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  • Virtual PC in Remote Desktop session runs very slowly??

    - by Michael Bray
    I have a VPN to my work which is quite fast... I Remote Desktop to my work PC, which is running a Microsoft Virtual PC. Working with the PC while I'm actually at work isn't too bad, but when I try to interact with it over the remote desktop, it is VERY slow to respond. Even simple typing can be slow, but screen painting and response time is painfully obvious. Any suggestions to help speed it up?

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  • explorer.exe opens slowly

    - by d9k
    explorer.exe opens veeeery slow when I press [Win+R] key or type "explorer ." in command prompt etc. But it opens with normal speed when I just click on the shortcut to any folder. When I click on the URL in IM like ICQ, already openned browser (firefox) takes too long to process link too. I have this problem for some months and I'm very tired of this. OS: WinXP SP3.

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  • 100% utilization on amazon server

    - by user2939830
    Good day, I would just like to know if you guys have any idea what could be the possible cause for a sudden disconnection of clients and 100% cpu utilization in our amazon server. This problem just started 2 days ago and in both occasion it happened at 7 plus in the morning gmt+8. What we usually do is just reset the socket for it to normalize and then on the next day same thing happened at 7 in the morning every client is disconnected from the server.

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  • my gateway laptop will not reboot

    - by dom
    My gateway laptop model nx570xl originally rebooted normally. I don't know when it happened, but now when i try to reboot it Keeps trying but it never happens forcing me to shut down and wait a random amt of time before it will start up again. Its very annoying and wastes a lot of my time. I don't think its a cpu overheating problem because its random when it starts up after i turn it back on. Any ideas?

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  • Ball to Ball Collision - Detection and Handling

    - by Simucal
    With the help of the Stack Overflow community I've written a pretty basic-but fun physics simulator. You click and drag the mouse to launch a ball. It will bounce around and eventually stop on the "floor". My next big feature I want to add in is ball to ball collision. The ball's movement is broken up into a x and y speed vector. I have gravity (small reduction of the y vector each step), I have friction (small reduction of both vectors each collision with a wall). The balls honestly move around in a surprisingly realistic way. I guess my question has two parts: What is the best method to detect ball to ball collision? Do I just have an O(n^2) loop that iterates over each ball and checks every other ball to see if it's radius overlaps? What equations do I use to handle the ball to ball collisions? Physics 101 How does it effect the two balls speed x/y vectors? What is the resulting direction the two balls head off in? How do I apply this to each ball? Handling the collision detection of the "walls" and the resulting vector changes were easy but I see more complications with ball-ball collisions. With walls I simply had to take the negative of the appropriate x or y vector and off it would go in the correct direction. With balls I don't think it is that way. Some quick clarifications: for simplicity I'm ok with a perfectly elastic collision for now, also all my balls have the same mass right now, but I might change that in the future. In case anyone is interested in playing with the simulator I have made so far, I've uploaded the source here (EDIT: Check the updated source below). Edit: Resources I have found useful 2d Ball physics with vectors: 2-Dimensional Collisions Without Trigonometry.pdf 2d Ball collision detection example: Adding Collision Detection Success! I have the ball collision detection and response working great! Relevant code: Collision Detection: for (int i = 0; i < ballCount; i++) { for (int j = i + 1; j < ballCount; j++) { if (balls[i].colliding(balls[j])) { balls[i].resolveCollision(balls[j]); } } } This will check for collisions between every ball but skip redundant checks (if you have to check if ball 1 collides with ball 2 then you don't need to check if ball 2 collides with ball 1. Also, it skips checking for collisions with itself). Then, in my ball class I have my colliding() and resolveCollision() methods: public boolean colliding(Ball ball) { float xd = position.getX() - ball.position.getX(); float yd = position.getY() - ball.position.getY(); float sumRadius = getRadius() + ball.getRadius(); float sqrRadius = sumRadius * sumRadius; float distSqr = (xd * xd) + (yd * yd); if (distSqr <= sqrRadius) { return true; } return false; } public void resolveCollision(Ball ball) { // get the mtd Vector2d delta = (position.subtract(ball.position)); float d = delta.getLength(); // minimum translation distance to push balls apart after intersecting Vector2d mtd = delta.multiply(((getRadius() + ball.getRadius())-d)/d); // resolve intersection -- // inverse mass quantities float im1 = 1 / getMass(); float im2 = 1 / ball.getMass(); // push-pull them apart based off their mass position = position.add(mtd.multiply(im1 / (im1 + im2))); ball.position = ball.position.subtract(mtd.multiply(im2 / (im1 + im2))); // impact speed Vector2d v = (this.velocity.subtract(ball.velocity)); float vn = v.dot(mtd.normalize()); // sphere intersecting but moving away from each other already if (vn > 0.0f) return; // collision impulse float i = (-(1.0f + Constants.restitution) * vn) / (im1 + im2); Vector2d impulse = mtd.multiply(i); // change in momentum this.velocity = this.velocity.add(impulse.multiply(im1)); ball.velocity = ball.velocity.subtract(impulse.multiply(im2)); } Source Code: Complete source for ball to ball collider. Binary: Compiled binary in case you just want to try bouncing some balls around. If anyone has some suggestions for how to improve this basic physics simulator let me know! One thing I have yet to add is angular momentum so the balls will roll more realistically. Any other suggestions? Leave a comment!

