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  • Is this paragraph in Code Complete 2 backwards, or am I misunderstanding it?

    - by user828584
    In chapter 13, when talking about pointers, there is a paragraph: Sometimes, however, you would like to have the semantics of pass by reference—that is, that the passed object should not be altered—with the implementation of pass by value—that is, passing the actual object rather than a copy. It seems like the author made a mistake and mixed the two up. Is this true, or am I not understanding what he's saying correctly?

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  • Different bounding volumes for culling and collision detection

    - by Serthy
    Should an object in a 3D-engine use different bounding volumes for collision-detection (broad-phase) and culling? Basically class renderBounds and class physBounds versus class boundingVolume? Each of this classes then could either contain the same type of volumes (AABB's, kDOP's, sphere's etc.) or a special fitting one for the particular object. (note: without considering of using an external physics engine)

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  • What are atan and atan2 used for in games?

    - by kyrogue
    I am having some trouble understanding Math.tan() and Math.atan() and Math.atan2(). I have basic knowledge of trigonmetry but the usage of SIN, COS, and TAN etc for game development is very new to me. I am reading on some tutorials and I see that by using tangent we can get the angle in which one object needs to be rotated by how much to face another object for example my mouse. So why do we still need to use atan or atan2?

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  • How to detect collisions between sprite and a user generated shape of some sort?

    - by Huwell
    How to detect a collision between a sprite and a user generated shape of some sort. For example. There are some objects on the screen. The user takes their finger and draws an circle shape around a object (The selection rule is painting circle around the sprite, but the painting shapes may be various). I need to detect which object selected, which just like: (demo images) http://i52.tinypic.com/28h0t1g.png

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  • How can I model a pendulum blade?

    - by Micah Delane Bolen
    Like this one from Saw V: What primitive shape/s would you start out with? How would you transform the primitive shape/s to give it a nice, smooth, sharp blade on one side without distorting the entire object in a weird way? I tried starting out with a cylinder and then subtracting the top half using a duplicate cylinder and a difference modifier, but I ended up distorting the entire object when I tried to pull the "blade" edges together. I think I need to add lattices to smoothly "sharpen" the edge of the blade.

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  • How to make creating viewmodels at runtime less painfull

