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  • Empty array (which's not empty)

    - by Brut4lity
    while($row = mysql_fetch_row($result)){ preg_match('#<span id="lblNumerZgloszenia" style="font-weight:bold;font-style:italic;">([^<]*)<\/span>#',$row[1],$matches); $query2 = 'UPDATE content_pl SET kategoria_data='.$matches[1].' WHERE id='.$row[0].';'; mysql_query($query2); } I'm doing this preg_match to get the span contents into $matches array. When I do a print_r($matches), it shows the right results but when I use $matches[1], it browser tells me that there is no such index.

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  • Looping through array values using JQuery and show them on separate lines

    - by user3192948
    I'm building a simple shopping cart where visitors can select a few items they want, click on the "Next" button, and see the confirmation list of things they just selected. I would like to have the confirmation list shown on each line for each item selected. HTML selection <div id="c_b"> <input type="checkbox" value="razor brand new razor that everyone loves, price at $.99" checked> <input type="checkbox" value="soap used soap for a nice public shower, good for your homies, price at $.99" checked> <input type="checkbox" value="manpacks ultimate choice, all in 1, price at $99"> </div> <button type='button' id='confirm'>Next</button> HTML confirmation list <div id='confirmation_list' style='display:none;'> <h2>You have selected item 1</h2> <h2>Your have selected item 2 </h2> </div> JS $(function(){ $('#confirm').click(function(){ var val = []; $(':checkbox:checked').each(function(i){ val[i] = $(this).val(); }); }); }); I ultimately want to replace the words 'Your have selected item 2' in h2s with the values selected from each check box. With the code above I'm able to collect the values of each checkbox into an array val, but having difficulty looping through and displaying them. Any advice would be appreciated.

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  • Need help finding a unique value in array

    - by bardockyo
    My code is complete minus one little flaw. It searches the array and prints out which values are unique, however it always counts the first entry as unique even if it is followed by the same value. Can anyone look at my code and tell me which part is messing this up because it is driving me crazy. #include <stdio.h> #define size 7 int main(void) { int array1[size], target, answer, found, x, k, prev, count =1, i; printf("Please input %d integers: ", size); scanf("%d", &target); for(x = 0; x < size; x++) { scanf("%d", &array1[x]); } prev = array1[0]; for (i = 1; i < size; i++) { if (array1[i] == prev) { count++; } else { if (count < 2) printf("%d=%d\n", prev, count); prev = array1[i]; count = 1; } } if (count < 2) { printf("%d=%d\n", prev, count); } return 0; }

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  • Performing an operation based on values within an array

    - by James W.
    I'm trying to figure out how to do operations based on values in an array. The values are taken from a string and inserted into the array e.g num = TextBox.Text.Split(' '); results = Convert.ToDouble(num[0]); for (int i = 0; i < num.Length - 1; i++) { if (num[i] == "+") { results += Convert.ToDouble(num[i++]); } ... } So based on this, let's say the TextBox string value was "1 + 2". So the array would be: ------------- | 1 | + | 2 | ------------- 0 1 2 (indexes) The part I'm having trouble with is Convert.ToDouble(num[i++]).. I've tried num[1] + 1, num[i + 1], etc I'm trying to figure out how to get it to perform the operation based on the first value and the value in the index after the operator. Which is the correct way to do something like this?

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  • What is the memoy size of a Java object array after it has been created?

    - by brenns10
    This probably doesn't even need asking, but I want to make sure I'm right on this. When you create an array of any object in Java like so: Object[] objArr = new Object[10]; The variable objArr is located in stack memory, and it points to a location in the heap where the array object is located. The size of that array in the heap is equal to a 12 byte object header + 4 (or 8, depending on the reference size) bytes * the number of entries in the array. Is this accurate? My question, then, is as follows. Since the array above is empty, does it take up 12 + 4*10 = 52 bytes of memory in the heap immediately after the execution of that line of code? Or does the JVM wait until you start putting things into the array before it instantiates it? Do the null references in the array take up space?

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  • size of array passed to C++ function ?

    - by user336994
    Hello, how can I get the size of an array that is passed to a function ? I have this code, but it is not working for me float verts[] = { -1.0,1.0,1.0, 1.0,1.0,1.0, 1.0,-1.0,1.0, -1.0,-1.0,1.0, -1.0,1.0,-1.0, 1.0,1.0,-1.0, 1.0,-1.0,-1.0, -1.0,-1.0,-1.0 }; void makeVectorData(float p_vertData[]) { int num = (sizeof(p_vertData)/sizeof(int)); cout << "output: " << num << endl; }; thanks,

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  • php in_array() inside a foreach

    - by 432skronker
    I am having issues with using in_array() inside a foreach loop. Not sure if this is even possible or if I am doing something ridiculous where there are better ways. What I want to do is go through all the items and if their item id matches one thats in the array, return true and add the price of the item to a runninng total. $price = 0; $result = false; $array = array(1533, 2343, 2333); foreach($order['items'] as $item){ if(in_array($item['Item'], $array)){ $result = true; $price += $item['Price']; } } **UPDATED** Here is the order array [items] => Array ( [0] => Array ( [Item] => 139957 [OrderID] => 16025 [SizeID] => 24 [Price] => 46.00 ) [1] => Array ( [Item] => 2343 [OrderID] => 16025 [SizeID] => 12 [Price] => 32.00 ) ) [data] => Array ( )

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  • this block of code going straight to break in java

    - by user2914851
    I have this block in a switch case statement that when selected, just breaks and presents me with the main menu again. System.out.println("Choose a competitor surname"); String competitorChoice2 = input.nextLine(); int lowestSpeed = Integer.MAX_VALUE; int highestSpeed = 0; for(int j = 0; j < clipArray.length; j++) { if(clipArray[j] != null) { if(competitorChoice2.equals(clipArray[j].getSurname())) { if(clipArray[j].getSpeed() > clipArray[highestSpeed].getSpeed()) { highestSpeed = j; } } } } for(int i = 0; i < clipArray.length; i++) { if(clipArray[i] != null) { if(competitorChoice2.equals(clipArray[i].getSurname())) { if(clipArray[i].getSpeed() < clipArray[lowestSpeed].getSpeed()) { lowestSpeed = i; } } } } for(int h = lowestSpeed; h < highestSpeed; h++ ) { System.out.println(""+clipArray[h].getLength()); } I have an array of objects and each object has a surname and a speed. I want the user to choose a surname and display the speeds of all of their clips from lowest to highest. when I select this option it just breaks and brings me back to the main menu

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  • Failing to use Array.Copy() in my WPF App

    - by Steven Wilson
    I am a C++ developer and recently started working on WPF. Well I am using Array.Copy() in my app and looks like I am not able to completely get the desired result. I had done in my C++ app as follows: static const signed char version[40] = { 'A', 'U', 'D', 'I', 'E', 'N', 'C', 'E', // name 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , // reserved, firmware size 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , // board number 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , // variant, version, serial 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 // date code, reserved }; unsigned char sendBuf[256] = {}; int memloc = 0; sendBuf[memloc++] = 0; sendBuf[memloc++] = 0; // fill in the audience header memcpy(sendBuf+memloc, version, 8); // the first 8 bytes memloc += 16; // the 8 copied, plus 8 reserved bytes I did the similar operation in my WPF (C#) app as follows: Byte[] sendBuf = new Byte[256]; char[] version = { 'A', 'U', 'D', 'I', 'E', 'N', 'C', 'E', // name '0', '0', '0', '0', '0', '0', '0', '0' , // reserved, firmware size '0', '0', '0', '0', '0', '0', '0', '0' , // board number '0', '0', '0', '0', '0', '0', '0', '0' , // variant, version, serial '0', '0', '0', '0', '0', '0', '0', '0' // date code, reserved }; // fill in the address to write to -- 0 sendBuf[memloc++] = 0; sendBuf[memloc++] = 0; // fill in the audience header Array.Copy(sendBuf + memloc, version, 8); // the first 8 bytes memloc += 16; But it throws me an error at Array.Copy(sendBuf + memloc, version, 8); as Operator '+' cannot be applied to operands of type 'byte[]' and 'int'. How can achieve this???? :) please help :)