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  • Problem with room/screen/menu controller in python game: old rooms are not removed from memory

    - by Jordan Magnuson
    I'm literally banging my head against a wall here (as in, yes, physically, at my current location, I am damaging my cranium). Basically, I've got a Python/Pygame game with some typical game "rooms", or "screens." EG title screen, high scores screen, and the actual game room. Something bad is happening when I switch between rooms: the old room (and its various items) are not removed from memory, or from my event listener. Not only that, but every time I go back to a certain room, my number of event listeners increases, as well as the RAM being consumed! (So if I go back and forth between the title screen and the "game room", for instance, the number of event listeners and the memory usage just keep going up and up. The main issue is that all the event listeners start to add up and really drain the CPU. I'm new to Python, and don't know if I'm doing something obviously wrong here, or what. I will love you so much if you can help me with this! Below is the relevant source code. Complete source code at http://www.necessarygames.com/my_games/betraveled/betraveled_src0328.zip MAIN.PY class RoomController(object): """Controls which room is currently active (eg Title Screen)""" def __init__(self, screen, ev_manager): self.room = None self.screen = screen self.ev_manager = ev_manager self.ev_manager.register_listener(self) self.room = self.set_room(config.room) def set_room(self, room_const): #Unregister old room from ev_manager if self.room: self.room.ev_manager.unregister_listener(self.room) self.room = None #Set new room based on const if room_const == config.TITLE_SCREEN: return rooms.TitleScreen(self.screen, self.ev_manager) elif room_const == config.GAME_MODE_ROOM: return rooms.GameModeRoom(self.screen, self.ev_manager) elif room_const == config.GAME_ROOM: return rooms.GameRoom(self.screen, self.ev_manager) elif room_const == config.HIGH_SCORES_ROOM: return rooms.HighScoresRoom(self.screen, self.ev_manager) def notify(self, event): if isinstance(event, ChangeRoomRequest): if event.game_mode: config.game_mode = event.game_mode self.room = self.set_room(event.new_room) #Run game def main(): pygame.init() screen = pygame.display.set_mode(config.screen_size) ev_manager = EventManager() spinner = CPUSpinnerController(ev_manager) room_controller = RoomController(screen, ev_manager) pygame_event_controller = PyGameEventController(ev_manager) spinner.run() EVENT_MANAGER.PY class EventManager: #This object is responsible for coordinating most communication #between the Model, View, and Controller. def __init__(self): from weakref import WeakKeyDictionary self.last_listeners = {} self.listeners = WeakKeyDictionary() self.eventQueue= [] self.gui_app = None #---------------------------------------------------------------------- def register_listener(self, listener): self.listeners[listener] = 1 #---------------------------------------------------------------------- def unregister_listener(self, listener): if listener in self.listeners: del self.listeners[listener] #---------------------------------------------------------------------- def clear(self): del self.listeners[:] #---------------------------------------------------------------------- def post(self, event): # if isinstance(event, MouseButtonLeftEvent): # debug(event.name) #NOTE: copying the list like this before iterating over it, EVERY tick, is highly inefficient, #but currently has to be done because of how new listeners are added to the queue while it is running #(eg when popping cards from a deck). Should be changed. See: http://dr0id.homepage.bluewin.ch/pygame_tutorial08.html #and search for "Watch the iteration" print 'Number of listeners: ' + str(len(self.listeners)) for listener in list(self.listeners): #NOTE: If the weakref has died, it will be #automatically removed, so we don't have #to worry about it. listener.notify(event) def notify(self, event): pass #------------------------------------------------------------------------------ class PyGameEventController: """...""" def __init__(self, ev_manager): self.ev_manager = ev_manager self.ev_manager.register_listener(self) self.input_freeze = False #---------------------------------------------------------------------- def notify(self, incoming_event): if isinstance(incoming_event, UserInputFreeze): self.input_freeze = True elif isinstance(incoming_event, UserInputUnFreeze): self.input_freeze = False elif isinstance(incoming_event, TickEvent) or isinstance(incoming_event, BoardCreationTick): #Share some time with other processes, so we don't hog the cpu pygame.time.wait(5) #Handle Pygame Events for event in pygame.event.get(): #If this event manager has an associated PGU GUI app, notify it of the event if self.ev_manager.gui_app: self.ev_manager.gui_app.event(event) #Standard event handling for everything else ev = None if event.type == QUIT: ev = QuitEvent() elif event.type == pygame.MOUSEBUTTONDOWN and not self.input_freeze: if event.button == 1: #Button 1 pos = pygame.mouse.get_pos() ev = MouseButtonLeftEvent(pos) elif event.type == pygame.MOUSEBUTTONDOWN and not self.input_freeze: if event.button == 2: #Button 2 pos = pygame.mouse.get_pos() ev = MouseButtonRightEvent(pos) elif event.type == pygame.MOUSEBUTTONUP and not self.input_freeze: if event.button == 2: #Button 2 Release pos = pygame.mouse.get_pos() ev = MouseButtonRightReleaseEvent(pos) elif event.type == pygame.MOUSEMOTION: pos = pygame.mouse.get_pos() ev = MouseMoveEvent(pos) #Post event to event manager if ev: self.ev_manager.post(ev) # elif isinstance(event, BoardCreationTick): # #Share some time with other processes, so we don't hog the cpu # pygame.time.wait(5) # # #If this event manager has an associated PGU GUI app, notify it of the event # if self.ev_manager.gui_app: # self.ev_manager.gui_app.event(event) #------------------------------------------------------------------------------ class CPUSpinnerController: def __init__(self, ev_manager): self.ev_manager = ev_manager self.ev_manager.register_listener(self) self.clock = pygame.time.Clock() self.cumu_time = 0 self.keep_going = True #---------------------------------------------------------------------- def run(self): if not self.keep_going: raise Exception('dead spinner') while self.keep_going: time_passed = self.clock.tick() fps = self.clock.get_fps() self.cumu_time += time_passed self.ev_manager.post(TickEvent(time_passed, fps)) if self.cumu_time >= 1000: self.cumu_time = 0 self.ev_manager.post(SecondEvent(fps=fps)) pygame.quit() #---------------------------------------------------------------------- def notify(self, event): if isinstance(event, QuitEvent): #this will stop the while loop from running self.keep_going = False EXAMPLE CLASS USING EVENT MANAGER class Timer(object): def __init__(self, ev_manager, time_left): self.ev_manager = ev_manager self.ev_manager.register_listener(self) self.time_left = time_left self.paused = False def __repr__(self): return str(self.time_left) def pause(self): self.paused = True def unpause(self): self.paused = False def notify(self, event): #Pause Event if isinstance(event, Pause): self.pause() #Unpause Event elif isinstance(event, Unpause): self.unpause() #Second Event elif isinstance(event, SecondEvent): if not self.paused: self.time_left -= 1

