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  • formula for best approximation for center of 2D rotation with small angles

    - by RocketSurgeon
    This is not a homework. I am asking to see if problem is classical (trivial) or non-trivial. It looks simple on a surface, and I hope it is truly a simple problem. Have N points (N = 2) with coordinates Xn, Yn on a surface of 2D solid body. Solid body has some small rotation (below Pi/180) combined with small shifts (below 1% of distance between any 2 points of N). Possibly some small deformation too (<<0.001%) Same N points have new coordinates named XXn, YYn Calculate with best approximation the location of center of rotation as point C with coordinates XXX, YYY. Thank you

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  • comparator with null values.

    - by pvgoddijn
    Hi, We have some code wich sorts a list of addresses based on the distance between their coordinates. this is done through collections.sort with a custom comparator. However from time to time an adress without coordinates is in the list causing a NullPointerException. My initial idea to fix this was to have the comparator return 0 as dististance for addresses where at least one of the coordinates is null. I fear this might lead to corruption of the order the 'valid' elements in the list. so is returning a '0' values for null data in a comparator ok, or is there a cleaner way to resolve this.

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  • Python Vector Class

    - by sfjedi
    I'm coming from a C# background where this stuff is super easy—trying to translate into Python for Maya. There's gotta' be a better way to do this. Basically, I'm looking to create a Vector class that will simply have x, y and z coordinates, but it would be ideal if this class returned a tuple with all 3 coordinates and if you could edit the values of this tuple through x, y and z properties, somehow. This is what I have so far, but there must be a better way to do this than using an exec statement, right? I hate using exec statements. class Vector(object): '''Creates a Maya vector/triple, having x, y and z coordinates as float values''' def __init__(self, x=0, y=0, z=0): self.x, self.y, self.z = x, y, z def attrsetter(attr): def set_float(self, value): setattr(self, attr, float(value)) return set_float for xyz in 'xyz': exec("%s = property(fget=attrgetter('_%s'), fset=attrsetter('_%s'))" % (xyz, xyz, xyz))

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  • Zip Code Radius Search question...

    - by KnockKnockWhosThere
    I'm wondering if it's possible to find all points by longitude and latitude within X radius of one point? So, if I provide a latitude/longitude of -76.0000, 38.0000, is it possible to simply find all the possible coordinates within (for example) a 10 mile radius of that? I know that there's a way to calculate the distance between two points, which is why I'm not clear as to whether this is possible... Because, it seems like you need to know the center coordinates (-76 and 38 in this case) as well as the coordinates of every other point in order to determine whether it falls within the specified radius... Is that right?

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  • Projecting a targetting ring using direct3d

    - by JohnB
    I'm trying to draw a "targetting ring" on the ground below a "unit" in a hobby 3d game I'm working on. Basically I want to project a bright red patterned ring onto the ground terrain below the unit. The only approach I can think of is this - Draw the world once as normal Draw the world a second time but in my vertex shader I have the world x,y,z coordinates of the vertex and I can pass in the coordinates of the highlighted unit - so I can calculate what the u,v coordinates in my project texture should be at that point in the world for that vertex. I'd then use the pixel shader to pick pixels from the target ring texture and blend them into the previously drawn world. I believe that should be easy, and should work but it involves me drawing the whole visible world twice as it's hard to determine exactly which polygons the targetting ring might fall onto. It seems a big overhead to draw the whole world twice, once for the normal lit textured ground, and then again just to draw the targetting ring. Is there a better approach that I'm missing?

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  • which euler rotations can i use ?

    - by melis
    i have two cartesian coordinates. There are xyz and BIG XYZ. I want to make these are paralel each other.forexample , x paralel to X ,y paralel to Y and z paralel to Z. I use rotation matris but I have a lot of different rotation matris . for example I have 3D point in xyz cartesien coordinates and its called A. and I want to change cartesien coordinate to BIG XYZ and find the same 3D point in this coordinates its called B.Until now it is okay. But when I used different rotational matris , points were changed.what can I do? Which Euler rotations can i use?

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  • How to get the row number of the QComboBox in QTableWidget

    - by dreamxiuhuishan
    Here is the code, but it dos not work. Who can create the code. Thanks very much! void add() { QComboBox *ziduan = new QComboBox; ziduan->addItem("??","nd"); int row =0; int col =1; QSignalMapper* signalMapper = new QSignalMapper(this); connect(ziduan, SIGNAL(currentIndexChanged(int)), signalMapper, SLOT(map())); signalMapper->setMapping(ziduan, QString("%1-%2").arg(row).arg(col)); connect(signalMapper, SIGNAL(mapped(const QString &)),this, SIGNAL(changeZiduan(const QString &))); } void sqlGenerator::changeZiduan(const QString &position) { QStringList coordinates = position.split("-"); int row = coordinates[0].toInt(); int col = coordinates[1].toInt(); }

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  • Programming user interface advice?

    - by onurozcelik
    Hi, In my project I going to generate a user interface through programming. Scalability of this UI is very important requirement. So far I am using two dimensional graphics for generating the UI. I think there may be different solutions but for the moment I know only two. First one is supplying X,Y coordinates of each two dimensional graphic on my UI.(I do not prefer this solution because I do not want to calculate X,Y coordinates of each graphic. For the moment I don't have a logic for doing this easily) Second one(which is currently I am using now) is using layouts which organizes its contents according to size of item. In this solution I don't have to calculate X,Y coordinates of each item.(Layout is doing this for me) But this approach may have its own pitfalls. I am very new to user interface programming. Can you give me advice about this issue?

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  • Gradient a Parallelogram

    - by nuclearpenguin
    I'm working in JavaScript drawing on a canvas, and have four coordinates to draw a parallelogram, called A, B, C, and D starting from the top-left, top-right, bottom-left, and bottom right, respectively. An example of some coordinates might be: A: (3, 3) B: (4, 3) C: (1, 0) D: (2, 0) I can draw the parallelogram just fine, but I would like to fill it in with a gradient. I want the gradient to fill in from left to right, but matching the angle of the shape. The library I use (CAKE) requires a start and stop coordinate for the gradient. My stop and start would be somewhere half way between A and C, and end somewhere half way between B and D. Of course, it is not simply EXACTLY half way because the angles at A, B, C, and D are not right angles. So given this information (the coordinates), how to I find the point on the line A - C to start, and the point on the line B - D to stop? Remember, I'm doing this in JavaScript, so I have some good Math tools at my disposal for calculation.