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  • replacing space with %20

    - by Codenotguru
    The following program replaces all spaces with %20.the compilation works fine but the program terminates during the runtime.Any help??? #include<iostream> #include<string> using namespace std; void removeSpaces(string url){ int len=url.length(); int i,count=0; while(i<=len){ if(url[i]==' ') count++; i++; } int length2=len+(count*2); string newarr[length2]; for(int j=len-1;j>=0;j--){ if(url[j]==' ') { newarr[length2-1]='0'; newarr[length2-2]='2'; newarr[length2-3]='%'; length2=length2-3; } else { newarr[length2-1]=url[j]; length2=length2-1; } } cout<<"\nThe number of spaces in the url is:"<<count; cout<<"\nThe replaced url is:"<<newarr; } int main(){ string url="http://www.ya h o o.com/"; removeSpaces(url); }

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  • Storing images in file system and returning URLs or virtually resizing and returning byte arrays?

    - by ismaelf
    I need to create a REST web service to manage user submitted images and displaying them all in a website. There are multiple websites that are going to use this service to manage and display images. The requirements are to have 5 pre-defined image sizes available. The 2 options I see are the following: The web service will create the 5 images, store them in the file system and and store the URL's in the database when the user submits the image. When the image is requested, the web service will return an array of URLs. I see this option to be a little hard on the hard drive. The estimates are 10,000 users per site, and lets say, 100 sites. The heavy processing will be done when the user submits the image and each image is going to be pulled from the File System. The web service will store just the image that the user submits in the file system and it's URL in the database. When the user request images, the web service will get the info from the DB, load the image on memory, create its 5 instances and return an object with 5 image arrays (I will probably cache the arrays). This option is harder on the processor and memory. The heavy processing will be done when the images get requested. A plus I see for option 2 is that it will give me the option to rewrite the URL of the image and make them site dependent (prettier) than having a image repository for all websites. But this is not a big deal. What do you think of these options? Do you have any other suggestions?

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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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  • How do I prevent jagged edges alongside the surfaces of my 3d model?

    - by badcodenotreat
    Lets say I've implemented in openGL a crude model viewer with shading which renders a series of blocks, such that I have something that looks like this. http://i.imgur.com/TsF7K.jpg Whenever I rotate my model to the side, it causes an unwanted jagged effect along any surface with a steep viewing angle. http://i.imgur.com/Bgl9o.jpg I'm pretty sure this is due to the polygon offset I used to prevent z-fighting between the model and the wireframe, however I'm not able to find the factor/unit parameters in openGL which prevent this unwanted effect. what are the best values of factor and unit for glPolygonOffset to prevent this? would implementing anti-aliasing alleviate the problem? is the trade off in performance trivial/significant? is this perhaps a shading issue? should i try a solution along this line of thought?

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  • How do i change everything behind a certain point in a Jagged array?

    - by Jack Null
    Say I have a jagged array, and position 2,3 is taken by int 3. Every other spot is filled with int 0. How would I fill all the positions behind 2,3 with a 4? 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 to this: 4 4 4 4 4 4 4 4 4 4 4 4 4 3 0 0 0 0 0 0 0 Ive tried variations of this: int a = 2; int b = 3; for (int x = 0; x < a; x++) { for (int y = 0; y < board.space[b].Length; y++) { board.space[x][y] = 4; } }

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  • What tells initramfs or the Ubuntu Server boot process how to assemble RAID arrays?

    - by Brad
    The simple question: how does initramfs know how to assemble mdadm RAID arrays at startup? My problem: I boot my server and get: Gave up waiting for root device. ALERT! /dev/disk/by-uuid/[UUID] does not exist. Dropping to a shell! This happens because /dev/md0 (which is /boot, RAID 1) and /dev/md1 (which is /, RAID 5) are not being assembled correctly. What I get is /dev/md0 isn't assembled at all. /dev/md1 is assembled, but instead of using /dev/sda2, /dev/sdb2, /dev/sdc2, and /dev/sdd2, it uses /dev/sda, /dev/sdb, /dev/sdc, /dev/sdd. To fix this and boot my server I do: $(initramfs) mdadm --stop /dev/md1 $(initramfs) mdadm --assemble /dev/md0 /dev/sda1 /dev/sdb1 /dev/sdc1 /dev/sdd1 $(initramfs) mdadm --assemble /dev/md1 /dev/sda2 /dev/sdb2 /dev/sdc2 /dev/sdd2 $(initramfs) exit And it boots properly and everything works. Now I just need the RAID arrays to assemble properly at boot so I don't have to manually assemble them. I've checked /etc/mdadm/mdadm.conf and the UUIDs of the two arrays listed in that file match the UUIDs from $ mdadm --detail /dev/md[0,1]. Other details: Ubuntu 10.10, GRUB2, mdadm 2.6.7.1 UPDATE: I have a feeling it has to do with superblocks. $ mdadm --examine /dev/sda outputs the same thing as $ mdadm --examine /dev/sda2. $ mdadm --examine /dev/sda1 seems to be fine because it outputs information about /dev/md0. I don't know if this is the problem or not, but it seems to fit with /dev/md1 getting assembled with /dev/sd[abcd] instead of /dev/sd[abcd]2. I tried zeroing the superblock on /dev/sd[abcd]. This removed the superblock from /dev/sd[abcd]2 as well and prevented me from being able to assemble /dev/md1 at all. I had to $ mdadm --create to get it back. This also put the super blocks back to the way they were.

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  • SortedDictionary and SortedList