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  • Bandwidth Limit Php Not working

    - by Saxtor
    Hey How are you doing guys, i am trying to limit bandwidth per users not by ipaddress for some reason my code doesnt work i need some help, what i am trying to do is to limit the download of the user that they would only have 10Gb per day to download however it seems to me that my buffer is not working when i use multiple connections it doesnt work, but when i use one connect it works 80% here is my code can you debug the error for me thanks. /** * @author saxtor if you can improve this code email me [email protected] * @copyright 2010 */ /** * CREATE TABLE IF NOT EXISTS `max_traffic` ( `id` int(255) NOT NULL AUTO_INCREMENT, `limit` int(255) NOT NULL, PRIMARY KEY (`id`) ) ENGINE=MyISAM DEFAULT CHARSET=latin1 AUTO_INCREMENT=0 ; */ //SQL Connection [this is hackable for testing] date_default_timezone_set("America/Guyana"); mysql_connect("localhost", "root", "") or die(mysql_error()); mysql_select_db("Quota") or die(mysql_error()); function quota($id) { $result = mysql_query("SELECT `limit` FROM max_traffic WHERE id='$id' ") or die(error_log(mysql_error()));; $row = mysql_fetch_array($result); return $row[0]; } function update_quota($id,$value) { $result = mysql_query("UPDATE `max_traffic` SET `limit`='$value' WHERE id='$id'") or die(mysql_error()); return $value; } if ( quota(1) != 0) $limit = quota(1); else $limit = 0; $multipart = false; //was a part of the file requested? (partial download) $range = $_SERVER["HTTP_RANGE"]; if ($range) { $cookie .= "\r\nRange: $range"; $multipart = true; header("X-UR-RANGE-Range: $range"); } $url = 'http://127.0.0.1/puppy.iso'; $filename = basename($url); //octet-stream + attachment => client always stores file header('Content-type: application/octet-stream'); header('Content-Disposition: attachment; filename="'.$filename.'"'); //always included so clients know this script supports resuming header("Accept-Ranges: bytes"); $user_agent = ini_get("user_agent"); ini_set("user_agent", $user_agent . "\r\nCookie: enc=$cookie"); $httphandle = fopen($url, "r"); $headers = stream_get_meta_data($httphandle); $size = $headers["wrapper_data"][6]; $sizer = explode(' ',$size); $size = $sizer[1]; //let's check the return header of rapidshare for range / length indicators //we'll just pass these to the client foreach ($headers["wrapper_data"] as $header) { $header = trim($header); if (substr(strtolower($header), 0, strlen("content-range")) == "content-range") { // _insert($range); header($header); header("X-RS-RANGE-" . $header); $multipart = true; //content-range indicates partial download } elseif (substr(strtolower($header), 0, strlen("Content-Length")) == "content-length") { // _insert($range); header($header); header("X-RS-CL-" . $header); } } if ($multipart) header('HTTP/1.1 206 Partial Content'); flush(); $speed = 4128; $packet = 1; //this is private dont touch. $bufsize = 128; //this is private dont touch/ $bandwidth = 0; //this is private dont touch. while (!(connection_aborted() || connection_status() == 1) && $size > 0) { while (!feof($httphandle) && $size > 0) { if ($limit <= 0 ) $size = 0; if ( $size < $bufsize && $size != 0 && $limit != 0) { echo fread($httphandle,$size); $bandwidth += $size; } else { if( $limit != 0) echo fread($httphandle,$bufsize); $bandwidth += $bufsize; } $size -= $bufsize; $limit -= $bufsize; flush(); if ($speed > 0 && ($bandwidth > $speed*$packet*103)) { usleep(100000); $packet++; //update_quota(1,$limit); } error_log(update_quota(1,$limit)); $limit = quota(1); //if( $size <= 0 ) // exit; } fclose($httphandle); } exit;