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  • Rotation towards an object in 3d space

    - by retoucher
    hello, i have two coordinates on a 2d plane in 3d space, and am trying to rotate one coordinate (a vector) to face the other coordinate. my vertical axis is the y-axis, so if both of the coordinates are located flat on the 2d plane, they would both have a y-axis of 0, and their x and z coordinates determine their position length/width-wise on the plane. right now, i'm calculating the angle like so (language agnostic): angle = atan2(z2-z1,x2-x1); and am rotating/translating in space like so: pushMatrix(); rotateY(angle); popMatrix(); this doesn't seem to be working though. are my calculations/process correct?

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  • Projection matrix + world plane ~> Homography from image plane to world plane

    - by B3ret
    I think I have my wires crossed on this, it should be quite easy. I have a projection matrix from world coordinates to image coordinates (4D homogeneous to 3D homgeneous), and therefore I also have the inverse projection matrix from image coordinates to world "rays". I want to project points of the image back onto a plane within the world (which is given of course as 4D homogeneous vector). The needed homography should be uniquely identified, yet I can not figure out how to compute it. Of course I could also intersect the back-projected rays with the world plane, but this seems not a good way, knowing that there MUST be a homography doing this for me. Thanks in advance, Ben

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  • Android: who can help me with setting up this google maps class please??

    - by Capsud
    Hi, Firstly this has turned out to be quite a long post so please bear with me as its not too difficult but you may need to clarify something with me if i haven't explained it correctly. So with some help the other day from guys on this forum, i managed to partially set up my 'mapClass' class, but i'm having trouble with it and its not running correctly so i would like some help if possible. I will post the code below so you can see. What Ive got is a 'Dundrum' class which sets up the listView for an array of items. Then ive got a 'dundrumSelector' class which I use to set up the setOnClickListener() methods on the listItems and link them to their correct views. DundrumSelector class.. public static final int BUTTON1 = R.id.anandaAddressButton; public static final int BUTTON2 = R.id.bramblesCafeAddressButton; public static final int BUTTON3 = R.id.brannigansAddressButton; public void onCreate(Bundle savedInstanceState){ super.onCreate(savedInstanceState); int position = getIntent().getExtras().getInt("position"); if(position == 0){ setContentView(R.layout.ananda); }; if(position == 1){ setContentView(R.layout.bramblescafe); }; if(position == 2){ setContentView(R.layout.brannigans); Button anandabutton = (Button) findViewById(R.id.anandaAddressButton); anandabutton.setOnClickListener(new View.OnClickListener() { public void onClick(View view) { Intent myIntent = new Intent(view.getContext(),MapClass.class); myIntent.putExtra("button", BUTTON1); startActivityForResult(myIntent,0); } }); Button bramblesbutton = (Button) findViewById(R.id.bramblesCafeAddressButton); bramblesbutton.setOnClickListener(new View.OnClickListener() { public void onClick(View view) { Intent myIntent = new Intent(view.getContext(),MapClass.class); myIntent.putExtra("button", BUTTON2); startActivityForResult(myIntent, 0); } }); etc etc.... Then what i did was set up static ints to represent the buttons which you can see at the top of this class, the reason for this is because in my mapClass activity I just want to have one method, because the only thing that is varying is the coordinates to each location. ie. i dont want to have 100+ map classes essentially doing the same thing other than different coordinates into the method. So my map class is as follows... case DundrumSelector.BUTTON1: handleCoordinates("53.288719","-6.241179"); break; case DundrumSelector.BUTTON2: handleCoordinates("53.288719","-6.241179"); break; case DundrumSelector.BUTTON3: handleCoordinates("53.288719","-6.241179"); break; } } private void handleCoordinates(String l, String b){ mapView = (MapView) findViewById(R.id.mapView); LinearLayout zoomLayout = (LinearLayout)findViewById(R.id.zoom); View zoomView = mapView.getZoomControls(); zoomLayout.addView(zoomView, new LinearLayout.LayoutParams( LayoutParams.WRAP_CONTENT, LayoutParams.WRAP_CONTENT)); mapView.displayZoomControls(true); mc = mapView.getController(); String coordinates[] = {l, b}; double lat = Double.parseDouble(coordinates[0]); double lng = Double.parseDouble(coordinates[1]); p = new GeoPoint( (int) (lat*1E6), (int) (lng*1E6)); mc.animateTo(p); mc.setZoom(17); mapView.invalidate(); } Now this is where my problem is. The onClick() events don't even work from the listView to get into the correct views. I have to comment out the methods in 'DundrumSelector' before I can get into their views. And this is what I dont understand, firstly why wont the onClick() events work, because its not even on that next view where the map is. I know this is a very long post and it might be quite confusing so let me know if you want any clarification.. Just to recap, what i'm trying to do is just have one class that sets up the map coordinates, like what i'm trying to do in my 'mapClass'. Please can someone help or suggest another way of doing this! Thanks alot everyone for reading this.

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  • Cache for large read only database recommendation

    - by paddydub
    I am building site on with Spring, Hibernate and Mysql. The mysql database contains information on coordinates and locations etc, it is never updated only queried. The database contains 15000 rows of coordinates and 48000 rows of coordinate connections. Every time a request is processed, the application needs to read all these coordinates which is taking approx 3-4 seconds. I would like to set up a cache, to allow quick access to the data. I'm researching memcached at the moment, can you please advise if this would be my best option?

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  • Mixing Matplotlib patches with polar plot?

    - by Roger
    I'm trying to plot some data in polar coordinates, but I don't want the standard ticks, labels, axes, etc. that you get with the Matplotlib polar() function. All I want is the raw plot and nothing else, as I'm handling everything with manually drawn patches and lines. Here are the options I've considered: 1) Drawing the data with polar(), hiding the superfluous stuff (with ax.axes.get_xaxis().set_visible(False), etc.) and then drawing my own axes (with Line2D, Circle, etc.). The problem is when I call polar() and subsequently add a Circle patch, it's drawn in polar coordinates and ends up looking like an infinity symbol. Also zooming doesn't seem to work with the polar() function. 2) Skip the polar() function and somehow make my own polar plot manually using Line2D. The problem is I don't know how to make Line2D draw in polar coordinates and haven't figured out how to use a transform to do that. Any idea how I should proceed?