    - by Simon Cooper
    Apart from Dictionary<TKey, TValue>, there's two other dictionaries in the BCL - SortedDictionary<TKey, TValue> and SortedList<TKey, TValue>. On the face of it, these two classes do the same thing - provide an IDictionary<TKey, TValue> interface where the iterator returns the items sorted by the key. So what's the difference between them, and when should you use one rather than the other? (as in my previous post, I'll assume you have some basic algorithm & datastructure knowledge) SortedDictionary We'll first cover SortedDictionary. This is implemented as a special sort of binary tree called a red-black tree. Essentially, it's a binary tree that uses various constraints on how the nodes of the tree can be arranged to ensure the tree is always roughly balanced (for more gory algorithmical details, see the wikipedia link above). What I'm concerned about in this post is how the .NET SortedDictionary is actually implemented. In .NET 4, behind the scenes, the actual implementation of the tree is delegated to a SortedSet<KeyValuePair<TKey, TValue>>. One example tree might look like this: Each node in the above tree is stored as a separate SortedSet<T>.Node object (remember, in a SortedDictionary, T is instantiated to KeyValuePair<TKey, TValue>): class Node { public bool IsRed; public T Item; public SortedSet<T>.Node Left; public SortedSet<T>.Node Right; } The SortedSet only stores a reference to the root node; all the data in the tree is accessed by traversing the Left and Right node references until you reach the node you're looking for. Each individual node can be physically stored anywhere in memory; what's important is the relationship between the nodes. This is also why there is no constructor to SortedDictionary or SortedSet that takes an integer representing the capacity; there are no internal arrays that need to be created and resized. This may seen trivial, but it's an important distinction between SortedDictionary and SortedList that I'll cover later on. And that's pretty much it; it's a standard red-black tree. Plenty of webpages and datastructure books cover the algorithms behind the tree itself far better than I could. What's interesting is the comparions between SortedDictionary and SortedList, which I'll cover at the end. As a side point, SortedDictionary has existed in the BCL ever since .NET 2. That means that, all through .NET 2, 3, and 3.5, there has been a bona-fide sorted set class in the BCL (called TreeSet). However, it was internal, so it couldn't be used outside System.dll. Only in .NET 4 was this class exposed as SortedSet. SortedList Whereas SortedDictionary didn't use any backing arrays, SortedList does. It is implemented just as the name suggests; two arrays, one containing the keys, and one the values (I've just used random letters for the values): The items in the keys array are always guarenteed to be stored in sorted order, and the value corresponding to each key is stored in the same index as the key in the values array. In this example, the value for key item 5 is 'z', and for key item 8 is 'm'. Whenever an item is inserted or removed from the SortedList, a binary search is run on the keys array to find the correct index, then all the items in the arrays are shifted to accomodate the new or removed item. For example, if the key 3 was removed, a binary search would be run to find the array index the item was at, then everything above that index would be moved down by one: and then if the key/value pair {7, 'f'} was added, a binary search would be run on the keys to find the index to insert the new item, and everything above that index would be moved up to accomodate the new item: If another item was then added, both arrays would be resized (to a length of 10) before the new item was added to the arrays. As you can see, any insertions or removals in the middle of the list require a proportion of the array contents to be moved; an O(n) operation. However, if the insertion or removal is at the end of the array (ie the largest key), then it's only O(log n); the cost of the binary search to determine it does actually need to be added to the end (excluding the occasional O(n) cost of resizing the arrays to fit more items). As a side effect of using backing arrays, SortedList offers IList Keys and Values views that simply use the backing keys or values arrays, as well as various methods utilising the array index of stored items, which SortedDictionary does not (and cannot) offer. The Comparison So, when should you use one and not the other? Well, here's the important differences: Memory usage SortedDictionary and SortedList have got very different memory profiles. SortedDictionary... has a memory overhead of one object instance, a bool, and two references per item. On 64-bit systems, this adds up to ~40 bytes, not including the stored item and the reference to it from the Node object. stores the items in separate objects that can be spread all over the heap. This helps to keep memory fragmentation low, as the individual node objects can be allocated wherever there's a spare 60 bytes. In contrast, SortedList... has no additional overhead per item (only the reference to it in the array entries), however the backing arrays can be significantly larger than you need; every time the arrays are resized they double in size. That means that if you add 513 items to a SortedList, the backing arrays will each have a length of 1024. To conteract this, the TrimExcess method resizes the arrays back down to the actual size needed, or you can simply assign list.Capacity = list.Count. stores its items in a continuous block in memory. If the list stores thousands of items, this can cause significant problems with Large Object Heap memory fragmentation as the array resizes, which SortedDictionary doesn't have. Performance Operations on a SortedDictionary always have O(log n) performance, regardless of where in the collection you're adding or removing items. In contrast, SortedList has O(n) performance when you're altering the middle of the collection. If you're adding or removing from the end (ie the largest item), then performance is O(log n), same as SortedDictionary (in practice, it will likely be slightly faster, due to the array items all being in the same area in memory, also called locality of reference). So, when should you use one and not the other? As always with these sort of things, there are no hard-and-fast rules. But generally, if you: need to access items using their index within the collection are populating the dictionary all at once from sorted data aren't adding or removing keys once it's populated then use a SortedList. But if you: don't know how many items are going to be in the dictionary are populating the dictionary from random, unsorted data are adding & removing items randomly then use a SortedDictionary. The default (again, there's no definite rules on these sort of things!) should be to use SortedDictionary, unless there's a good reason to use SortedList, due to the bad performance of SortedList when altering the middle of the collection.

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  • Should I go vor Arrays or Objects in PHP in a CouchDB/Ajax app?

    - by karlthorwald
    I find myself converting between array and object all the time in PHP application that uses couchDB and Ajax. Of course I am also converting objects to JSON and back (for sometimes couchdb but mostly Ajax), but this is not so much disturbing my workflow. At the present I have php objects that are returned by the CouchDB modules I use and on the other hand I have the old habbit to return arrays like array("error"="not found","data"=$dataObj) from my functions. This leads to a mixed occurence of real php objects and nested arrays and I cast with (object) or (array) if necessary. The worst thing is that I know more or less by heart what a function returns, but not what type (array or object), so I often run into type errors. My plan is now to always cast arrays to objects before returning from a function. Of course this implies a lot of refactoring. Is this the right way to go? What about the conversion overhead? Other ideas or tips? Edit: Kenaniah's answer suggests I should go the other way, this would mean I'd cast everything to arrays. And for all the Ajax / JSON stuff and also for CouchDB I would use $myarray = json_decode($json_data,$assoc = false) Even more work to change all the CouchDB and Ajax functions but in the end I have better code.

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  • Should I go for Arrays or Objects in PHP in a CouchDB/Ajax app?

    - by karlthorwald
    I find myself converting between array and object all the time in PHP application that uses couchDB and Ajax. Of course I am also converting objects to JSON and back (for sometimes couchdb but mostly Ajax), but this is not so much disturbing my workflow. At the present I have php objects that are returned by the CouchDB modules I use and on the other hand I have the old habbit to return arrays like array("error"="not found","data"=$dataObj) from my functions. This leads to a mixed occurence of real php objects and nested arrays and I cast with (object) or (array) if necessary. The worst thing is that I know more or less by heart what a function returns, but not what type (array or object), so I often run into type errors. My plan is now to always cast arrays to objects before returning from a function. Of course this implies a lot of refactoring. Is this the right way to go? What about the conversion overhead? Other ideas or tips? Edit: Kenaniah's answer suggests I should go the other way, this would mean I'd cast everything to arrays. And for all the Ajax / JSON stuff and also for CouchDB I would use $myarray = json_decode($json_data,$assoc = true); //EDIT: changed to true, whcih is what I really meant Even more work to change all the CouchDB and Ajax functions but in the end I have better code.

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  • How do I count how many arrays have the same name within a multidimensional array with php?

    - by zeckdude
    I have a multidimensional array, and I would have multiple arrays within it. Some of those arrays contain multiple arrays within them as well, and I would like to count how many arrays are within the second array(the date). This is an example of the structure of the multidimensional array: $_SESSION['final_shipping'][04/03/2010][book] $_SESSION['final_shipping'][04/12/2010][magazine] $_SESSION['final_shipping'][04/12/2010][cd] This is the foreach statement I am currently using to count how many of the second array(the one with the dates) exists. foreach($_SESSION['final_shipping'] as $date_key => $date_value) { foreach ($date_value as $product_key => $product_value) { echo 'There are ' . count($date_key) . ' of the ' . $date_key . ' selection.<br/>'; } } It is currently outputting this: There are 1 of the 04/03/2010 selection. There are 1 of the 04/12/2010 selection. There are 1 of the 04/12/2010 selection. I would like it to output this: There are 1 of the 04/03/2010 selection. There are 2 of the 04/12/2010 selection.