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  • Updating resources in SharpDX - why can I not map a dynamic texture?

    - by sebf
    I am trying to map a Texture2D resource in DirectX11 via SharpDX. The resource is declared as a ShaderResource, with Default usage and the 'Write' CPU flag specified. My call however fails with a generic exception from SharpDX: _Parent.Context.MapSubresource(_Resource, 0, SharpDX.Direct3D11.MapMode.Write, SharpDX.Direct3D11.MapFlags.None, out stream); I see from this question that it is supported. The MSDN docs and this other question hint that instead of using Context.MapSubresource() I should be using Texture2D.Map(), however, the DirectX11 Texture2D class does not define Map() (though it does for the DX10 equivalent). If I call the above with MapMode.WriteDiscard, the call succeeds but in this case the previous content of the texture is lost, which is no good when I only want to update a section of it. Has the Map() method been removed in DirectX11 or am I looking in the wrong place? Is the MapSubresource() method unsuitable or am I using it wrong?

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  • LWJGL - Eclipse error [on hold]

    - by Zarkopafilis
    When I try to run my lwjgl project, an error pops . Here is the log file: # A fatal error has been detected by the Java Runtime Environment: # EXCEPTION_ACCESS_VIOLATION (0xc0000005) at pc=0x6d8fcc0a, pid=5612, tid=900 # JRE version: 6.0_16-b01 Java VM: Java HotSpot(TM) Client VM (14.2-b01 mixed mode windows-x86 ) Problematic frame: V [jvm.dll+0xfcc0a] # If you would like to submit a bug report, please visit: http://java.sun.com/webapps/bugreport/crash.jsp # --------------- T H R E A D --------------- Current thread (0x016b9000): JavaThread "main" [_thread_in_vm, id=900, stack(0x00160000,0x001b0000)] siginfo: ExceptionCode=0xc0000005, reading address 0x00000000 Registers: EAX=0x00000000, EBX=0x00000000, ECX=0x00000006, EDX=0x00000000 ESP=0x001af4d4, EBP=0x001af524, ESI=0x016b9000, EDI=0x016b9110 EIP=0x6d8fcc0a, EFLAGS=0x00010246 Top of Stack: (sp=0x001af4d4) 0x001af4d4: 6da44bd8 016b9110 00000000 001af668 0x001af4e4: ffffffff 22200000 001af620 76ec39c2 0x001af4f4: 001af524 6d801086 0000000b 001afd34 0x001af504: 016b9000 016dd990 016b9000 00000000 0x001af514: 001af5f4 6d9ee000 6d9ef2f0 ffffffff 0x001af524: 001af58c 10008c85 016b9110 00000000 0x001af534: 00000000 000a0554 00000000 00000024 0x001af544: 00000000 00000000 001af6ac 00000000 Instructions: (pc=0x6d8fcc0a) 0x6d8fcbfa: e8 e8 d0 1d 08 00 8b 45 10 c7 45 d8 0b 00 00 00 0x6d8fcc0a: 8b 00 8b 48 08 0f b7 51 26 8b 40 0c 8b 4c 90 20 Stack: [0x00160000,0x001b0000], sp=0x001af4d4, free space=317k Native frames: (J=compiled Java code, j=interpreted, Vv=VM code, C=native code) V [jvm.dll+0xfcc0a] C [lwjgl.dll+0x8c85] C [USER32.dll+0x18876] C [USER32.dll+0x170f4] C [USER32.dll+0x1119e] C [ntdll.dll+0x460ce] C [USER32.dll+0x10e29] C [USER32.dll+0x10e84] C [lwjgl.dll+0x1cf0] j org.lwjgl.opengl.WindowsDisplay.createWindow(Lorg/lwjgl/opengl/DrawableLWJGL;Lorg/lwjgl/opengl/DisplayMode;Ljava/awt/Canvas;II)V+102 j org.lwjgl.opengl.Display.createWindow()V+71 j