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  • GoogleMaps API v3 - Need help with two "click" event scenarios. Need similar functionality to v2 AP

    - by Nathan Raley
    In version 2 of the API the map click event returned an Overlay, LatLng, Overlaylatlng. I used this to create a generic map event that would either retrieve the coordinates of the Map click event, or return the coordinates of a Marker or other type of Overlay. Now that API v3 doesn't return the Overlay or Overlaylatlng during the map click event, how can I go about creating a generic "click" event for the map that works if the user clicks on a marker or overlay? I really don't want to create a click event for each marker I have on my page as I am creating anywhere from a handful to a couple thousand markers. Also, I had to create a custom ImageMapType in order to display the StreetViewOverlay like we could do in v2 of the API because I couldn't find anywhere that told me how to add the StreetViewOverlay without the pegman icon. How can I go about retrieving the LatLng coordinates of a click on this overlay type as well?

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  • jquery custom drag and drop

    - by samlochner
    I am trying to create functionality similar to drag and drop. I need to create my own as there will be some significant differences to the drag and drop in the jquery UI. I would like to have mousemove being called repeatedly at all times, and mousedown called every time the mouse is pressed. So I have the following code: $(document).bind('mousemove',function(e){ $("#coords").text("e.pageX: " + e.pageX + ", e.pageY: " + e.pageY); }); $(document).bind('mousedown',function(e){ }); ('coords' is the id of a div) As I move the mouse, coordinates are reported correctly in 'coords'. If I depress a mouse button and then move the mouse, coordinates are still reported correctly. But if I depress a mouse button on an image and then move the mouse, coordinates are reported correctly for a few sets, and then they seize up! Why is this happening and how can I fix it? Thanks, Sam

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  • Fast image coordinate lookup in Numpy

    - by victor
    I've got a big numpy array full of coordinates (about 400): [[102, 234], [304, 104], .... ] And a numpy 2d array my_map of size 800x800. What's the fastest way to look up the coordinates given in that array? I tried things like paletting as described in this post: http://opencvpython.blogspot.com/2012/06/fast-array-manipulation-in-numpy.html but couldn't get it to work. I was also thinking about turning each coordinate into a linear index of the map and then piping it straight into my_map like so: my_map[linearized_coords] but I couldn't get vectorize to properly translate the coordinates into a linear fashion. Any ideas?

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  • Calculating the angle between two points

    - by kingrichard2005
    I'm currently developing a simple 2D game for Android. I have a stationary object that's situated in the center of the screen and I'm trying to get that object to rotate and point to the area on the screen that the user touches. I have the constant coordinates that represent the center of the screen and I can get the coordinates of the point that the user taps on. I'm using the formula outlined in this forum: How to get angle between two points? -It says as follows "If you want the the angle between the line defined by these two points and the horizontal axis: double angle = atan2(y2 - y1, x2 - x1) * 180 / PI;". -I implemented this, but I think the fact the I'm working in screen coordinates is causing a miscalculation, since the Y-coordinate is reversed. I'm not sure if this is the right way to go about it, any other thoughts or suggestions are appreciated.

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  • regex to break a string into "key" / "value" pairs when # of pairs is variable?

    - by user141146
    Hi, I'm using Ruby 1.9 and I'm wondering if there's a simple regex way to do this. I have many strings that look like some variation of this: str = "Allocation: Random, Control: Active Control, Endpoint Classification: Safety Study, Intervention Model: Parallel Assignment, Masking: Double Blind (Subject, Caregiver, Investigator, Outcomes Assessor), Primary Purpose: Treatment" The idea is that I'd like to break this string into its functional components Allocation: Random Control: Active Control Endpoint Classification: Safety Study Intervention Model: Parallel Assignment Masking: Double Blind (Subject, Caregiver, Investigator, Outcomes, Assessor) Primary Purpose: Treatment The "syntax" of the string is that there is a "key" which consists of one or more "words or other characters" (e.g. Intervention Model) followed by a colon (:). Each key has a corresponding "value" (e.g., Parallel Assignment) that immediately follows the colon (:)…The "value" consists of words, commas (whatever), but the end of the "value" is signaled by a comma. The # of key/value pairs is variable. I'm also assuming that colons (:) aren't allowed to be part of the "value" and that commas (,) aren't allowed to be part of the "key". One would think that there is a "regexy" way to break this into its component pieces, but my attempt at making an appropriate matching regex only picks up the first key/value pair and I'm not sure how to capture the others. Any thoughts on how to capture the other matches? regex = /(([^,]+?): ([^:]+?,))+?/ => /(([^,]+?): ([^:]+?,))+?/ irb(main):139:0> str = "Allocation: Random, Control: Active Control, Endpoint Classification: Safety Study, Intervention Model: Parallel Assignment, Masking: Double Blind (Subject, Caregiver, Investigator, Outcomes Assessor), Primary Purpose: Treatment" => "Allocation: Random, Control: Active Control, Endpoint Classification: Safety Study, Intervention Model: Parallel Assignment, Masking: Double Blind (Subject, Caregiver, Investigator, Outcomes Assessor), Primary Purpose: Treatment" irb(main):140:0> str.match regex => #<MatchData "Allocation: Random," 1:"Allocation: Random," 2:"Allocation" 3:" Random,"> irb(main):141:0> $1 => "Allocation: Random," irb(main):142:0> $2 => "Allocation" irb(main):143:0> $3 => " Random," irb(main):144:0> $4 => nil

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  • Understanding G1 GC Logs