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  • How John Got 15x Improvement Without Really Trying

    - by rchrd
    The following article was published on a Sun Microsystems website a number of years ago by John Feo. It is still useful and worth preserving. So I'm republishing it here.  How I Got 15x Improvement Without Really Trying John Feo, Sun Microsystems Taking ten "personal" program codes used in scientific and engineering research, the author was able to get from 2 to 15 times performance improvement easily by applying some simple general optimization techniques. Introduction Scientific research based on computer simulation depends on the simulation for advancement. The research can advance only as fast as the computational codes can execute. The codes' efficiency determines both the rate and quality of results. In the same amount of time, a faster program can generate more results and can carry out a more detailed simulation of physical phenomena than a slower program. Highly optimized programs help science advance quickly and insure that monies supporting scientific research are used as effectively as possible. Scientific computer codes divide into three broad categories: ISV, community, and personal. ISV codes are large, mature production codes developed and sold commercially. The codes improve slowly over time both in methods and capabilities, and they are well tuned for most vendor platforms. Since the codes are mature and complex, there are few opportunities to improve their performance solely through code optimization. Improvements of 10% to 15% are typical. Examples of ISV codes are DYNA3D, Gaussian, and Nastran. Community codes are non-commercial production codes used by a particular research field. Generally, they are developed and distributed by a single academic or research institution with assistance from the community. Most users just run the codes, but some develop new methods and extensions that feed back into the general release. The codes are available on most vendor platforms. Since these codes are younger than ISV codes, there are more opportunities to optimize the source code. Improvements of 50% are not unusual. Examples of community codes are AMBER, CHARM, BLAST, and FASTA. Personal codes are those written by single users or small research groups for their own use. These codes are not distributed, but may be passed from professor-to-student or student-to-student over several years. They form the primordial ocean of applications from which community and ISV codes emerge. Government research grants pay for the development of most personal codes. This paper reports on the nature and performance of this class of codes. Over the last year, I have looked at over two dozen personal codes from more than a dozen research institutions. The codes cover a variety of scientific fields, including astronomy, atmospheric sciences, bioinformatics, biology, chemistry, geology, and physics. The sources range from a few hundred lines to more than ten thousand lines, and are written in Fortran, Fortran 90, C, and C++. For the most part, the codes are modular, documented, and written in a clear, straightforward manner. They do not use complex language features, advanced data structures, programming tricks, or libraries. I had little trouble understanding what the codes did or how data structures were used. Most came with a makefile. Surprisingly, only one of the applications is parallel. All developers have access to parallel machines, so availability is not an issue. Several tried to parallelize their applications, but stopped after encountering difficulties. Lack of education and a perception that parallelism is difficult prevented most from trying. I parallelized several of the codes using OpenMP, and did not judge any of the codes as difficult to parallelize. Even more surprising than the lack of parallelism is the inefficiency of the codes. I was able to get large improvements in performance in a matter of a few days applying simple optimization techniques. Table 1 lists ten representative codes [names and affiliation are omitted to preserve anonymity]. Improvements on one processor range from 2x to 15.5x with a simple average of 4.75x. I did not use sophisticated performance tools or drill deep into the program's execution character as one would do when tuning ISV or community codes. Using only a profiler and source line timers, I identified inefficient sections of code and improved their performance by inspection. The changes were at a high level. I am sure there is another factor of 2 or 3 in each code, and more if the codes are parallelized. The study’s results show that personal scientific codes are running many times slower than they should and that the problem is pervasive. Computational scientists are not sloppy programmers; however, few are trained in the art of computer programming or code optimization. I found that most have a working knowledge of some programming language and standard software engineering practices; but they do not know, or think about, how to make their programs run faster. They simply do not know the standard techniques used to make codes run faster. In fact, they do not even perceive that such techniques exist. The case studies described in this paper show that applying simple, well known techniques can significantly increase the performance of personal codes. It is important that the scientific community and the Government agencies that support scientific research find ways to better educate academic scientific programmers. The inefficiency of their codes is so bad that it is retarding both the quality and progress of scientific research. # cacheperformance redundantoperations loopstructures performanceimprovement 1 x x 15.5 2 x 2.8 3 x x 2.5 4 x 2.1 5 x x 2.0 6 x 5.0 7 x 5.8 8 x 6.3 9 2.2 10 x x 3.3 Table 1 — Area of improvement and performance gains of 10 codes The remainder of the paper is organized as follows: sections 2, 3, and 4 discuss the three most common sources of inefficiencies in the codes studied. These are cache performance, redundant operations, and loop structures. Each section includes several examples. The last section summaries the work and suggests a possible solution to the issues raised. Optimizing cache performance Commodity microprocessor systems use caches to increase memory bandwidth and reduce memory latencies. Typical latencies from processor to L1, L2, local, and remote memory are 3, 10, 50, and 200 cycles, respectively. Moreover, bandwidth falls off dramatically as memory distances increase. Programs that do not use cache effectively run many times slower than programs that do. When optimizing for cache, the biggest performance gains are achieved by accessing data in cache order and reusing data to amortize the overhead of cache misses. Secondary considerations are prefetching, associativity, and replacement; however, the understanding and analysis required to optimize for the latter are probably beyond the capabilities of the non-expert. Much can be gained simply by accessing data in the correct order and maximizing data reuse. 6 out of the 10 codes studied here benefited from such high level optimizations. Array Accesses The most important cache optimization is the most basic: accessing Fortran array elements in column order and C array elements in row order. Four of the ten codes—1, 2, 4, and 10—got it wrong. Compilers will restructure nested loops to optimize cache performance, but may not do so if the loop structure is too complex, or the loop body includes conditionals, complex addressing, or function calls. In code 1, the compiler failed to invert a key loop because of complex addressing do I = 0, 1010, delta_x IM = I - delta_x IP = I + delta_x do J = 5, 995, delta_x JM = J - delta_x JP = J + delta_x T1 = CA1(IP, J) + CA1(I, JP) T2 = CA1(IM, J) + CA1(I, JM) S1 = T1 + T2 - 4 * CA1(I, J) CA(I, J) = CA1(I, J) + D * S1 end do end do In code 2, the culprit is conditionals do I = 1, N do J = 1, N If (IFLAG(I,J) .EQ. 0) then T1 = Value(I, J-1) T2 = Value(I-1, J) T3 = Value(I, J) T4 = Value(I+1, J) T5 = Value(I, J+1) Value(I,J) = 0.25 * (T1 + T2 + T5 + T4) Delta = ABS(T3 - Value(I,J)) If (Delta .GT. MaxDelta) MaxDelta = Delta endif enddo enddo I fixed both programs by inverting the loops by hand. Code 10 has three-dimensional arrays and triply nested loops. The structure of the most computationally intensive loops is too complex to invert automatically or by hand. The only practical solution is to transpose the arrays so that the dimension accessed by the innermost loop is in cache order. The arrays can be transposed at construction or prior to entering a computationally intensive section of code. The former requires all array references to be modified, while the latter is cost effective only if the cost of the transpose is amortized over many accesses. I used the second approach to optimize code 10. Code 5 has four-dimensional arrays and loops are nested four deep. For all of the reasons cited above the compiler is not able to restructure three key loops. Assume C arrays and let the four dimensions of the arrays be i, j, k, and l. In the original code, the index structure of the three loops is L1: for i L2: for i L3: for i for l for l for j for k for j for k for j for k for l So only L3 accesses array elements in cache order. L1 is a very complex loop—much too complex to invert. I brought the loop into cache alignment by transposing the second and fourth dimensions of the arrays. Since the code uses a macro to compute all array indexes, I effected the transpose at construction and changed the macro appropriately. The dimensions of the new arrays are now: i, l, k, and j. L3 is a simple loop and easily inverted. L2 has a loop-carried scalar dependence in k. By promoting the scalar name that carries the dependence to an array, I was able to invert the third and fourth subloops aligning the loop with cache. Code 5 is by far the most difficult of the four codes to optimize for array accesses; but the knowledge required to fix the problems is no more than that required for the other codes. I would judge this code at the limits of, but not beyond, the capabilities of appropriately trained computational scientists. Array Strides When a cache miss occurs, a line (64 bytes) rather than just one word is loaded into the cache. If data is accessed stride 1, than the cost of the miss is amortized over 8 words. Any stride other than one reduces the cost savings. Two of the ten codes studied suffered from non-unit strides. The codes represent two important classes of "strided" codes. Code 1 employs a multi-grid algorithm to