    - by Mr Happy
    I apologize for the long question, it reads a bit as a rant, but I promise it's not! I've summarized my question(s) below In the MVC world, things are straightforward. The Model has state, the View shows the Model, and the Controller does stuff to/with the Model (basically), a controller has no state. To do stuff the Controller has some dependencies on web services, repository, the lot. When you instantiate a controller you care about supplying those dependencies, nothing else. When you execute an action (method on Controller), you use those dependencies to retrieve or update the Model or calling some other domain service. If there's any context, say like some user wants to see the details of a particular item, you pass the Id of that item as parameter to the Action. Nowhere in the Controller is there any reference to any state. So far so good. Enter MVVM. I love WPF, I love data binding. I love frameworks that make data binding to ViewModels even easier (using Caliburn Micro a.t.m.). I feel things are less straightforward in this world though. Let's do the exercise again: the Model has state, the View shows the ViewModel, and the ViewModel does stuff to/with the Model (basically), a ViewModel does have state! (to clarify; maybe it delegates all the properties to one or more Models, but that means it must have a reference to the model one way or another, which is state in itself) To do stuff the ViewModel has some dependencies on web services, repository, the lot. When you instantiate a ViewModel you care about supplying those dependencies, but also the state. And this, ladies and gentlemen, annoys me to no end. Whenever you need to instantiate a ProductDetailsViewModel from the ProductSearchViewModel (from which you called the ProductSearchWebService which in turn returned IEnumerable<ProductDTO>, everybody still with me?), you can do one of these things: call new ProductDetailsViewModel(productDTO, _shoppingCartWebService /* dependcy */);, this is bad, imagine 3 more dependencies, this means the ProductSearchViewModel needs to take on those dependencies as well. Also changing the constructor is painfull. call _myInjectedProductDetailsViewModelFactory.Create().Initialize(productDTO);, the factory is just a Func, they are easily generated by most IoC frameworks. I think this is bad because Init methods are a leaky abstraction. You also can't use the readonly keyword for fields that are set in the Init method. I'm sure there are a few more reasons. call _myInjectedProductDetailsViewModelAbstractFactory.Create(productDTO); So... this is the pattern (abstract factory) that is usually recommended for this type of problem. I though it was genious since it satisfies my craving for static typing, until I actually started using it. The amount of boilerplate code is I think too much (you know, apart from the ridiculous variable names I get use). For each ViewModel that needs runtime parameters you'll get two extra files (factory interface and implementation), and you need to type the non-runtime dependencies like 4 extra times. And each time the dependencies change, you get to change it in the factory as well. It feels like I don't even use an DI container anymore. (I think Castle Windsor has some kind of solution for this [with it's own drawbacks, correct me if I'm wrong]). do something with anonymous types or dictionary. I like my static typing. So, yeah. Mixing state and behavior in this way creates a problem which don't exist at all in MVC. And I feel like there currently isn't a really adequate solution for this problem. Now I'd like to observe some things: People actually use MVVM. So they either don't care about all of the above, or they have some brilliant other solution. I haven't found an indepth example of MVVM with WPF. For example, the NDDD-sample project immensely helped me understand some DDD concepts. I'd really like it if someone could point me in the direction of something similar for MVVM/WPF. Maybe I'm doing MVVM all wrong and I should turn my design upside down. Maybe I shouldn't have this problem at all. Well I know other people have asked the same question so I think I'm not the only one. To summarize Am I correct to conclude that having the ViewModel being an integration point for both state and behavior is the reason for some difficulties with the MVVM pattern as a whole? Is using the abstract factory pattern the only/best way to instantiate a ViewModel in a statically typed way? Is there something like an in depth reference implementation available? Is having a lot of ViewModels with both state/behavior a design smell?

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  • rotating spheres

    - by Dave
    I want to continuously rotate 2 spheres, however the rotation does not seem to work. Here is my code: float angle = 0.0f; void light(){ glEnable(GL_LIGHTING); glEnable(GL_LIGHT0); glEnable(GL_LIGHT1); // Create light components GLfloat positionlight1[] = { 9.0, 5.0, 1.0, 0.0 }; GLfloat positionlight2[] = {0.2,2.5,1.3,0.0}; GLfloat light_ambient1[] = { 0.0, 0.0, 1.0, 1.0}; GLfloat light_diffuse[] = { 1.0, 1.0, 1.0, 1.0 }; glLightfv(GL_LIGHT0, GL_AMBIENT, light_ambient1); glLightfv(GL_LIGHT1, GL_DIFFUSE, light_diffuse); glLightfv(GL_LIGHT0, GL_POSITION, positionlight1); glLightfv(GL_LIGHT1, GL_POSITION, positionlight2); } void changeSize(int w, int h) { if (h==0) // Prevent A Divide By Zero By { h=1; // Making Height Equal One } glMatrixMode(GL_PROJECTION); // Select The Projection Matrix glLoadIdentity(); // Reset The Projection Matrix glViewport(0,0,w,h);// Reset The Current Viewport // Calculate The Aspect Ratio Of The Window gluPerspective(45.0f,(GLfloat)w/(GLfloat)h,0.1f,100.0f); glMatrixMode(GL_MODELVIEW); // Select The Modelview Matrix // Reset The Modelview Matrix } void renderScene(void) { glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT); glPushMatrix(); //set where to start the current object glTranslatef(0.0,1.2,-6); glRotatef(angle,0,1.2,-6); glutSolidSphere(1,50,50); glPopMatrix(); //end the current object transformations glPushMatrix(); //set where to start the current object glTranslatef(0.0,-2,-6); glRotatef(angle,0,-2,-6); glutSolidSphere(0.5,50,50); glPopMatrix(); //end the current object transformations angle=+0.1; glutSwapBuffers(); } int main(int argc, char **argv) { // init GLUT and create window glutInit(&argc, argv); glutInitDisplayMode(GLUT_DEPTH | GLUT_DOUBLE | GLUT_RGBA); glutInitWindowPosition(100,100); glutInitWindowSize(500,500); glutCreateWindow("Hello World"); // register callbacks light(); glutDisplayFunc(renderScene); glutReshapeFunc(changeSize); glutIdleFunc(renderScene); // enter GLUT event processing loop glutMainLoop(); return 1; } Graphicstest::Graphicstest(void) { } In the renderscene where i draw,translate and rotate my 2 spheres. It does not seem to rotate the spheres continuously. What am i doing wrong?

