaris:~# zfs create -o mountpoint=/data2 mypool/mydata2 root@solaris:~# zfs set compression=gzip-9 mypool/mydata2 root@solaris:~# zfs get compression mypool/mydata1 NAME PROPERTY VALUE SOURCE mypool/mydata1 compression off default root@solaris:~# zfs get compression mypool/mydata2 NAME PROPERTY VALUE SOURCE mypool/mydata2 compression gzip-9 local Now you can copy the contents of /usr/lib into both your normal and compressing filesystem and observe the results. Don't forget the dot or period (".") in the find(1) command below: root@solaris:~# cd /usr/lib root@solaris:/usr/lib# find . -print | cpio -pdv /data1 root@solaris:/usr/lib# find . -print | cpio -pdv /data2 The copy into the compressing file system takes longer - as it has to perform the compression but the results show the effect: root@solaris:/usr/lib# zfs list NAME USED AVAIL REFER MOUNTPOINT mypool 1.35G 1.59G 31K /mypool mypool/mydata1 1.01G 1.59G 1.01G /data1 mypool/mydata2 341M 1.59G 341M /data2 Note that the available space in the pool is shared amongst the file systems. This behavior can be modified using quotas and reservations which are not covered in this lab but are covered extensively in the ZFS Administrators Guide. Back to top Exercise Z.4: ZFS Deduplication The deduplication property is used to remove redundant data from a ZFS file system. With the property enabled duplicate data blocks are removed synchronously. The result is that only unique data is stored and common componenents are shared. Task:See how to implement deduplication and its effects Lab: You will create a ZFS file system with deduplication turned on and see if it reduces the amount of physical storage needed when we again fill it with a copy of /usr/lib. root@solaris:/usr/lib# zfs destroy mypool/mydata2 root@solaris:/usr/lib# zfs set dedup=on mypool/mydata1 root@solaris:/usr/lib# rm -rf /data1/* root@solaris:/usr/lib# mkdir /data1/2nd-copy root@solaris:/usr/lib# zfs list NAME USED AVAIL REFER MOUNTPOINT mypool 1.02M 2.94G 31K /mypool mypool/mydata1 43K 2.94G 43K /data1 root@solaris:/usr/lib# find . -print | cpio -pd /data1 2142768 blocks root@solaris:/usr/lib# zfs list NAME USED AVAIL REFER MOUNTPOINT mypool 1.02G 1.99G 31K /mypool mypool/mydata1 1.01G 1.99G 1.01G /data1 root@solaris:/usr/lib# find . -print | cpio -pd /data1/2nd-copy 2142768 blocks root@solaris:/usr/lib#zfs list NAME USED AVAIL REFER MOUNTPOINT mypool 1.99G 1.96G 31K /mypool mypool/mydata1 1.98G 1.96G 1.98G /data1 You could go on creating copies for quite a while...but you get the idea. Note that deduplication and compression can be combined: the compression acts on metadata. Deduplication works across file systems in a pool and there is a zpool-wide property dedupratio: root@solaris:/usr/lib# zpool get dedupratio mypool NAME PROPERTY VALUE SOURCE mypool dedupratio 4.30x - Deduplication can also be checked using "zpool list": root@solaris:/usr/lib# zpool list NAME SIZE ALLOC FREE CAP DEDUP HEALTH ALTROOT mypool 2.98G 1001M 2.01G 32% 4.30x ONLINE - rpool 15.9G 6.66G 9.21G 41% 1.00x ONLINE - Before moving on to the next topic, destroy that dataset and free up some space: root@solaris:~# zfs destroy mypool/mydata1 Back to top Exercise Z.5: ZFS Encryption Task: Encrypt sensitive data. Lab: Explore basic ZFS encryption. This lab only covers the basics of ZFS Encryption. In particular it does not cover various aspects of key management. Please see the ZFS Adminastrion