org.lwjgl.opengl.Display.create(Lorg/lwjgl/opengl/PixelFormat;Lorg/lwjgl/opengl/Drawable;Lorg/lwjgl/opengl/ContextAttribs;)V+72 j org.lwjgl.opengl.Display.create(Lorg/lwjgl/opengl/PixelFormat;)V+12 j org.lwjgl.opengl.Display.create()V+7 j zarkopafilis.koding.io.javafx.Main.main([Ljava/lang/String;)V+16 v ~StubRoutines::call_stub V [jvm.dll+0xecf9c] V [jvm.dll+0x1741e1] V [jvm.dll+0xed01d] V [jvm.dll+0xf5be5] V [jvm.dll+0xfd83d] C [javaw.exe+0x2155] C [javaw.exe+0x833e] C [kernel32.dll+0x51154] C [ntdll.dll+0x5b2b9] C [ntdll.dll+0x5b28c] Java frames: (J=compiled Java code, j=interpreted, Vv=VM code) j org.lwjgl.opengl.WindowsDisplay.nCreateWindow(IIIIZZJ)J+0 j org.lwjgl.opengl.WindowsDisplay.createWindow(Lorg/lwjgl/opengl/DrawableLWJGL;Lorg/lwjgl/opengl/DisplayMode;Ljava/awt/Canvas;II)V+102 j org.lwjgl.opengl.Display.createWindow()V+71 j org.lwjgl.opengl.Display.create(Lorg/lwjgl/opengl/PixelFormat;Lorg/lwjgl/opengl/Drawable;Lorg/lwjgl/opengl/ContextAttribs;)V+72 j org.lwjgl.opengl.Display.create(Lorg/lwjgl/opengl/PixelFormat;)V+12 j org.lwjgl.opengl.Display.create()V+7 j zarkopafilis.koding.io.javafx.Main.main([Ljava/lang/String;)V+16 v ~StubRoutines::call_stub --------------- P R O C E S S --------------- Java Threads: ( = current thread ) 0x0179a400 JavaThread "Low Memory Detector" daemon [_thread_blocked, id=4460, stack(0x0b900000,0x0b950000)] 0x01795400 JavaThread "CompilerThread0" daemon [_thread_blocked, id=5264, stack(0x0b8b0000,0x0b900000)] 0x01790c00 JavaThread "Attach Listener" daemon [_thread_blocked, id=6080, stack(0x0b860000,0x0b8b0000)] 0x01786400 JavaThread "Signal Dispatcher" daemon [_thread_blocked, id=1204, stack(0x0b810000,0x0b860000)] 0x01759c00 JavaThread "Finalizer" daemon [_thread_blocked, id=5772, stack(0x0b7c0000,0x0b810000)] 0x01755000 JavaThread "Reference Handler" daemon [_thread_blocked, id=4696, stack(0x01640000,0x01690000)] =0x016b9000 JavaThread "main" [_thread_in_vm, id=900, stack(0x00160000,0x001b0000)] Other Threads: 0x01751c00 VMThread [stack: 0x015f0000,0x01640000] [id=4052] 0x0179c800 WatcherThread [stack: 0x0b950000,0x0b9a0000] [id=3340] VM state:not at safepoint (normal execution) VM Mutex/Monitor currently owned by a thread: None Heap def new generation total 960K, used 816K [0x037c0000, 0x038c0000, 0x03ca0000) eden space 896K, 91% used [0x037c0000, 0x0388c2c0, 0x038a0000) from space 64K, 0% used [0x038a0000, 0x038a0000, 0x038b0000) to space 64K, 0% used [0x038b0000, 0x038b0000, 0x038c0000) tenured generation total 4096K, used 0K [0x03ca0000, 0x040a0000, 0x077c0000) the space 4096K, 0% used [0x03ca0000, 0x03ca0000, 0x03ca0200, 0x040a0000) compacting perm gen total 12288K, used 2143K [0x077c0000, 0x083c0000, 0x0b7c0000) the space 12288K, 17% used [0x077c0000, 0x079d7e38, 0x079d8000, 0x083c0000) No shared spaces configured. Dynamic libraries: 0x00400000 - 0x00424000 C:\Program Files\Java\jre6\bin\javaw.exe 0x77550000 - 0x7768e000 C:\Windows\SYSTEM32\ntdll.dll 0x75a80000 - 0x75b54000 C:\Windows\system32\kernel32.dll 0x758d0000 - 0x7591b000 C:\Windows\system32\KERNELBASE.dll 0x759e0000 - 0x75a80000 C:\Windows\system32\ADVAPI32.dll 0x76070000 - 0x7611c000 C:\Windows\system32\msvcrt.dll 0x77250000 - 0x77269000 C:\Windows\SYSTEM32\sechost.dll 0x771a0000 - 0x77241000 