reduce time to convergence. The grids are every tenth, fifth, second, and unit element. Since time to convergence is inversely proportional to the distance between elements, coarse grids converge quickly providing good starting values for finer grids. The better starting values further reduce the time to convergence. The downside is that grids of every nth element, n > 1, introduce non-unit strides into the computation. In the original code, much of the savings of the multi-grid algorithm were lost due to this problem. I eliminated the problem by compressing (copying) coarse grids into continuous memory, and rewriting the computation as a function of the compressed grid. On convergence, I copied the final values of the compressed grid back to the original grid. The savings gained from unit stride access of the compressed grid more than paid for the cost of copying. Using compressed grids, the loop from code 1 included in the previous section becomes do j = 1, GZ do i = 1, GZ T1 = CA(i+0, j-1) + CA(i-1, j+0) T4 = CA1(i+1, j+0) + CA1(i+0, j+1) S1 = T1 + T4 - 4 * CA1(i+0, j+0) CA(i+0, j+0) = CA1(i+0, j+0) + DD * S1 enddo enddo where CA and CA1 are compressed arrays of size GZ. Code 7 traverses a list of objects selecting objects for later processing. The labels of the selected objects are stored in an array. The selection step has unit stride, but the processing steps have irregular stride. A fix is to save the parameters of the selected objects in temporary arrays as they are selected, and pass the temporary arrays to the processing functions. The fix is practical if the same parameters are used in selection as in processing, or if processing comprises a series of distinct steps which use overlapping subsets of the parameters. Both conditions are true for code 7, so I achieved significant improvement by copying parameters to temporary arrays during selection. Data reuse In the previous sections, we optimized for spatial locality. It is also important to optimize for temporal locality. Once read, a datum should be used as much as possible before it is forced from cache. Loop fusion and loop unrolling are two techniques that increase temporal locality. Unfortunately, both techniques increase register pressure—as loop bodies become larger, the number of registers required to hold temporary values grows. Once register spilling occurs, any gains evaporate quickly. For multiprocessors with small register sets or small caches, the sweet spot can be very small. In the ten codes presented here, I found no opportunities for loop fusion and only two opportunities for loop unrolling (codes 1 and 3). In code 1, unrolling the outer and inner loop one iteration increases the number of result values computed by the loop body from 1 to 4, do J = 1, GZ-2, 2 do I = 1, GZ-2, 2 T1 = CA1(i+0, j-1) + CA1(i-1, j+0) T2 = CA1(i+1, j-1) + CA1(i+0, j+0) T3 = CA1(i+0, j+0) + CA1(i-1, j+1) T4 = CA1(i+1, j+0) + CA1(i+0, j+1) T5 = CA1(i+2, j+0) + CA1(i+1, j+1) T6 = CA1(i+1, j+1) + CA1(i+0, j+2) T7 = CA1(i+2, j+1) + CA1(i+1, j+2) S1 = T1 + T4 - 4 * CA1(i+0, j+0) S2 = T2 + T5 - 4 * CA1(i+1, j+0) S3 = T3 + T6 - 4 * CA1(i+0, j+1) S4 = T4 + T7 - 4 * CA1(i+1, j+1) CA(i+0, j+0) = CA1(i+0, j+0) + DD * S1 CA(i+1, j+0) = CA1(i+1, j+0) + DD * S2 CA(i+0, j+1) = CA1(i+0, j+1) + DD * S3 CA(i+1, j+1) = CA1(i+1, j+1) + DD * S4 enddo enddo The loop body executes 12 reads, whereas as the rolled loop shown in the previous section executes 20 reads to compute the same four values. In code 3, two loops are unrolled 8 times and one loop is unrolled 4 times. Here is the before for (k = 0; k < NK[u]; k++) { sum = 0.0; for (y = 0; y < NY; y++) { sum += W[y][u][k] * delta[y]; } backprop[i++]=sum; } and after code for (k = 0; k < KK - 8; k+=8) { sum0 = 0.0; sum1 = 0.0; sum2 = 0.0; sum3 = 0.0; sum4 = 0.0; sum5 = 0.0; sum6 = 0.0; sum7 = 0.0; for (y = 0; y < NY; y++) { sum0 += W[y][0][k+0] * delta[y]; sum1 += W[y][0][k+1] * delta[y]; sum2 += W[y][0][k+2] * delta[y]; sum3 += W[y][0][k+3] * delta[y]; sum4 += W[y][0][k+4] * delta[y]; sum5 += W[y][0][k+5] * delta[y]; sum6 += W[y][0][k+6] * delta[y]; sum7 += W[y][0][k+7] * delta[y]; } backprop[k+0] = sum0; backprop[k+1] = sum1; backprop[k+2] = sum2; backprop[k+3] = sum3; backprop[k+4] = sum4; backprop[k+5] = sum5; backprop[k+6] = sum6; backprop[k+7] = sum7; } for one of the loops unrolled 8 times. Optimizing for temporal locality is the most difficult optimization considered in this paper. The concepts are not difficult, but the sweet spot is small. Identifying where the program can benefit from loop unrolling or loop fusion is not trivial. Moreover, it takes some effort to get it right. Still, educating scientific programmers about temporal locality and teaching them how to optimize for it will pay dividends. Reducing instruction count Execution time is a function of instruction count. Reduce the count and you usually reduce the time. The best solution is to use a more efficient algorithm; that is, an algorithm whose order of complexity is smaller, that converges quicker, or is more accurate. Optimizing source code without changing the algorithm yields smaller, but still significant, gains. This paper considers only the latter because the intent is to study how much better codes can run if written by programmers schooled in basic code optimization techniques. The ten codes studied benefited from three types of "instruction reducing" optimizations. The two most prevalent were hoisting invariant memory and data operations out of inner loops. The third was eliminating unnecessary data copying. The nature of these inefficiencies is language dependent. Memory operations The semantics of C make it difficult for the compiler to determine all the invariant memory operations in a loop. The problem is particularly acute for loops in functions since the compiler may not know the values of the function's parameters at every call site when compiling the function. Most compilers support pragmas to help resolve ambiguities; however, these pragmas are not comprehensive and there is no standard syntax. To guarantee that invariant memory operations are not executed repetitively, the user has little choice but to hoist the operations by hand. The problem is not as severe in Fortran programs because in the absence of equivalence statements, it is a violation of the language's semantics for two names to share memory. Codes 3 and 5 are C programs. In both cases, the compiler did not hoist all invariant memory operations from inner loops. Consider the following loop from code 3 for (y = 0; y < NY; y++) { i = 0; for (u = 0; u < NU; u++) { for (k = 0; k < NK[u]; k++) { dW[y][u][k] += delta[y] * I1[i++]; } } } Since dW[y][u] can point to the same memory space as delta for one or more values of y and u, assignment to dW[y][u][k] may change the value of delta[y]. In reality, dW and delta do not overlap in memory, so I rewrote the loop as for (y = 0; y < NY; y++) { i = 0; Dy = delta[y]; for (u = 0; u < NU; u++) { for (k = 0; k < NK[u]; k++) { dW[y][u][k] += Dy * I1[i++]; } } } Failure to hoist invariant memory operations may be due to complex address calculations. If the compiler can not determine that the address calculation is invariant, then it can hoist neither the calculation nor the associated memory operations. As noted above, code 5 uses a macro to address four-dimensional arrays #define MAT4D(a,q,i,j,k) (double *)((a)->data + (q)*(a)->strides[0] + (i)*(a)->strides[3] + (j)*(a)->strides[2] + (k)*(a)->strides[1]) The macro is too complex for the compiler to understand and so, it does not identify any subexpressions as loop invariant. The simplest way to eliminate the address calculation from the innermost loop (over i) is to define a0 = MAT4D(a,q,0,j,k) before the loop and then replace all instances of *MAT4D(a,q,i,j,k) in the loop with a0[i] A similar problem appears in code 6, a Fortran program. The key loop in this program is do n1 = 1, nh nx1 = (n1 - 1) / nz + 1 nz1 = n1 - nz * (nx1 - 1) do n2 = 1, nh nx2 = (n2 - 1) / nz + 1 nz2 = n2 - nz * (nx2 - 1) ndx = nx2 - nx1 ndy = nz2 - nz1 gxx = grn(1,ndx,ndy) gyy = grn(2,ndx,ndy) gxy = grn(3,ndx,ndy) balance(n1,1) = balance(n1,1) + (force(n2,1) * gxx + force(n2,2) * gxy) * h1 balance(n1,2) = balance(n1,2) + (force(n2,1) * gxy + force(n2,2) * gyy)*h1 end do end do The programmer has written this loop well—there are no loop invariant operations with respect to n1 and n2. However, the loop resides within an iterative loop over time and the index calculations are independent with respect to time. Trading space for time, I precomputed the index values prior to the entering the time loop and stored the values in two arrays. I then replaced the index calculations with reads of the arrays. Data operations Ways to reduce data operations can appear in many forms. Implementing a more efficient algorithm produces the biggest gains. The closest I came to an algorithm change was in code 4. This code computes the inner product of K-vectors A(i) and B(j), 0 = i < N, 0 = j < M, for most values of i and j. Since the program computes most of the NM possible inner products, it is more efficient to compute all the inner products in one triply-nested loop rather than one at a time when needed. The savings accrue from reading A(i) once for all B(j) vectors and from loop unrolling. for (i = 0; i < N; i+=8) { for (j = 0; j < M; j++) { sum0 = 0.0; sum1 = 0.0; sum2 = 0.0; sum3 = 0.0; sum4 = 0.0; sum5 = 0.0; sum6 = 0.0; sum7 = 0.0; for (k = 0; k < K; k++) { sum0 += A[i+0][k] * B[j][k]; sum1 += A[i+1][k] * B[j][k]; sum2 += A[i+2][k] * B[j][k]; sum3 += A[i+3][k] * B[j][k]; sum4 += A[i+4][k] * B[j][k]; sum5 += A[i+5][k] * B[j][k]; sum6 += A[i+6][k] * B[j][k]; sum7 += A[i+7][k] * B[j][k]; } C[i+0][j] = sum0; C[i+1][j] = sum1; C[i+2][j] = sum2; C[i+3][j] = sum3; C[i+4][j] = sum4; C[i+5][j] = sum5; C[i+6][j] = sum6; C[i+7][j] = sum7; }} This change requires knowledge of a typical run; i.e., that most inner products are computed. The reasons for the change, however, derive from basic optimization concepts. It is the type of change easily made at development time by a knowledgeable programmer. In code 5, we have the data version of the index optimization in code 6. Here a very expensive computation is a function of the loop indices and so cannot be hoisted out of the loop; however, the computation is invariant with respect to an outer iterative loop over time. We can compute its value for each iteration of the computation loop prior to entering the time loop and save the values in an array. The increase in memory required to store the values is small in comparison to the large savings in time. The main loop in Code 8 is doubly nested. The inner loop includes a series of guarded computations; some are a function of the inner loop index but not the outer loop index while others are a function of the outer loop index but not the inner loop index for (j = 0; j < N; j++) { for (i = 0; i < M; i++) { r = i * hrmax; R = A[j]; temp = (PRM[3] == 0.0) ? 