Manual and the zfs_encrypt(1M) manual page for more detail on this functionality. Back to top root@solaris:~# zfs create -o encryption=on mypool/data2 Enter passphrase for 'mypool/data2': ******** Enter again: ******** root@solaris:~# Creation of a descendent dataset shows that encryption is inherited from the parent: root@solaris:~# zfs create mypool/data2/data3 root@solaris:~# zfs get -r encryption,keysource,keystatus,checksum mypool/data2 NAME PROPERTY VALUE SOURCE mypool/data2 encryption on local mypool/data2 keysource passphrase,prompt local mypool/data2 keystatus available - mypool/data2 checksum sha256-mac local mypool/data2/data3 encryption on inherited from mypool/data2 mypool/data2/data3 keysource passphrase,prompt inherited from mypool/data2 mypool/data2/data3 keystatus available - mypool/data2/data3 checksum sha256-mac inherited from mypool/data2 You will find the online manual page zfs_encrypt(1M) contains examples. In particular, if time permits during this lab session you may wish to explore the changing of a key using "zfs key -c mypool/data2". Exercise Z.6: Shadow Migration Shadow Migration allows you to migrate data from an old file system to a new file system while simultaneously allowing access and modification to the new file system during the process. You can use Shadow Migration to migrate a local or remote UFS or ZFS file system to a local file system. Task: You wish to migrate data from one file system (UFS, ZFS, VxFS) to ZFS while mainaining access to it. Lab: Create the infrastructure for shadow migration and transfer one file system into another. First create the file system you want to migrate root@solaris:~# zpool create oldstuff c3t4d0 root@solaris:~# zfs create oldstuff/forgotten Then populate it with some files: root@solaris:~# cd /var/adm root@solaris:/var/adm# find . -print | cpio -pdv /oldstuff/forgotten You need the shadow-migration package installed: root@solaris:~# pkg install shadow-migration Packages to install: 1 Create boot environment: No Create backup boot environment: No Services to change: 1 DOWNLOAD PKGS FILES XFER (MB) Completed 1/1 14/14 0.2/0.2 PHASE ACTIONS Install Phase 39/39 PHASE ITEMS Package State Update Phase 1/1 Image State Update Phase 2/2 You then enable the shadowd service: root@solaris:~# svcadm enable shadowd root@solaris:~# svcs shadowd STATE STIME FMRI online 7:16:09 svc:/system/filesystem/shadowd:default Set the filesystem to be migrated to read-only root@solaris:~# zfs set readonly=on oldstuff/forgotten Create a new zfs file system with the shadow property set to the file system to be migrated: root@solaris:~# zfs create -o shadow=file:///oldstuff/forgotten mypool/remembered Use the shadowstat(1M) command to see the progress of the migration: root@solaris:~# shadowstat EST BYTES BYTES ELAPSED DATASET XFRD LEFT ERRORS TIME mypool/remembered 92.5M - - 00:00:59 mypool/remembered 99.1M 302M - 00:01:09 mypool/remembered 109M 260M - 00:01:19 mypool/remembered 133M 304M - 00:01:29 mypool/remembered 149M 339M - 00:01:39 mypool/remembered 156M 86.4M - 00:01:49 mypool/remembered 156M 8E 29 (completed) Note that if you had created /mypool/remembered as encrypted, this would be the preferred method of encrypting existing data. Similarly for compressing or deduplicating existing data. The procedure for migrating a file system over NFS is similar - see the ZFS Administration manual. That concludes this lab session.