C:\Windows\system32\RPCRT4.dll 0x76eb0000 - 0x76f79000 C:\Windows\system32\USER32.dll 0x76e60000 - 0x76eae000 C:\Windows\system32\GDI32.dll 0x77770000 - 0x7777a000 C:\Windows\system32\LPK.dll 0x75fd0000 - 0x7606e000 C:\Windows\system32\USP10.dll 0x770b0000 - 0x770cf000 C:\Windows\system32\IMM32.DLL 0x770d0000 - 0x7719c000 C:\Windows\system32\MSCTF.dll 0x7c340000 - 0x7c396000 C:\Program Files\Java\jre6\bin\msvcr71.dll 0x6d800000 - 0x6da8b000 C:\Program Files\Java\jre6\bin\client\jvm.dll 0x73a00000 - 0x73a32000 C:\Windows\system32\WINMM.dll 0x75610000 - 0x7565b000 C:\Windows\system32\apphelp.dll 0x6d7b0000 - 0x6d7bc000 C:\Program Files\Java\jre6\bin\verify.dll 0x6d330000 - 0x6d34f000 C:\Program Files\Java\jre6\bin\java.dll 0x6d290000 - 0x6d298000 C:\Program Files\Java\jre6\bin\hpi.dll 0x776e0000 - 0x776e5000 C:\Windows\system32\PSAPI.DLL 0x6d7f0000 - 0x6d7ff000 C:\Program Files\Java\jre6\bin\zip.dll 0x10000000 - 0x1004c000 C:\Users\theo\Desktop\workspace\JavaFX1\lib\natives\windows\lwjgl.dll 0x5d170000 - 0x5d238000 C:\Windows\system32\OPENGL32.dll 0x6e7b0000 - 0x6e7d2000 C:\Windows\system32\GLU32.dll 0x70620000 - 0x70707000 C:\Windows\system32\DDRAW.dll 0x70610000 - 0x70616000 C:\Windows\system32\DCIMAN32.dll 0x75b60000 - 0x75cfd000 C:\Windows\system32\SETUPAPI.dll 0x759b0000 - 0x759d7000 C:\Windows\system32\CFGMGR32.dll 0x76d70000 - 0x76dff000 C:\Windows\system32\OLEAUT32.dll 0x75db0000 - 0x75f0c000 C:\Windows\system32\ole32.dll 0x758b0000 - 0x758c2000 C:\Windows\system32\DEVOBJ.dll 0x74060000 - 0x74073000 C:\Windows\system32\dwmapi.dll 0x74b60000 - 0x74b69000 C:\Windows\system32\VERSION.dll 0x745f0000 - 0x7478e000 C:\Windows\WinSxS\x86_microsoft.windows.common-controls_6595b64144ccf1df_6.0.7600.16661_none_420fe3fa2b8113bd\COMCTL32.dll 0x75d50000 - 0x75da7000 C:\Windows\system32\SHLWAPI.dll 0x74370000 - 0x743b0000 C:\Windows\system32\uxtheme.dll 0x22200000 - 0x22206000 C:\Program Files\ESET\ESET Smart Security\eplgHooks.dll VM Arguments: jvm_args: -Djava.library.path=C:\Users\theo\Desktop\workspace\JavaFX1\lib\natives\windows -Dfile.encoding=Cp1253 java_command: zarkopafilis.koding.io.javafx.Main Launcher Type: SUN_STANDARD Environment Variables: PATH=C:/Program Files/Java/jre6/bin/client;C:/Program Files/Java/jre6/bin;C:/Program Files/Java/jre6/lib/i386;C:\Perl\site\bin;C:\Perl\bin;C:\Ruby200\bin;C:\Program Files\Common Files\Microsoft Shared\Windows Live;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0\;C:\Program Files\Windows Live\Shared;C:\Users\theo\Desktop\eclipse; USERNAME=theo OS=Windows_NT PROCESSOR_IDENTIFIER=x86 Family 6 Model 37 Stepping 5, GenuineIntel --------------- S Y S T E M --------------- OS: Windows 7 Build 7600 CPU:total 4 (8 cores per cpu, 2 threads per core) family 6 model 37 stepping 5, cmov, cx8, fxsr, mmx, sse, sse2, sse3, ssse3, sse4.1, sse4.2, ht Memory: 4k page, physical 2097151k(1257972k free), swap 4194303k(4194303k free) vm_info: Java HotSpot(TM) Client VM (14.2-b01) for windows-x86 JRE (1.6.0_16-b01), built on Jul 31 2009 11:26:58 by "java_re" with MS VC++ 7.1 time: Wed Oct 23 22:00:12 2013 elapsed time: 0 seconds Code: Display.setDisplayMode(new DisplayMode(800,600)); Display.create();//Error here I am using JDK 6