1.0 : pow(r, PRM[3]); high = temp * kcoeff * B[j] * PRM[2] * PRM[4]; low = high * PRM[6] * PRM[6] / (1.0 + pow(PRM[4] * PRM[6], 2.0)); kap = (R > PRM[6]) ? high * R * R / (1.0 + pow(PRM[4]*r, 2.0) : low * pow(R/PRM[6], PRM[5]); < rest of loop omitted > }} Note that the value of temp is invariant to j. Thus, we can hoist the computation for temp out of the loop and save its values in an array. for (i = 0; i < M; i++) { r = i * hrmax; TEMP[i] = pow(r, PRM[3]); } [N.B. – the case for PRM[3] = 0 is omitted and will be reintroduced later.] We now hoist out of the inner loop the computations invariant to i. Since the conditional guarding the value of kap is invariant to i, it behooves us to hoist the computation out of the inner loop, thereby executing the guard once rather than M times. The final version of the code is for (j = 0; j < N; j++) { R = rig[j] / 1000.; tmp1 = kcoeff * par[2] * beta[j] * par[4]; tmp2 = 1.0 + (par[4] * par[4] * par[6] * par[6]); tmp3 = 1.0 + (par[4] * par[4] * R * R); tmp4 = par[6] * par[6] / tmp2; tmp5 = R * R / tmp3; tmp6 = pow(R / par[6], par[5]); if ((par[3] == 0.0) && (R > par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * tmp5; } else if ((par[3] == 0.0) && (R <= par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * tmp4 * tmp6; } else if ((par[3] != 0.0) && (R > par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * TEMP[i] * tmp5; } else if ((par[3] != 0.0) && (R <= par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * TEMP[i] * tmp4 * tmp6; } for (i = 0; i < M; i++) { kap = KAP[i]; r = i * hrmax; < rest of loop omitted > } } Maybe not the prettiest piece of code, but certainly much more efficient than the original loop, Copy operations Several programs unnecessarily copy data from one data structure to another. This problem occurs in both Fortran and C programs, although it manifests itself differently in the two languages. Code 1 declares two arrays—one for old values and one for new values. At the end of each iteration, the array of new values is copied to the array of old values to reset the data structures for the next iteration. This problem occurs in Fortran programs not included in this study and in both Fortran 77 and Fortran 90 code. Introducing pointers to the arrays and swapping pointer values is an obvious way to eliminate the copying; but pointers is not a feature that many Fortran programmers know well or are comfortable using. An easy solution not involving pointers is to extend the dimension of the value array by 1 and use the last dimension to differentiate between arrays at different times. For example, if the data space is N x N, declare the array (N, N, 2). Then store the problem’s initial values in (_, _, 2) and define the scalar names new = 2 and old = 1. At the start of each iteration, swap old and new to reset the arrays. The old–new copy problem did not appear in any C program. In programs that had new and old values, the code swapped pointers to reset data structures. Where unnecessary coping did occur is in structure assignment and parameter passing. Structures in C are handled much like scalars. Assignment causes the data space of the right-hand name to be copied to the data space of the left-hand name. Similarly, when a structure is passed to a function, the data space of the actual parameter is copied to the data space of the formal parameter. If the structure is large and the assignment or function call is in an inner loop, then copying costs can grow quite large. While none of the ten programs considered here manifested this problem, it did occur in programs not included in the study. A simple fix is always to refer to structures via pointers. Optimizing loop structures Since scientific programs spend almost all their time in loops, efficient loops are the key to good performance. Conditionals, function calls, little instruction level parallelism, and large numbers of temporary values make it difficult for the compiler to generate tightly packed, highly efficient code. Conditionals and function calls introduce jumps that disrupt code flow. Users should eliminate or isolate conditionls to their own loops as much as possible. Often logical expressions can be substituted for if-then-else statements. For example, code 2 includes the following snippet MaxDelta = 0.0 do J = 1, N do I = 1, M < code omitted > Delta = abs(OldValue ? NewValue) if (Delta > MaxDelta) MaxDelta = Delta enddo enddo if (MaxDelta .gt. 0.001) goto 200 Since the only use of MaxDelta is to control the jump to 200 and all that matters is whether or not it is greater than 0.001, I made MaxDelta a boolean and rewrote the snippet as MaxDelta = .false. do J = 1, N do I = 1, M < code omitted > Delta = abs(OldValue ? NewValue) MaxDelta = MaxDelta .or. (Delta .gt. 0.001) enddo enddo if (MaxDelta) goto 200 thereby, eliminating the conditional expression from the inner loop. A microprocessor can execute many instructions per instruction cycle. Typically, it can execute one or more memory, floating point, integer, and jump operations. To be executed simultaneously, the operations must be independent. Thick loops tend to have more instruction level parallelism than thin loops. Moreover, they reduce memory traffice by maximizing data reuse. Loop unrolling and loop fusion are two techniques to increase the size of loop bodies. Several of the codes studied benefitted from loop unrolling, but none benefitted from loop fusion. This observation is not too surpising since it is the general tendency of programmers to write thick loops. As loops become thicker, the number of temporary values grows, increasing register pressure. If registers spill, then memory traffic increases and code flow is disrupted. A thick loop with many temporary values may execute slower than an equivalent series of thin loops. The biggest gain will be achieved if the thick loop can be split into a series of independent loops eliminating the need to write and read temporary arrays. I found such an occasion in code 10 where I split the loop do i = 1, n do j = 1, m A24(j,i)= S24(j,i) * T24(j,i) + S25(j,i) * U25(j,i) B24(j,i)= S24(j,i) * T25(j,i) + S25(j,i) * U24(j,i) A25(j,i)= S24(j,i) * C24(j,i) + S25(j,i) * V24(j,i) B25(j,i)= S24(j,i) * U25(j,i) + S25(j,i) * V25(j,i) C24(j,i)= S26(j,i) * T26(j,i) + S27(j,i) * U26(j,i) D24(j,i)= S26(j,i) * T27(j,i) + S27(j,i) * V26(j,i) C25(j,i)= S27(j,i) * S28(j,i) + S26(j,i) * U28(j,i) D25(j,i)= S27(j,i) * T28(j,i) + S26(j,i) * V28(j,i) end do end do into two disjoint loops do i = 1, n do j = 1, m A24(j,i)= S24(j,i) * T24(j,i) + S25(j,i) * U25(j,i) B24(j,i)= S24(j,i) * T25(j,i) + S25(j,i) * U24(j,i) A25(j,i)= S24(j,i) * C24(j,i) + S25(j,i) * V24(j,i) B25(j,i)= S24(j,i) * U25(j,i) + S25(j,i) * V25(j,i) end do end do do i = 1, n do j = 1, m C24(j,i)= S26(j,i) * T26(j,i) + S27(j,i) * U26(j,i) D24(j,i)= S26(j,i) * T27(j,i) + S27(j,i) * V26(j,i) C25(j,i)= S27(j,i) * S28(j,i) + S26(j,i) * U28(j,i) D25(j,i)= S27(j,i) * T28(j,i) + S26(j,i) * V28(j,i) end do end do Conclusions Over the course of the last year, I have had the opportunity to work with over two dozen academic scientific programmers at leading research universities. Their research interests span a broad range of scientific fields. Except for two programs that relied almost exclusively on library routines (matrix multiply and fast Fourier transform), I was able to improve significantly the single processor performance of all codes. Improvements range from 2x to 15.5x with a simple average of 4.75x. Changes to the source code were at a very high level. I did not use sophisticated techniques or programming tools to discover inefficiencies or effect the changes. Only one code was parallel despite the availability of parallel systems to all developers. Clearly, we have a problem—personal scientific research codes are highly inefficient and not running parallel. The developers are unaware of simple optimization techniques to make programs run faster. They lack education in the art of code optimization and parallel programming. I do not believe we can fix the problem by publishing additional books or training manuals. To date, the developers in questions have not studied the books or manual available, and are unlikely to do so in the future. Short courses are a possible solution, but I believe they are too concentrated to be much use. The general concepts can be taught in a three or four day course, but that is not enough time for students to practice what they learn and acquire the experience to apply and extend the concepts to their codes. Practice is the key to becoming proficient at optimization. I recommend that graduate students be required to take a semester length course in optimization and parallel programming. We would never give someone access to state-of-the-art scientific equipment costing hundreds of thousands of dollars without first requiring them to demonstrate that they know how to use the equipment. Yet the criterion for time on state-of-the-art supercomputers is at most an interesting project. Requestors are never asked to demonstrate that they know how to use the system, or can use the system effectively. A semester course would teach them the required skills. Government agencies that fund academic scientific research pay for most of the computer systems supporting scientific research as well as the development of most personal scientific codes. These agencies should require graduate schools to offer a course in optimization and parallel programming as a requirement for funding. About the Author John Feo received his Ph.D. in Computer Science from The University of Texas at Austin in 1986. After graduate school, Dr. Feo worked at Lawrence Livermore National Laboratory where he was the Group Leader of the Computer Research Group and principal investigator of the Sisal Language Project. In 1997, Dr. Feo joined Tera Computer Company where he was project manager for the MTA, and oversaw the programming and evaluation of the MTA at the San Diego Supercomputer Center. In 2000, Dr. Feo joined Sun Microsystems as an HPC application specialist. He works with university research groups to optimize and parallelize scientific codes. Dr. Feo has published over two dozen research articles in the areas of parallel parallel programming, parallel programming languages, and application performance.