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  • I need beginner help on loading an image (2D). I have an error

    - by Seth Taddiken
    I keep getting a "NullReferenceExeption was unhandled" with "Object reference not set to an instance of an object." written under it. I have all of the images (png) correct with names and added to references. protected override void LoadContent() { spriteBatch = new SpriteBatch(GraphicsDevice); backGround = Content.Load("Cracked"); player1.playerBlock = Content.Load("square"); player2.playerBlock = Content.Load("square2"); }

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  • Why has the accessor methods from the JavaBean specification become the standard for Java development?

    - by Dakotah North
    The JavaBeans Specification describes a JavaBean as A Java Bean is a reusable software component that can be manipulated visually in a builder tool Since the majority of the lines of code that are written seem to have nothing to do with being manipulated visually in a builder tool, why has the JavaBean specification been the "way" to write object oriented code? I would like to forgo the traditional getter/setter in favor of Fluent Interfaces all throughout the code, not just in builders but fear doing so since this is traditionally not the way way object oriented code is written in Java.

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  • How to get a Read-Write Reference to Parent GameObject from a script component attached to it?

    - by onguarde
    I have a game object(object) with a script component(myscript) attached. I have a reference to myscript component through getComponent, and I want to change the transform of the gameObject the script is attached to. myscript.gameObject.transform = (new value); The above code gives me error, Property 'UnityEngine.GameObject.transform' is read only. Is there a way to get a read-write version?

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  • Question about design

    - by lukeluke
    Two fast questions about two design decisions: Suppose that you are checking collisions between game elements. When you find a collision between object 1 and object 2, do you play immediately a sound effect or do you insert it in a list and, in a later a stage, do you process all sound effects? Same question as above for user input. When the user presses key 'keypad left' do you insert the event in a queue and process it later or do you update character position immediately? Thx

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  • Do first-class methods exist?

    - by gdhoward
    Okay, I know first-class functions are cool, closures even better, etc. But is there any language with first-class methods? In my mind, I see a first-class method as an "object" that has both a function pointer and a pointer to a specific instance of the class/object, but the implementation doesn't matter. I just want to know if there is any language that uses them. And as a bonus, how were they implemented?

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  • Quickly Warning

    - by bradjones
    Every time I run my application I receive these warning errors can anyone tell me what is causing them? $ quickly run /usr/lib/python2.7/dist-packages/gi/overrides/Gtk.py:391: Warning: g_object_set_property: construct property "type" for object `Window' can't be set after construction Gtk.Window.__init__(self, type=type, **kwds) /usr/lib/python2.7/dist-packages/gi/overrides/Gtk.py:391: Warning: g_object_set_property: construct property "type" for object `AppWindow' can't be set after construction Gtk.Window.__init__(self, type=type, **kwds)

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  • How would I detect if two 2D arrays of any shape collided?

    - by user2104648
    Say there's two or more moveable objects of any shape in 2D plane, each object has its own 2D boolean array to act as a bounds box which can range from 10 to 100 pixels, the program then reads each pixel from a image that represents it, and appropriatly changes the array to true(pixel has a alpha more then 1) or false(pixel has a alpha less than one). Each time one of these objects moves, what would be the best accurate way to test if they hit another object in Java using as few APIs/libraries as possible?