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  • Unity Locks Up in Live CD

    - by user212883
    I'm trying to run from the live USB to install Ubuntu 13.10 on my Windows Machine (as I've grown a touch sick of Windows). However, whenever I boot into the LiveUSB session after a few moments the Unity desktop locks up (except the mouse pointer, which I can move). Is this something to do with the fact I've got an NVidia 580 GTX? I've heard of issues with Ubuntu and this card. I've also got an SSD, but given that it's booting from USB I shouldn't think that's an issue. System Specs: Processor: Intel Core i7-2600K CPU @ 3.40 GHZ Motherboard: Asus Maximus IV Gene-Z Z68 Socket 1155 RAM: 8GB DDR3 GPU: ASUS NVidia 580 GTX

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  • Understanding and Controlling Parallel Query Processing in SQL Server

    Data warehousing and general reporting applications tend to be CPU intensive because they need to read and process a large number of rows. To facilitate quick data processing for queries that touch a large amount of data, Microsoft SQL Server exploits the power of multiple logical processors to provide parallel query processing operations such as parallel scans. Through extensive testing, we have learned that, for most large queries that are executed in a parallel fashion, SQL Server can deliver linear or nearly linear response time speedup as the number of logical processors increases. However, some queries in high parallelism scenarios perform suboptimally. There are also some parallelism issues that can occur in a multi-user parallel query workload. This white paper describes parallel performance problems you might encounter when you run such queries and workloads, and it explains why these issues occur. In addition, it presents how data warehouse developers can detect these issues, and how they can work around them or mitigate them.

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  • Ruby on Rails - How can I start? [closed]

    - by Mashael
    I have misconception in understanding the relationship between Ruby language and Ruby on Rails Framework. Because of 'I am an absolute beginner' in web development I have no idea if I have to grasp the fundamentals of Ruby before I go with Ruby on Rails! I also want to ask who is behind both Ruby and Ruby on Rails. Who is developing both? And is there intention to improve its speed? In short, I'd like to know the road map to effectively beginning learning Ruby on Rails. Furthermore, I'm wondering about the next steps in improving Ruby and Rails and who are the main role players in improving them?

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  • Week in Geek: Facebook Valentine’s Day Scams Edition

    - by Asian Angel
    This week we learned how to get started with the Linux command-line text editor Nano, “speed up Start Menu searching, halt auto-rotating Android screens, & set up Dropbox-powered torrenting”, change the default application for Android tasks, find great gift recommendations for Valentine’s Day using the How-To Geek Valentine’s Day gift guide, had fun decorating our desktops with TRON and TRON Legacy theme items, and more Latest Features How-To Geek ETC Internet Explorer 9 RC Now Available: Here’s the Most Interesting New Stuff Here’s a Super Simple Trick to Defeating Fake Anti-Virus Malware How to Change the Default Application for Android Tasks Stop Believing TV’s Lies: The Real Truth About "Enhancing" Images The How-To Geek Valentine’s Day Gift Guide Inspire Geek Love with These Hilarious Geek Valentines Four Awesome TRON Legacy Themes for Chrome and Iron Anger is Illogical – Old School Style Instructional Video [Star Trek Mashup] Get the Old Microsoft Paint UI Back in Windows 7 Relax and Sleep Is a Soothing Sleep Timer Google Rolls Out Two-Factor Authentication Peaceful Early Morning by the Riverside Wallpaper

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  • SQL SERVER – Database Dynamic Caching by Automatic SQL Server Performance Acceleration