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  • cPickle ImportError: No module named multiarray

    - by Rafal
    Hello, I'm using cPickle to save my Database into file. The code looks like that: def Save_DataBase(): import cPickle from scipy import * from numpy import * a=Results.VersionName #filename='D:/results/'+a[a.find('/')+1:-a.find('/')-2]+Results.AssType[:3]+str(random.randint(0,100))+Results.Distribution+".lft" filename='D:/results/pppp.lft' plik=open(filename,'w') DataOutput=[[[DataBase.Arrays.Nodes,DataBase.Arrays.Links,DataBase.Arrays.Turns,DataBase.Arrays.Connectors,DataBase.Arrays.Zones], [DataBase.Nodes.Data,DataBase.Links.Data,DataBase.Turns.Data,DataBase.OrigConnectors.Data,DataBase.DestConnectors.Data,DataBase.Zones.Data], [DataBase.Nodes.DictionaryPy2Vis,DataBase.Links.DictionaryPy2Vis,DataBase.Turns.DictionaryPy2Vis,DataBase.OrigConnectors.DictionaryPy2Vis,DataBase.DestConnectors.DictionaryPy2Vis,DataBase.Zones.DictionaryPy2Vis], [DataBase.Nodes.DictionaryVis2Py,DataBase.Links.DictionaryVis2Py,DataBase.Turns.DictionaryVis2Py,DataBase.OrigConnectors.DictionaryVis2Py,DataBase.DestConnectors.DictionaryVis2Py,DataBase.Zones.DictionaryVis2Py], [DataBase.Paths.List]],[Results.VersionName,Results.noZones,Results.noNodes,Results.noLinks,Results.noTurns,Results.noTrips, Results.Times.VersionLoad,Results.Times.GetData,Results.Times.GetCoords,Results.Times.CrossTheTime,Results.Times.Plot_Cylinder, Results.AssType,Results.AssParam,Results.tStart,Results.tEnd,Results.Distribution,Results.tVector]] cPickle.dump(DataOutput, plik, protocol=0) plik.close()` And it works fine. Most of my Database rows are lists of a lists, vecor-like, or array-like data sets. But now when I input data, an error occurs: def Load_DataBase(): import cPickle from scipy import * from numpy import * filename='D:/results/pppp.lft' plik= open(filename, 'rb') """ first cPickle load approach """ A= cPickle.load(plik) """ fail """ """ Another approach - data format exact as in Output step above , also fails""" [[[DataBase.Arrays.Nodes,DataBase.Arrays.Links,DataBase.Arrays.Turns,DataBase.Arrays.Connectors,DataBase.Arrays.Zones], [DataBase.Nodes.Data,DataBase.Links.Data,DataBase.Turns.Data,DataBase.OrigConnectors.Data,DataBase.DestConnectors.Data,DataBase.Zones.Data], [DataBase.Nodes.DictionaryPy2Vis,DataBase.Links.DictionaryPy2Vis,DataBase.Turns.DictionaryPy2Vis,DataBase.OrigConnectors.DictionaryPy2Vis,DataBase.DestConnectors.DictionaryPy2Vis,DataBase.Zones.DictionaryPy2Vis], [DataBase.Nodes.DictionaryVis2Py,DataBase.Links.DictionaryVis2Py,DataBase.Turns.DictionaryVis2Py,DataBase.OrigConnectors.DictionaryVis2Py,DataBase.DestConnectors.DictionaryVis2Py,DataBase.Zones.DictionaryVis2Py], [DataBase.Paths.List]],[Results.VersionName,Results.noZones,Results.noNodes,Results.noLinks,Results.noTurns,Results.noTrips, Results.Times.VersionLoad,Results.Times.GetData,Results.Times.GetCoords,Results.Times.CrossTheTime,Results.Times.Plot_Cylinder, Results.AssType,Results.AssParam,Results.tStart,Results.tEnd,Results.Distribution,Results.tVector]]= cPickle.load(plik)` Error is (in both cases): A= cPickle.load(plik) ImportError: No module named multiarray Any Ideas? PS.