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  • BinarySerialization size not consist?! (2 replies)

    Hello all, I have met something pretty odd. I am running a serialization on an object and not always the size of the output stream is the same. I even created a test that I am running the serialization in a loop, and each time I am running I have different results sizes in some point in the loop. This happens when I am using a object that was filled in the server side, and I get them thru WCF work...

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  • Requring static class setter to be called before constructor, bad design?

    - by roverred
    I have a class, say Foo, and every instance of Foo will need and contain the same List object, myList. Since every class instance will share the same List Object, I thought it would be good to make myList static and use a static function to set myList before the constructor is called. I was wondering if this was bad, because this requires the setter to be called before the constructor? If the person doesn't, the program will crash. Alternative way would be passing myList every time.

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  • Converting a PowerShell Script into a Module Part 2

    In this article the author explains how PSModuleInfo object for a module can be retrieved. Further, he shows how code can be injected into the module to manipulate the state of a module without having to reload it. He also explains how to directly set some metadata elements, like the module description, and some other PSModuleInfo object features.

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  • Game programming and quantity of timers

    - by andresjb
    I've made a simple 2D game engine using C# and DirectX and it's fully functional for the demo I made to test it. I have a Timer object that uses QueryPerformanceCounter and I don't know what's the better choice: use only one timer in the game loop to update everything in the game, or an independent timer in every object that needs one. My worry is that when I try to implement threads, what will happen with timers? What happens with the sync?

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  • BinarySerialization size not consist?! (2 replies)

    Hello all, I have met something pretty odd. I am running a serialization on an object and not always the size of the output stream is the same. I even created a test that I am running the serialization in a loop, and each time I am running I have different results sizes in some point in the loop. This happens when I am using a object that was filled in the server side, and I get them thru WCF work...

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  • Big Data – Buzz Words: What is MapReduce – Day 7 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned what is Hadoop. In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – MapReduce. What is MapReduce? MapReduce was designed by Google as a programming model for processing large data sets with a parallel, distributed algorithm on a cluster. Though, MapReduce was originally Google proprietary technology, it has been quite a generalized term in the recent time. MapReduce comprises a Map() and Reduce() procedures. Procedure Map() performance filtering and sorting operation on data where as procedure Reduce() performs a summary operation of the data. This model is based on modified concepts of the map and reduce functions commonly available in functional programing. The library where procedure Map() and Reduce() belongs is written in many different languages. The most popular free implementation of MapReduce is Apache Hadoop which we will explore tomorrow. Advantages of MapReduce Procedures The MapReduce Framework usually contains distributed servers and it runs various tasks in parallel to each other. There are various components which manages the communications between various nodes of the data and provides the high availability and fault tolerance. Programs written in MapReduce functional styles are automatically parallelized and executed on commodity machines. The MapReduce Framework takes care of the details of partitioning the data and executing the processes on distributed server on run time. During this process if there is any disaster the framework provides high availability and other available modes take care of the responsibility of the failed node. As you can clearly see more this entire MapReduce Frameworks provides much more than just Map() and Reduce() procedures; it provides scalability and fault tolerance as well. A typical implementation of the MapReduce Framework processes many petabytes of data and thousands of the processing machines. How do MapReduce Framework Works? A typical MapReduce Framework contains petabytes of the data and thousands of the nodes. Here is the basic explanation of the MapReduce Procedures which uses this massive commodity of the servers. Map() Procedure There is always a master node in this infrastructure which takes an input. Right after taking input master node divides it into smaller sub-inputs or sub-problems. These sub-problems are distributed to worker nodes. A worker node later processes them and does necessary analysis. Once the worker node completes the process with this sub-problem it returns it back to master node. Reduce() Procedure All the worker nodes return the answer to the sub-problem assigned to them to master node. The master node collects the answer and once again aggregate that in the form of the answer to the original big problem which was assigned master node. The MapReduce Framework does the above Map () and Reduce () procedure in the parallel and independent to each other. All the Map() procedures can run parallel to each other and once each worker node had completed their task they can send it back to master code to compile it with a single answer. This particular procedure can be very effective when it is implemented on a very large amount of data (Big Data). The MapReduce Framework has five different steps: Preparing Map() Input Executing User Provided Map() Code Shuffle Map Output to Reduce Processor Executing User Provided Reduce Code Producing the Final Output Here is the Dataflow of MapReduce Framework: Input Reader Map Function Partition Function Compare Function Reduce Function Output Writer In a future blog post of this 31 day series we will explore various components of MapReduce in Detail. MapReduce in a Single Statement MapReduce is equivalent to SELECT and GROUP BY of a relational database for a very large database. Tomorrow In tomorrow’s blog post we will discuss Buzz Word – HDFS. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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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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  • My Take on Hadoop World 2011