    - by poonam
    The purpose of this post is to explain the meaning of GC logs generated with some tracing and diagnostic options for G1 GC. We will take a look at the output generated with PrintGCDetails which is a product flag and provides the most detailed level of information. Along with that, we will also look at the output of two diagnostic flags that get enabled with -XX:+UnlockDiagnosticVMOptions option - G1PrintRegionLivenessInfo that prints the occupancy and the amount of space used by live objects in each region at the end of the marking cycle and G1PrintHeapRegions that provides detailed information on the heap regions being allocated and reclaimed. We will be looking at the logs generated with JDK 1.7.0_04 using these options. Option -XX:+PrintGCDetails Here's a sample log of G1 collection generated with PrintGCDetails. 0.522: [GC pause (young), 0.15877971 secs] [Parallel Time: 157.1 ms] [GC Worker Start (ms): 522.1 522.2 522.2 522.2 Avg: 522.2, Min: 522.1, Max: 522.2, Diff: 0.1] [Ext Root Scanning (ms): 1.6 1.5 1.6 1.9 Avg: 1.7, Min: 1.5, Max: 1.9, Diff: 0.4] [Update RS (ms): 38.7 38.8 50.6 37.3 Avg: 41.3, Min: 37.3, Max: 50.6, Diff: 13.3] [Processed Buffers : 2 2 3 2 Sum: 9, Avg: 2, Min: 2, Max: 3, Diff: 1] [Scan RS (ms): 9.9 9.7 0.0 9.7 Avg: 7.3, Min: 0.0, Max: 9.9, Diff: 9.9] [Object Copy (ms): 106.7 106.8 104.6 107.9 Avg: 106.5, Min: 104.6, Max: 107.9, Diff: 3.3] [Termination (ms): 0.0 0.0 0.0 0.0 Avg: 0.0, Min: 0.0, Max: 0.0, Diff: 0.0] [Termination Attempts : 1 4 4 6 Sum: 15, Avg: 3, Min: 1, Max: 6, Diff: 5] [GC Worker End (ms): 679.1 679.1 679.1 679.1 Avg: 679.1, Min: 679.1, Max: 679.1, Diff: 0.1] [GC Worker (ms): 156.9 157.0 156.9 156.9 Avg: 156.9, Min: 156.9, Max: 157.0, Diff: 0.1] [GC Worker Other (ms): 0.3 0.3 0.3 0.3 Avg: 0.3, Min: 0.3, Max: 0.3, Diff: 0.0] [Clear CT: 0.1 ms] [Other: 1.5 ms] [Choose CSet: 0.0 ms] [Ref Proc: 0.3 ms] [Ref Enq: 0.0 ms] [Free CSet: 0.3 ms] [Eden: 12M(12M)->0B(10M) Survivors: 0B->2048K Heap: 13M(64M)->9739K(64M)] [Times: user=0.59 sys=0.02, real=0.16 secs] This is the typical log of an Evacuation Pause (G1 collection) in which live objects are copied from one set of regions (young OR young+old) to another set. It is a stop-the-world activity and all the application threads are stopped at a safepoint during this time. This pause is made up of several sub-tasks indicated by the indentation in the log entries. Here's is the top most line that gets printed for the Evacuation Pause. 0.522: [GC pause (young), 0.15877971 secs] This is the highest level information telling us that it is an Evacuation Pause that started at 0.522 secs from the start of the process, in which all the regions being evacuated are Young i.e. Eden and Survivor regions. This collection took 0.15877971 secs to finish. Evacuation Pauses can be mixed as well. In which case the set of regions selected include all of the young regions as well as some old regions. 1.730: [GC pause (mixed), 0.32714353 secs] Let's take a look at all the sub-tasks performed in this Evacuation Pause. [Parallel Time: 157.1 ms] Parallel Time is the total elapsed time spent by all the parallel GC worker threads. The following lines correspond to the parallel tasks performed by these worker threads in this total parallel time, which in this case is 157.1 ms. [GC Worker Start (ms): 522.1 522.2 522.2 522.2Avg: 522.2, Min: 522.1, Max: 522.2, Diff: 0.1] The first line tells us the start time of each of the worker thread in milliseconds. The start times are ordered with respect to the worker thread ids – thread 0 started at 522.1ms and thread 1 started at 522.2ms from the start of the process. The second line tells the Avg, Min, Max and Diff of the start times of all of the worker threads. [Ext Root Scanning (ms): 1.6 1.5 1.6 1.9 Avg: 1.7, Min: 1.5, Max: 1.9, Diff: 0.4] This gives us the time spent by each worker thread scanning the roots (globals, registers, thread stacks and VM data structures). Here, thread 0 took 1.6ms to perform the root scanning task and thread 1 took 1.5 ms. The second line clearly shows the Avg, Min, Max and Diff of the times spent by all the worker threads. [Update RS (ms): 38.7 38.8 50.6 37.3 Avg: 41.3, Min: 37.3, Max: 50.6, Diff: 13.3] Update RS gives us the time each thread spent in updating the Remembered Sets. Remembered Sets are the data structures that keep track of the references that point into a heap region. Mutator threads keep changing the object graph and thus the references that point into a particular region. We keep track of these changes in buffers called Update Buffers. The Update RS sub-task processes the update buffers that were not able to be processed concurrently, and updates the corresponding remembered sets of all regions. [Processed Buffers : 2 2 3 2Sum: 9, Avg: 2, Min: 2, Max: 3, Diff: 1] This tells us the number of Update Buffers (mentioned above) processed by each worker thread. [Scan RS (ms): 9.9 9.7 0.0 9.7 Avg: 7.3, Min: 0.0, Max: 9.9, Diff: 9.9] These are the times each worker thread had spent in scanning the Remembered Sets. Remembered Set of a region contains cards that correspond to the references pointing into that region. This phase scans those cards looking for the references pointing into all the regions of the collection set. [Object Copy (ms): 106.7 106.8 104.6 107.9 Avg: 106.5, Min: 104.6, Max: 107.9, Diff: 3.3] These are the times spent by each worker thread copying live objects from the regions in the Collection Set to the other regions. [Termination (ms): 0.0 0.0 0.0 0.0 Avg: 0.0, Min: 0.0, Max: 0.0, Diff: 0.0] Termination time is the time spent by the worker thread offering to terminate. But before terminating, it checks the work queues of other threads and if there are still object references in other work queues, it tries to steal object references, and if it succeeds in stealing a reference, it processes that and offers to terminate again. [Termination Attempts : 1 4 4 6 Sum: 15, Avg: 3, Min: 1, Max: 6, Diff: 5] This gives the number of times each thread has offered to terminate. [GC Worker End (ms): 679.1 679.1 679.1 679.1 Avg: 679.1, Min: 679.1, Max: 679.1, Diff: 0.1] These are the times in milliseconds at which each worker thread stopped. [GC Worker (ms): 156.9 157.0 156.9 156.9 Avg: 156.9, Min: 156.9, Max: 157.0, Diff: 0.1] These are the total lifetimes of each worker thread. [GC Worker Other (ms): 0.3 0.3 0.3 0.3Avg: 0.3, Min: 0.3, Max: 0.3, Diff: 0.0] These are the times that each worker thread spent in performing some other tasks that we have not accounted above for the total Parallel Time. [Clear CT: 0.1 ms] This is the time spent in clearing the Card Table. This task is performed in serial mode. [Other: 1.5 ms] Time spent in the some other tasks listed below. The following sub-tasks (which individually may be parallelized) are performed serially. [Choose CSet: 0.0 ms] Time spent in selecting the regions for the Collection Set. [Ref Proc: 0.3 ms] Total time spent in processing Reference objects. [Ref Enq: 0.0 ms] Time spent in enqueuing references to the ReferenceQueues. [Free CSet: 0.3 ms] Time spent in freeing the collection set data structure. [Eden: 12M(12M)->0B(13M) Survivors: 0B->2048K Heap: 14M(64M)->9739K(64M)] This line gives the details on the heap size changes with the Evacuation Pause. This shows that Eden had the occupancy of 12M and its capacity was also 12M before the collection. After the collection, its occupancy got reduced to 0 since everything is evacuated/promoted from Eden during a collection, and its target size grew to 13M. The new Eden capacity of 13M is not reserved at this point. This value is the target size of the Eden. Regions are added to Eden as the demand is made and when the added regions reach to the target size, we start the next collection. Similarly, Survivors had the occupancy of 0 bytes and it grew to 2048K after the collection. The total heap occupancy and capacity was 14M and 64M receptively before the collection and it became 9739K and 64M after the collection. Apart from the evacuation pauses, G1 also performs concurrent-marking to build the live data information of regions. 1.416: [GC pause (young) (initial-mark), 0.62417980 secs] ….... 2.042: [GC concurrent-root-region-scan-start] 2.067: [GC concurrent-root-region-scan-end, 0.0251507] 2.068: [GC concurrent-mark-start] 3.198: [GC concurrent-mark-reset-for-overflow] 4.053: [GC concurrent-mark-end, 1.9849672 sec] 4.055: [GC remark 4.055: [GC ref-proc, 0.0000254 secs], 0.0030184 secs] [Times: user=0.00 sys=0.00, real=0.00 secs] 4.088: [GC cleanup 117M->106M(138M), 0.0015198 secs] [Times: user=0.00 sys=0.00, real=0.00 secs] 4.090: [GC concurrent-cleanup-start] 4.091: [GC concurrent-cleanup-end, 0.0002721] The first phase of a marking cycle is Initial Marking where all the objects directly reachable from the roots are marked and this phase is piggy-backed on a fully young Evacuation Pause. 2.042: [GC concurrent-root-region-scan-start] This marks the start of a concurrent phase that scans the set of root-regions which are directly reachable from the survivors of the initial marking phase. 2.067: [GC concurrent-root-region-scan-end, 0.0251507] End of the concurrent root region scan phase and it lasted for 0.0251507 seconds. 2.068: [GC concurrent-mark-start] Start of the concurrent marking at 2.068 secs from the start of the process. 3.198: [GC concurrent-mark-reset-for-overflow] This indicates that the global marking stack had became full and there was an overflow of the stack. Concurrent marking detected this overflow and had to reset the data structures to start the marking again. 4.053: [GC concurrent-mark-end, 1.9849672 sec] End of the concurrent marking phase and it lasted for 1.9849672 seconds. 4.055: [GC remark 4.055: [GC ref-proc, 0.0000254 secs], 0.0030184 secs] This corresponds to the remark phase which is a stop-the-world phase. It completes the left over marking work (SATB buffers processing) from the previous phase. In this case, this phase took 0.0030184 secs and out of which 0.0000254 secs were spent on Reference processing. 