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  • calloc v/s malloc and time efficiency

    - by yCalleecharan
    Hi, I've read with interest the post "c difference between malloc and calloc". I'm using malloc in my code and would like to know what difference I'll have using calloc instead. My present (pseudo)code with malloc: Scenario 1 int main() { allocate large arrays with malloc INITIALIZE ALL ARRAY ELEMENTS TO ZERO for loop //say 1000 times do something and write results to arrays end for loop FREE ARRAYS with free command } //end main If I use calloc instead of malloc, then I'll have: Scenario2 int main() { for loop //say 1000 times ALLOCATION OF ARRAYS WITH CALLOC do something and write results to arrays FREE ARRAYS with free command end for loop } //end main I have three questions: Which of the scenarios is more efficient if the arrays are very large? Which of the scenarios will be more time efficient if the arrays are very large? In both scenarios,I'm just writing to arrays in the sense that for any given iteration in the for loop, I'm writing each array sequentially from the first element to the last element. The important question: If I'm using malloc as in scenario 1, then is it necessary that I initialize the elements to zero? Say with malloc I have array z = [garbage1, garbage2, garbage 3]. For each iteration, I'm writing elements sequentially i.e. in the first iteration I get z =[some_result, garbage2, garbage3], in the second iteration I get in the first iteration I get z =[some_result, another_result, garbage3] and so on, then do I need specifically to initialize my arrays after malloc?

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  • What is good book for administration & configuration of Storage logical arrays?

    - by unknown (yahoo)
    I am looking for a book which can explain pros and cons of different combination of configurations/policies of storage Arrays and may also suggest some best practices for certain scenarios for e.g. when data availability & security is very important. There are a lot of "books for dummy" but they don't go in depth, I am a more of developer so I would like to understand how and why exactly it works beneath policies & configuration settings. I am working with EMC clarion logical array but I will have to work with EMC Symmetrix or NetApp or any other types of disk arrays.

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  • In python: how to apply itertools.product to elements of a list of lists

    - by Guilherme Rocha
    I have a list of arrays and I would like to get the cartesian product of the elements in the arrays. I will use an example to make this more concrete... itertools.product seems to do the trick but I am stuck in a little detail. arrays = [(-1,+1), (-2,+2), (-3,+3)]; If I do cp = list(itertools.product(arrays)); I get cp = cp0 = [((-1, 1),), ((-2, 2),), ((-3, 3),)] But what I want to get is cp1 = [(-1,-2,-3), (-1,-2,+3), (-1,+2,-3), (-1,+2,+3), ..., (+1,+2,-3), (+1,+2,+3)]. I have tried a few different things: cp = list(itertools.product(itertools.islice(arrays, len(arrays)))); cp = list(itertools.product(iter(arrays, len(arrays)))); They all gave me cp0 instead of cp1. Any ideas? Thanks in advance.

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  • JUnit Theories: Why can't I use Lists (instead of arrays) as DataPoints?

    - by MatrixFrog
    I've started using the new(ish) JUnit Theories feature for parameterizing tests. If your Theory is set up to take, for example, an Integer argument, the Theories test runner picks up any Integers marked with @DataPoint: @DataPoint public static Integer number = 0; as well as any Integers in arrays: @DataPoints public static Integer[] numbers = {1, 2, 3}; or even methods that return arrays like: @DataPoints public static Integer[] moreNumbers() { return new Integer[] {4, 5, 6};}; but not in Lists. The following does not work: @DataPoints public static List<Integer> numberList = Arrays.asList(7, 8, 9); Am I doing something wrong, or do Lists really not work? Was it a conscious design choice not to allow the use Lists as data points, or is that just a feature that hasn't been implemented yet? Are there plans to implement it in a future version of JUnit?

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