    - by Jean-Pierre Dijcks
    I’m sure some of you have read pieces about Hadoop World and I did see some headlines which were somewhat, shall we say, interesting? I thought the keynote by Larry Feinsmith of JP Morgan Chase & Co was one of the highlights of the conference for me. The reason was very simple, he addressed some real use cases outside of internet and ad platforms. The following are my notes, since the keynote was recorded I presume you can go and look at Hadoopworld.com at some point… On the use cases that were mentioned: ETL – how can I do complex data transformation at scale Doing Basel III liquidity analysis Private banking – transaction filtering to feed [relational] data marts Common Data Platform – a place to keep data that is (or will be) valuable some day, to someone, somewhere 360 Degree view of customers – become pro-active and look at events across lines of business. For example make sure the mortgage folks know about direct deposits being stopped into an account and ensure the bank is pro-active to service the customer Treasury and Security – Global Payment Hub [I think this is really consolidation of data to cross reference activity across business and geographies] Data Mining Bypass data engineering [I interpret this as running a lot of a large data set rather than on samples] Fraud prevention – work on event triggers, say a number of failed log-ins to the website. When they occur grab web logs, firewall logs and rules and start to figure out who is trying to log in. Is this me, who forget his password, or is it someone in some other country trying to guess passwords Trade quality analysis – do a batch analysis or all trades done and run them through an analysis or comparison pipeline One of the key requests – if you can say it like that – was for vendors and entrepreneurs to make sure that new tools work with existing tools. JPMC has a large footprint of BI Tools and Big Data reporting and tools should work with those tools, rather than be separate. Security and Entitlement – how to protect data within a large cluster from unwanted snooping was another topic that came up. I thought his Elephant ears graph was interesting (couldn’t actually read the points on it, but the concept certainly made some sense) and it was interesting – when asked to show hands – how the audience did not (!) think that RDBMS and Hadoop technology would overlap completely within a few years. Another interesting session was the session from Disney discussing how Disney is building a DaaS (Data as a Service) platform and how Hadoop processing capabilities are mixed with Database technologies. I thought this one of the best sessions I have seen in a long time. It discussed real use case, where problems existed, how they were solved and how Disney planned some of it. The planning focused on three things/phases: Determine the Strategy – Design a platform and evangelize this within the organization Focus on the people – Hire key people, grow and train the staff (and do not overload what you have with new things on top of their day-to-day job), leverage a partner with experience Work on Execution of the strategy – Implement the platform Hadoop next to the other technologies and work toward the DaaS platform This kind of fitted with some of the Linked-In comments, best summarized in “Think Platform – Think Hadoop”. In other words [my interpretation], step back and engineer a platform (like DaaS in the Disney example), then layer the rest of the solutions on top of this platform. One general observation, I got the impression that we have knowledge gaps left and right. On the one hand are people looking for more information and details on the Hadoop tools and languages. On the other I got the impression that the capabilities of today’s relational databases are underestimated. Mostly in terms of data volumes and parallel processing capabilities or things like commodity hardware scale-out models. All in all I liked this conference, it was great to chat with a wide range of people on Oracle big data, on big data, on use cases and all sorts of other stuff. Just hope they get a set of bigger rooms next time… and yes, I hope I’m going to be back next year!