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  • Use depth bias for shadows in deferred shading

    - by cubrman
    We are building a deferred shading engine and we have a problem with shadows. To add shadows we use two maps: the first one stores the depth of the scene captured by the player's camera and the second one stores the depth of the scene captured by the light's camera. We then ran a shader that analyzes the two maps and outputs the third one with the ready shadow areas for the current frame. The problem we face is a classic one: Self-Shadowing: A standard way to solve this is to use the slope-scale depth bias and depth offsets, however as we are doing things in a deferred way we cannot employ this algorithm. Any attempts to set depth bias when capturing light's view depth produced no or unsatisfying results. So here is my question: MSDN article has a convoluted explanation of the slope-scale: bias = (m × SlopeScaleDepthBias) + DepthBias Where m is the maximum depth slope of the triangle being rendered, defined as: m = max( abs(delta z / delta x), abs(delta z / delta y) ) Could you explain how I can implement this algorithm manually in a shader? Maybe there are better ways to fix this problem for deferred shadows?

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  • Why is Quicksort called "Quicksort"?

    - by Darrel Hoffman
    The point of this question is not to debate the merits of this over any other sorting algorithm - certainly there are many other questions that do this. This question is about the name. Why is Quicksort called "Quicksort"? Sure, it's "quick", most of the time, but not always. The possibility of degenerating to O(N^2) is well known. There are various modifications to Quicksort that mitigate this problem, but the ones which bring the worst case down to a guaranteed O(n log n) aren't generally called Quicksort anymore. (e.g. Introsort). I just wonder why of all the well-known sorting algorithms, this is the only one deserving of the name "quick", which describes not how the algorithm works, but how fast it (usually) is. Mergesort is called that because it merges the data. Heapsort is called that because it uses a heap. Introsort gets its name from "Introspective", since it monitors its own performance to decide when to switch from Quicksort to Heapsort. Similarly for all the slower ones - Bubblesort, Insertion sort, Selection sort, etc. They're all named for how they work. The only other exception I can think of is "Bogosort", which is really just a joke that nobody ever actually uses in practice. Why isn't Quicksort called something more descriptive, like "Partition sort" or "Pivot sort", which describe what it actually does? It's not even a case of "got here first". Mergesort was developed 15 years before Quicksort. (1945 and 1960 respectively according to Wikipedia) I guess this is really more of a history question than a programming one. I'm just curious how it got the name - was it just good marketing?

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  • Why are SW engineering interviews disproportionately difficult?

    - by stackoverflowuser2010
    First, some background on me. I have a PhD in CS and have had jobs both as a software engineer and as an R&D research scientist, both at Very Large Corporations You Know Very Well. I recently changed jobs and interviewed for both types of jobs (as I have done in the past). My observation: SW engineer job interviews are way, way disproportionately more difficult than CS researcher job interviews, but the researcher job is higher paying, more competitive, more rewarding, more interesting, and has a higher upside. Here's a typical interview loop for researcher: Phone interview to see if my research is in alignment with the lab's researcher In-person, give presentation on my recent research for one hour (which represents maybe 9 month's worth of work), answer questions In-person one-on-one interviews with about 5 researchers, where they ask me very reasonable questions on my work/publications/patents, including: technical questions, where my work fits into related work, and how I can extend my work to new areas Here's a typical interview loop for SW engineer: Phone interview where I'm asked algorithm questions and maybe do some coding. Pretty standard. In-person interviews at the whiteboard where they drill the F*** out of you on esoteric C++ minutia (e.g. how does a polymorphic virtual function call work), algorithms (make all-pairs-shortest-path algorithm work for 1B vertices), system design (design a database load balancer), etc. This goes on for six or seven interviews. Ridiculous. Why would anyone be willing to put up with this? What is the point of asking about C++ trivia or writing code to prove yourself? Why not make the SE interview more like the researcher interview where you give a talk about what you've done? How are technical job interviews for other fields, like physics, chemistry, civil engineering, mechanical engineering?

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  • Functional programming and stateful algorithms

    - by bigstones
    I'm learning functional programming with Haskell. In the meantime I'm studying Automata theory and as the two seem to fit well together I'm writing a small library to play with automata. Here's the problem that made me ask the question. While studying a way to evaluate a state's reachability I got the idea that a simple recursive algorithm would be quite inefficient, because some paths might share some states and I might end up evaluating them more than once. For example, here, evaluating reachability of g from a, I'd have to exclude f both while checking the path through d and c: So my idea is that an algorithm working in parallel on many paths and updating a shared record of excluded states might be great, but that's too much for me. I've seen that in some simple recursion cases one can pass state as an argument, and that's what I have to do here, because I pass forward the list of states I've gone through to avoid loops. But is there a way to pass that list also backwards, like returning it in a tuple together with the boolean result of my canReach function? (although this feels a bit forced) Besides the validity of my example case, what other techniques are available to solve this kind of problems? I feel like these must be common enough that there have to be solutions like what happens with fold* or map. So far, reading learnyouahaskell.com I didn't find any, but consider I haven't touched monads yet. (if interested, I posted my code on codereview)

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