4.088: [GC cleanup 117M->106M(138M), 0.0015198 secs] Cleanup phase which is again a stop-the-world phase. It goes through the marking information of all the regions, computes the live data information of each region, resets the marking data structures and sorts the regions according to their gc-efficiency. In this example, the total heap size is 138M and after the live data counting it was found that the total live data size dropped down from 117M to 106M. 4.090: [GC concurrent-cleanup-start] This concurrent cleanup phase frees up the regions that were found to be empty (didn't contain any live data) during the previous stop-the-world phase. 4.091: [GC concurrent-cleanup-end, 0.0002721] Concurrent cleanup phase took 0.0002721 secs to free up the empty regions. Option -XX:G1PrintRegionLivenessInfo Now, let's look at the output generated with the flag G1PrintRegionLivenessInfo. This is a diagnostic option and gets enabled with -XX:+UnlockDiagnosticVMOptions. G1PrintRegionLivenessInfo prints the live data information of each region during the Cleanup phase of the concurrent-marking cycle. 26.896: [GC cleanup ### PHASE Post-Marking @ 26.896### HEAP committed: 0x02e00000-0x0fe00000 reserved: 0x02e00000-0x12e00000 region-size: 1048576 Cleanup phase of the concurrent-marking cycle started at 26.896 secs from the start of the process and this live data information is being printed after the marking phase. Committed G1 heap ranges from 0x02e00000 to 0x0fe00000 and the total G1 heap reserved by JVM is from 0x02e00000 to 0x12e00000. Each region in the G1 heap is of size 1048576 bytes. ### type address-range used prev-live next-live gc-eff### (bytes) (bytes) (bytes) (bytes/ms) This is the header of the output that tells us about the type of the region, address-range of the region, used space in the region, live bytes in the region with respect to the previous marking cycle, live bytes in the region with respect to the current marking cycle and the GC efficiency of that region. ### FREE 0x02e00000-0x02f00000 0 0 0 0.0 This is a Free region. ### OLD 0x02f00000-0x03000000 1048576 1038592 1038592 0.0 Old region with address-range from 0x02f00000 to 0x03000000. Total used space in the region is 1048576 bytes, live bytes as per the previous marking cycle are 1038592 and live bytes with respect to the current marking cycle are also 1038592. The GC efficiency has been computed as 0. ### EDEN 0x03400000-0x03500000 20992 20992 20992 0.0 This is an Eden region. ### HUMS 0x0ae00000-0x0af00000 1048576 1048576 1048576 0.0### HUMC 0x0af00000-0x0b000000 1048576 1048576 1048576 0.0### HUMC 0x0b000000-0x0b100000 1048576 1048576 1048576 0.0### HUMC 0x0b100000-0x0b200000 1048576 1048576 1048576 0.0### HUMC 0x0b200000-0x0b300000 1048576 1048576 1048576 0.0### HUMC 0x0b300000-0x0b400000 1048576 1048576 1048576 0.0### HUMC 0x0b400000-0x0b500000 1001480 1001480 1001480 0.0 These are the continuous set of regions called Humongous regions for storing a large object. HUMS (Humongous starts) marks the start of the set of humongous regions and HUMC (Humongous continues) tags the subsequent regions of the humongous regions set. ### SURV 0x09300000-0x09400000 16384 16384 16384 0.0 This is a Survivor region. ### SUMMARY capacity: 208.00 MB used: 150.16 MB / 72.19 % prev-live: 149.78 MB / 72.01 % next-live: 142.82 MB / 68.66 % At the end, a summary is printed listing the capacity, the used space and the change in the liveness after the completion of concurrent marking. In this case, G1 heap capacity is 208MB, total used space is 150.16MB which is 72.19% of the total heap size, live data in the previous marking was 149.78MB which was 72.01% of the total heap size and the live data as per the current marking is 142.82MB which is 68.66% of the total heap size. Option -XX:+G1PrintHeapRegions G1PrintHeapRegions option logs the regions related events when regions are committed, allocated into or are reclaimed. COMMIT/UNCOMMIT events G1HR COMMIT [0x6e900000,0x6ea00000]G1HR COMMIT [0x6ea00000,0x6eb00000] Here, the heap is being initialized or expanded and the region (with bottom: 0x6eb00000 and end: 0x6ec00000) is being freshly committed. COMMIT events are always generated in order i.e. the next COMMIT event will always be for the uncommitted region with the lowest address. G1HR UNCOMMIT [0x72700000,0x72800000]G1HR UNCOMMIT [0x72600000,0x72700000] Opposite to COMMIT. The heap got shrunk at the end of a Full GC and the regions are being uncommitted. Like COMMIT, UNCOMMIT events are also generated in order i.e. the next UNCOMMIT event will always be for the committed region with the highest address. GC Cycle events G1HR #StartGC 7G1HR CSET 0x6e900000G1HR REUSE 0x70500000G1HR ALLOC(Old) 0x6f800000G1HR RETIRE 0x6f800000 0x6f821b20G1HR #EndGC 7 This shows start and end of an Evacuation pause. This event is followed by a GC counter tracking both evacuation pauses and Full GCs. Here, this is the 7th GC since the start of the process. G1HR #StartFullGC 17G1HR UNCOMMIT [0x6ed00000,0x6ee00000]G1HR POST-COMPACTION(Old) 0x6e800000 0x6e854f58G1HR #EndFullGC 17 Shows start and end of a Full GC. This event is also followed by the same GC counter as above. This is the 17th GC since the start of the process. ALLOC events G1HR ALLOC(Eden) 0x6e800000 The region with bottom 0x6e800000 just started being used for allocation. In this case it is an Eden region and allocated into by a mutator thread. G1HR ALLOC(StartsH) 0x6ec00000 0x6ed00000G1HR ALLOC(ContinuesH) 0x6ed00000 0x6e000000 Regions being used for the allocation of Humongous object. The object spans over two regions. G1HR ALLOC(SingleH) 0x6f900000 0x6f9eb010 Single region being used for the allocation of Humongous object. G1HR COMMIT [0x6ee00000,0x6ef00000]G1HR COMMIT [0x6ef00000,0x6f000000]G1HR COMMIT [0x6f000000,0x6f100000]G1HR COMMIT [0x6f100000,0x6f200000]G1HR ALLOC(StartsH) 0x6ee00000 0x6ef00000G1HR ALLOC(ContinuesH) 0x6ef00000 0x6f000000G1HR ALLOC(ContinuesH) 0x6f000000 0x6f100000G1HR ALLOC(ContinuesH) 0x6f100000 0x6f102010 Here, Humongous object allocation request could not be satisfied by the free committed regions that existed in the heap, so the heap needed to be expanded. Thus new regions are committed and then allocated into for the Humongous object. G1HR ALLOC(Old) 0x6f800000 Old region started being used for allocation during GC. G1HR ALLOC(Survivor) 0x6fa00000 Region being used for copying old objects into during a GC. Note that Eden and Humongous ALLOC events are generated outside the GC boundaries and Old and Survivor ALLOC events are generated inside the GC boundaries. Other Events G1HR RETIRE 0x6e800000 0x6e87bd98 Retire and stop using the region having bottom 0x6e800000 and top 0x6e87bd98 for allocation. Note that most regions are full when they are retired and we omit those events to reduce the output volume. A region is retired when another region of the same type is allocated or we reach the start or end of a GC(depending on the region). So for Eden regions: For example: 1. ALLOC(Eden) Foo2. ALLOC(Eden) Bar3. StartGC At point 2, Foo has just been retired and it was full. At point 3, Bar was retired and it was full. If they were not full when they were retired, we will have a RETIRE event: 1. ALLOC(Eden) Foo2. RETIRE Foo top3. ALLOC(Eden) Bar4. StartGC G1HR CSET 0x6e900000 Region (bottom: 0x6e900000) is selected for the Collection Set. The region might have been selected for the collection set earlier (i.e. when it was allocated). However, we generate the CSET events for all regions in the CSet at the start of a GC to make sure there's no confusion about which regions are part of the CSet. G1HR POST-COMPACTION(Old) 0x6e800000 0x6e839858 POST-COMPACTION event is generated for each non-empty region in the heap after a full compaction. A full compaction moves objects around, so we don't know what the resulting shape of the heap is (which regions were written to, which were emptied, etc.). To deal with this, we generate a POST-COMPACTION event for each non-empty region with its type (old/humongous) and the heap boundaries. At this point we should only have Old and Humongous regions, as we have collapsed the young generation, so we should not have eden and survivors. POST-COMPACTION events are generated within the Full GC boundary. G1HR CLEANUP 0x6f400000G1HR CLEANUP 0x6f300000G1HR CLEANUP 0x6f200000 These regions were found empty after remark phase of Concurrent Marking and are reclaimed shortly afterwards. G1HR #StartGC 5G1HR CSET 0x6f400000G1HR CSET 0x6e900000G1HR REUSE 0x6f800000 At the end of a GC we retire the old region we are allocating into. Given that its not full, we will carry on allocating into it during the next GC. This is what REUSE means. In the above case 0x6f800000 should have been the last region with an ALLOC(Old) event during the previous GC and should have been retired before the end of the previous GC. G1HR ALLOC-FORCE(Eden) 0x6f800000 A specialization of ALLOC which indicates that we have reached the max desired number of the particular region type (in this case: Eden), but we decided to allocate one more. Currently it's only used for Eden regions when we extend the young generation because we cannot do a GC as the GC-Locker is active. G1HR EVAC-FAILURE 0x6f800000 During a GC, we have failed to evacuate an object from the given region as the heap is full and there is no space left to copy the object. This event is generated within GC boundaries and exactly once for each region from which we failed to evacuate objects. When Heap Regions are reclaimed ? It is also worth mentioning when the heap regions in the G1 heap are reclaimed. All regions that are in the CSet (the ones that appear in CSET events) are reclaimed at the end of a GC. The exception to that are regions with EVAC-FAILURE events. All regions with CLEANUP events are reclaimed. After a Full GC some regions get reclaimed (the ones from which we moved the objects out). But that is not shown explicitly, instead the non-empty regions that are left in the heap are printed out with the POST-COMPACTION events.