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  • Investigating Strategies For Functional Decomposition

    - by Liam McLennan
    Introducing Functional Decomposition Before I begin I must apologise. I think I am using the term ‘functional decomposition’ loosely, and probably incorrectly. For the purpose of this article I use functional decomposition to mean the recursive splitting of a large problem into increasingly smaller ones, so that the one large problem may be solved by solving a set of smaller problems. The justification for functional decomposition is that the decomposed problem is more easily solved. As software developers we recognise that the smaller pieces are more easily tested, since they do less and are more cohesive. Functional decomposition is important to all scientific pursuits. Once we understand natural selection we can start to look for humanities ancestral species, once we understand the big bang we can trace our expanding universe back to its origin. Isaac Newton acknowledged the compositional nature of his scientific achievements: If I have seen further than others, it is by standing upon the shoulders of giants   The Two Strategies For Functional Decomposition of Computer Programs Private Methods When I was working on my undergraduate degree I was taught to functionally decompose problems by using private methods. Consider the problem of painting a house. The obvious solution is to solve the problem as a single unit: public void PaintAHouse() { // all the things required to paint a house ... } We decompose the problem by breaking it into parts: public void PaintAHouse() { PaintUndercoat(); PaintTopcoat(); } private void PaintUndercoat() { // everything required to paint the undercoat } private void PaintTopcoat() { // everything required to paint the topcoat } The problem can be recursively decomposed until a sufficiently granular level of detail is reached: public void PaintAHouse() { PaintUndercoat(); PaintTopcoat(); } private void PaintUndercoat() { prepareSurface(); fetchUndercoat(); paintUndercoat(); } private void PaintTopcoat() { fetchPaint(); paintTopcoat(); } According to Wikipedia, at least one computer programmer has referred to this process as “the art of subroutining”. The practical issues that I have encountered when using private methods for decomposition are: To preserve the top level API all of the steps must be private. This means that they can’t easily be tested. The private methods often have little cohesion except that they form part of the same solution. Decomposing to Classes The alternative is to decompose large problems into multiple classes, effectively using a class instead of each private method. The API delegates to related classes, so the API is not polluted by the sub-steps of the problem, and the steps can be easily tested because they are each in their own highly cohesive class. Additionally, I think that this technique facilitates better adherence to the Single Responsibility Principle, since each class can be decomposed until it has precisely one responsibility. Revisiting my previous example using class composition: public class HousePainter { private undercoatPainter = new UndercoatPainter(); private topcoatPainter = new TopcoatPainter(); public void PaintAHouse() { undercoatPainter.Paint(); topcoatPainter.Paint(); } } Summary When decomposing a problem there is more than one way to represent the sub-problems. Using private methods keeps the logic in one place and prevents a proliferation of classes (thereby following the four rules of simple design) but the class decomposition is more easily testable and more compatible with the Single Responsibility Principle.

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  • Faster Memory Allocation Using vmtasks