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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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  • SQL SERVER – Partition Parallelism Support in expressor 3.6

    - by pinaldave
    I am very excited to learn that there is a new version of expressor’s data integration platform coming out in March of this year.  It will be version 3.6, and I look forward to using it and telling everyone about it.  Let me describe a little bit more about what will be so great in expressor 3.6: Greatly enhanced user interface Parallel Processing Bulk Artifact Upgrading The User Interface First let me cover the most obvious enhancements. The expressor Studio user interface (UI) has had some significant work done. Kudos to the expressor Engineering team; the entire UI is a visual masterpiece that is very responsive and intuitive. The improvements are more than just eye candy; they provide significant productivity gains when developing expressor Dataflows. Operator shape icons now include a description that identifies the function of each operator, instead of having to guess at the function by the icon. Operator shapes and highlighting depict the current function and status: Disabled, enabled, complete, incomplete, and error. Each status displays an appropriate message in the message panel with correction suggestions. Floating or docking property panels provide descriptive tool tips for each property as well as auto resize when adjusting the canvas, without having to search Help or the need to scroll around to get access to the property. Progress and status indicators let you know when an operation is working. “No limit” canvas with snap-to-grid allows automatic sizing and accurate positioning when you have numerous operators in the Dataflow. The inline tool bar offers quick access to pan, zoom, fit and overview functions. Selecting multiple artifacts with a right click context allows you to easily manage your workspace more efficiently. Partitioning and Parallel Processing Partitioning allows each operator to process multiple subsets of records in parallel as opposed to processing all records that flow through that operator in a single sequential set. This capability allows the user to configure the expressor Dataflow to run in a way that most efficiently utilizes the resources of the hardware where the Dataflow is running. Partitions can exist in most individual operators. Using partitions increases the speed of an expressor data integration application, therefore improving performance and load times. With the expressor 3.6 Enterprise Edition, expressor simplifies enabling parallel processing by adding intuitive partition settings that are easy to configure. Bulk Artifact Upgrading Bulk Artifact Upgrading sounds a bit intimidating, but it actually is not and it is a welcome addition to expressor Studio. In past releases, users were prompted to confirm that they wanted to upgrade their individual artifacts only when opened. This was a cumbersome and repetitive process. Now with bulk artifact upgrading, a user can easily select what artifact or group of artifacts to upgrade all at once. As you can see, there are many new features and upgrade options that will prove to make expressor Studio quicker and more efficient.  I hope I’m not the only one who is excited about all these new upgrades, and that I you try expressor and share your experience with me. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

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  • Efficiently separating Read/Compute/Write steps for concurrent processing of entities in Entity/Component systems

    - by TravisG
    Setup I have an entity-component architecture where Entities can have a set of attributes (which are pure data with no behavior) and there exist systems that run the entity logic which act on that data. Essentially, in somewhat pseudo-code: Entity { id; map<id_type, Attribute> attributes; } System { update(); vector<Entity> entities; } A system that just moves along all entities at a constant rate might be MovementSystem extends System { update() { for each entity in entities position = entity.attributes["position"]; position += vec3(1,1,1); } } Essentially, I'm trying to parallelise update() as efficiently as possible. This can be done by running entire systems in parallel, or by giving each update() of one system a couple of components so different threads can execute the update of the same system, but for a different subset of entities registered with that system. Problem In reality, these systems sometimes require that entities interact(/read/write data from/to) each other, sometimes within the same system (e.g. an AI system that reads state from other entities surrounding the current processed entity), but sometimes between different systems that depend on each other (i.e. a movement system that requires data from a system that processes user input). Now, when trying to parallelize the update phases of entity/component systems, the phases in which data (components/attributes) from Entities are read and used to compute something, and the phase where the modified data is written back to entities need to be separated in order to avoid data races. Otherwise the only way (not taking into account just "critical section"ing everything) to avoid them is to serialize parts of the update process that depend on other parts. This seems ugly. To me it would seem more elegant to be able to (ideally) have all processing running in parallel, where a system may read data from all entities as it wishes, but doesn't write modifications to that data back until some later point. The fact that this is even possible is based on the assumption that modification write-backs are usually very small in complexity, and don't require much performance, whereas computations are very expensive (relatively). So the overhead added by a delayed-write phase might be evened out by more efficient updating of entities (by having threads work more % of the time instead of waiting). A concrete example of this might be a system that updates physics. The system needs to both read and write a lot of data to and from entities. Optimally, there would be a system in place where all available threads update a subset of all entities registered with the physics system. In the case of the physics system this isn't trivially possible because of race conditions. So without a workaround, we would have to find other systems to run in parallel (which don't modify the same data as the physics system), other wise the remaining threads are waiting and wasting time. However, that has disadvantages Practically, the L3 cache is pretty much always better utilized when updating a large system with multiple threads, as opposed to multiple systems at once, which all act on different sets of data. Finding and assembling other systems to run in parallel can be extremely time consuming to design well enough to optimize performance. Sometimes, it might even not be possible at all because a system just depends on data that is touched by all other systems. Solution? In my thinking, a possible solution would be a system where reading/updating and writing of data is separated, so that in one expensive phase, systems only read data and compute what they need to compute, and then in a separate, performance-wise cheap, write phase, attributes of entities that needed to be modified are finally written back to the entities. The Question How might such a system be implemented to achieve optimal performance, as well as making programmer life easier? What are the implementation details of such a system and what might have to be changed in the existing EC-architecture to accommodate this solution?