    - by Steve Sistare
    You may have noticed a new system process called "vmtasks" on Solaris 11 systems: % pgrep vmtasks 8 % prstat -p 8 PID USERNAME SIZE RSS STATE PRI NICE TIME CPU PROCESS/NLWP 8 root 0K 0K sleep 99 -20 9:10:59 0.0% vmtasks/32 What is vmtasks, and why should you care? In a nutshell, vmtasks accelerates creation, locking, and destruction of pages in shared memory segments. This is particularly helpful for locked memory, as creating a page of physical memory is much more expensive than creating a page of virtual memory. For example, an ISM segment (shmflag & SHM_SHARE_MMU) is locked in memory on the first shmat() call, and a DISM segment (shmflg & SHM_PAGEABLE) is locked using mlock() or memcntl(). Segment operations such as creation and locking are typically single threaded, performed by the thread making the system call. In many applications, the size of a shared memory segment is a large fraction of total physical memory, and the single-threaded initialization is a scalability bottleneck which increases application startup time. To break the bottleneck, we apply parallel processing, harnessing the power of the additional CPUs that are always present on modern platforms. For sufficiently large segments, as many of 16 threads of vmtasks are employed to assist an application thread during creation, locking, and destruction operations. The segment is implicitly divided at page boundaries, and each thread is given a chunk of pages to process. The per-page processing time can vary, so for dynamic load balancing, the number of chunks is greater than the number of threads, and threads grab chunks dynamically as they finish their work. Because the threads modify a single application address space in compressed time interval, contention on locks protecting VM data structures locks was a problem, and we had to re-scale a number of VM locks to get good parallel efficiency. The vmtasks process has 1 thread per CPU and may accelerate multiple segment operations simultaneously, but each operation gets at most 16 helper threads to avoid monopolizing CPU resources. We may reconsider this limit in the future. Acceleration using vmtasks is enabled out of the box, with no tuning required, and works for all Solaris platform architectures (SPARC sun4u, SPARC sun4v, x86). The following tables show the time to create + lock + destroy a large segment, normalized as milliseconds per gigabyte, before and after the introduction of vmtasks: ISM system ncpu before after speedup ------ ---- ------ ----- ------- x4600 32 1386 245 6X X7560 64 1016 153 7X M9000 512 1196 206 6X T5240 128 2506 234 11X T4-2 128 1197 107 11x DISM system ncpu before after speedup ------ ---- ------ ----- ------- x4600 32 1582 265 6X X7560 64 1116 158 7X M9000 512 1165 152 8X T5240 128 2796 198 14X (I am missing the data for T4 DISM, for no good reason; it works fine). The following table separates the creation and destruction times: ISM, T4-2 before after ------ ----- create 702 64 destroy 495 43 To put this in perspective, consider creating a 512 GB ISM segment on T4-2. Creating the segment would take 6 minutes with the old code, and only 33 seconds with the new. If this is your Oracle SGA, you save over 5 minutes when starting the database, and you also save when shutting it down prior to a restart. Those minutes go directly to your bottom line for service availability.

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  • Inkscape: what are "line" objects?

    - by Peter Mortensen
    What is a "line" object in Inkscape? Drawing lines in Inkscape is by using the tool "Draw Bezier curves and straight lines (Shift+F6)". This creates objects of another type, "path". Using Inkscape: is there a way to convert an object of type "line" into an object of the more general type "path"? I have imported a drawing (mostly lines, rectangles and text) that has been through Adobe Illustrator: originally made in Inkscape, imported into Illustrator, edited, saved from Illustrator as SVG, imported into Inkscape. Sample from the imported SVG file: <path id="path5855" stroke="#000000" d=" M320.198,275.935" /> <line fill="none" stroke="#000000" x1="348.553" y1="45.097" x2="348.553" y2="185.346" id="line3368" /> Update 1: I have inspected the original XML (SVG) file from 2006 and it does not contain any "line" XML tags. Thus it must be a crime of Adobe Illustrator. When a line is selected in this imported SVG file the bottom panel displays: "Line in root. Click selection to toggle scale/rotation handles.". When a line is selected that was drawn in Inkscape the bottom panel displays: "Path (2 nodes) in Layer 1. Click selection to toggle scale/rotation handles." What is the difference between "line" and "path"? Is "line" some kind of read-only/non-editable object? A generic term like "line" is not easy to use in search, but I have now found the definitions for "line" and "path": SVG line: http://www.w3schools.com/svg/svg_line.asp SVG path: http://www.w3schools.com/svg/svg_path.asp Platform: Inkscape v0.46 (2008-03-10), Windows XP 64 bit, 8 GB RAM.

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