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  • Asynchrony in C# 5 (Part II)

    - by javarg
    This article is a continuation of the series of asynchronous features included in the new Async CTP preview for next versions of C# and VB. Check out Part I for more information. So, let’s continue with TPL Dataflow: Asynchronous functions TPL Dataflow Task based asynchronous Pattern Part II: TPL Dataflow Definition (by quote of Async CTP doc): “TPL Dataflow (TDF) is a new .NET library for building concurrent applications. It promotes actor/agent-oriented designs through primitives for in-process message passing, dataflow, and pipelining. TDF builds upon the APIs and scheduling infrastructure provided by the Task Parallel Library (TPL) in .NET 4, and integrates with the language support for asynchrony provided by C#, Visual Basic, and F#.” This means: data manipulation processed asynchronously. “TPL Dataflow is focused on providing building blocks for message passing and parallelizing CPU- and I/O-intensive applications”. Data manipulation is another hot area when designing asynchronous and parallel applications: how do you sync data access in a parallel environment? how do you avoid concurrency issues? how do you notify when data is available? how do you control how much data is waiting to be consumed? etc.  Dataflow Blocks TDF provides data and action processing blocks. Imagine having preconfigured data processing pipelines to choose from, depending on the type of behavior you want. The most basic block is the BufferBlock<T>, which provides an storage for some kind of data (instances of <T>). So, let’s review data processing blocks available. Blocks a categorized into three groups: Buffering Blocks Executor Blocks Joining Blocks Think of them as electronic circuitry components :).. 1. BufferBlock<T>: it is a FIFO (First in First Out) queue. You can Post data to it and then Receive it synchronously or asynchronously. It synchronizes data consumption for only one receiver at a time (you can have many receivers but only one will actually process it). 2. BroadcastBlock<T>: same FIFO queue for messages (instances of <T>) but link the receiving event to all consumers (it makes the data available for consumption to N number of consumers). The developer can provide a function to make a copy of the data if necessary. 3. WriteOnceBlock<T>: it stores only one value and once it’s been set, it can never be replaced or overwritten again (immutable after being set). As with BroadcastBlock<T>, all consumers can obtain a copy of the value. 4. ActionBlock<TInput>: this executor block allows us to define an operation to be executed when posting data to the queue. Thus, we must pass in a delegate/lambda when creating the block. Posting data will result in an execution of the delegate for each data in the queue. You could also specify how many parallel executions to allow (degree of parallelism). 5. TransformBlock<TInput, TOutput>: this is an executor block designed to transform each input, that is way it defines an output parameter. It ensures messages are processed and delivered in order. 6. TransformManyBlock<TInput, TOutput>: similar to TransformBlock but produces one or more outputs from each input. 7. BatchBlock<T>: combines N single items into one batch item (it buffers and batches inputs). 8. JoinBlock<T1, T2, …>: it generates tuples from all inputs (it aggregates inputs). Inputs could be of any type you want (T1, T2, etc.). 9. BatchJoinBlock<T1, T2, …>: aggregates tuples of collections. It generates collections for each type of input and then creates a tuple to contain each collection (Tuple<IList<T1>, IList<T2>>). Next time I will show some examples of usage for each TDF block. * Images taken from Microsoft’s Async CTP documentation.

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  • Effectively implementing a game view using java

    - by kdavis8
    I am writing a 2d game in java. The game mechanics are similar to the Pokémon game boy advance series e.g. fire red, ruby, diamond and so on. I need a way to draw a huge map maybe 5000 by 5000 pixels and then load individual in game sprites to across the entirety of the map, like rendering a scene. Game sprites would be things like terrain objects, trees, rocks, bushes, also houses, castles, NPC's and so on. But i also need to implement some kind of camera view class that focuses on the player. the camera view class needs to follow the characters movements throughout the game map but it also needs to clip the rest of the map away from the user's field of view, so that the user can only see the arbitrary proximity adjacent to the player's sprite. The proximity's range could be something like 500 pixels in every direction around the player’s sprite. On top of this, i need to implement an independent resolution for the game world so that the game view will be uniform on all screen sizes and screen resolutions. I know that this does sound like a handful and may fall under the category of multiple questions, but the questions are all related and any advice would be very much appreciated. I don’t need a full source code listing but maybe some pointers to effective java API classes that could make doing what i need to do a lot simpler. Also any algorithmic/ design advice would greatly benefit me as well. example of what i am trying to do in source code form below package myPackage; /** * The Purpose of GameView is to: Render a scene using Scene class, Create a * clipping pane using CameraView class, and finally instantiate a coordinate * grid using Path class. * * Once all of these things have been done, GameView class should then be * instantiated and used jointly with its helper classes. CameraView should be * used as the main drawing image. CameraView is the the window to the game * world.Scene passes data constantly to CameraView so that the entire map flows * smoothly. Path uses the x and y coordinates from camera view to construct * cells for path finding algorithms. */ public class GameView { // Scene is a helper class to game view. it renders the entire map to memory // for the camera view. Scene scene; // Camera View is a helper class to game view. It clips the Scene into a // small image that follows the players coordinates. CameraView Camera; // Path is a helper class to game view. It observes and calculates the // coordinates of camera view and divides them into Grids/Cells for Path // finding. Path path; // this represents the player and has a getSprite() method that will return // the current frame column row combination of the passed sprite sheet. Sprite player; }

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