    - by pinaldave
    My second look at SafePeak’s new version (2.1) revealed to me few additional interesting features. For those of you who hadn’t read my previous reviews SafePeak and not familiar with it, here is a quick brief: SafePeak is in business of accelerating performance of SQL Server applications, as well as their scalability, without making code changes to the applications or to the databases. SafePeak performs database dynamic caching, by caching in memory result sets of queries and stored procedures while keeping all those cache correct and up to date. Cached queries are retrieved from the SafePeak RAM in microsecond speed and not send to the SQL Server. The application gets much faster results (100-500 micro seconds), the load on the SQL Server is reduced (less CPU and IO) and the application or the infrastructure gets better scalability. SafePeak solution is hosted either within your cloud servers, hosted servers or your enterprise servers, as part of the application architecture. Connection of the application is done via change of connection strings or adding reroute line in the c:\windows\system32\drivers\etc\hosts file on all application servers. For those who would like to learn more on SafePeak architecture and how it works, I suggest to read this vendor’s webpage: SafePeak Architecture. More interesting new features in SafePeak 2.1 In my previous review of SafePeak new I covered the first 4 things I noticed in the new SafePeak (check out my article “SQLAuthority News – SafePeak Releases a Major Update: SafePeak version 2.1 for SQL Server Performance Acceleration”): Cache setup and fine-tuning – a critical part for getting good caching results Database templates Choosing which database to cache Monitoring and analysis options by SafePeak Since then I had a chance to play with SafePeak some more and here is what I found. 5. Analysis of SQL Performance (present and history): In SafePeak v.2.1 the tools for understanding of performance became more comprehensive. Every 15 minutes SafePeak creates and updates various performance statistics. Each query (or a procedure execute) that arrives to SafePeak gets a SQL pattern, and after it is used again there are statistics for such pattern. An important part of this product is that it understands the dependencies of every pattern (list of tables, views, user defined functions and procs). From this understanding SafePeak creates important analysis information on performance of every object: response time from the database, response time from SafePeak cache, average response time, percent of traffic and break down of behavior. One of the interesting things this behavior column shows is how often the object is actually pdated. The break down analysis allows knowing the above information for: queries and procedures, tables, views, databases and even instances level. The data is show now on all arriving queries, both read queries (that can be cached), but also any types of updates like DMLs, DDLs, DCLs, and even session settings queries. The stats are being updated every 15 minutes and SafePeak dashboard allows going back in time and investigating what happened within any time frame. 6. Logon trigger, for making sure nothing corrupts SafePeak cache data If you have an application with many parts, many servers many possible locations that can actually update the database, or the SQL Server is accessible to many DBAs or software engineers, each can access some database directly and do some changes without going thru SafePeak – this can create a potential corruption of the data stored in SafePeak cache. To make sure SafePeak cache is correct it needs to get all updates to arrive to SafePeak, and if a DBA will access the database directly and do some changes, for example, then SafePeak will simply not know about it and will not clean SafePeak cache. In the new version, SafePeak brought a new feature called “Logon Trigger” to solve the above challenge. By special click of a button SafePeak can deploy a special server logon trigger (with a CLR object) on your SQL Server that actually monitors all connections and informs SafePeak on any connection that is coming not from SafePeak. In SafePeak dashboard there is an interface that allows to control which logins can be ignored based on login names and IPs, while the rest will invoke cache cleanup of SafePeak and actually locks SafePeak cache until this connection will not be closed. Important to note, that this does not interrupt any logins, only informs SafePeak on such connection. On the Dashboard screen in SafePeak you will be able to see those connections and then decide what to do with them. Configuration of this feature in SafePeak dashboard can be done here: Settings -> SQL instances management -> click on instance -> Logon Trigger tab. Other features: 7. User management ability to grant permissions to someone without changing its configuration and only use SafePeak as performance analysis tool. 8. Better reports for analysis of performance using 15 minute resolution charts. 9. Caching of client cursors 10. Support for IPv6 Summary SafePeak is a great SQL Server performance acceleration solution for users who want immediate results for sites with performance, scalability and peak spikes challenges. Especially if your apps are packaged or 3rd party, since no code changes are done. SafePeak can significantly increase response times, by reducing network roundtrip to the database, decreasing CPU resource usage, eliminating I/O and storage access. SafePeak team provides a free fully functional trial www.safepeak.com/download and actually provides a one-on-one assistance during such trial. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: About Me, Pinal Dave, PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, SQL Utility, T SQL, Technology

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  • What a Performance! MySQL 5.5 and InnoDB 1.1 running on Oracle Linux

    - by zeynep.koch(at)oracle.com
    The MySQL performance team in Oracle has recently completed a series of benchmarks comparing Read / Write and Read-Only performance of MySQL 5.5 with the InnoDB and MyISAM storage engines. Compared to MyISAM, InnoDB delivered 35x higher throughput on the Read / Write test and 5x higher throughput on the Read-Only test, with 90% scalability across 36 CPU cores. A full analysis of results and MySQL configuration parameters are documented in a new whitepaperIn addition to the benchmark, the new whitepaper, also includes:- A discussion of the use-cases for each storage engine- Best practices for users considering the migration of existing applications from MyISAM to InnoDB- A summary of the performance and scalability enhancements introduced with MySQL 5.5 and InnoDB 1.1.The benchmark itself was based on Sysbench, running on AMD Opteron "Magny-Cours" processors, and Oracle Linux with the Unbreakable Enterprise Kernel You can learn more about MySQL 5.5 and InnoDB 1.1 from here and download it from here to test whether you witness performance gains in your real-world applications.  By Mat Keep

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  • Why does my laptop resume immediately after suspend?

    - by Igor Zinov'yev
    I seem to be having some problem with suspend mode. Every time I try to suspend my laptop, it just locks the screen. Or maybe it successfully suspends just to resume only an instant after. What could cause such a behaviour? I'm running 32-bit Ubuntu 12.04 with the 3.2.0-25 kernel on a HP dv5-1178er Pavilion laptop (Intel Core 2 Duo). Here are the relevant log sections: kern.log: Jun 1 10:42:21 igor-laptop kernel: [ 2225.131171] PM: Syncing filesystems ... done. Jun 1 10:42:21 igor-laptop kernel: [ 2225.141222] PM: Preparing system for mem sleep Jun 1 10:42:21 igor-laptop kernel: [ 2225.141239] Freezing user space processes ... (elapsed 0.01 seconds) done. Jun 1 10:42:21 igor-laptop kernel: [ 2225.156171] Freezing remaining freezable tasks ... (elapsed 0.01 seconds) done. Jun 1 10:42:21 igor-laptop kernel: [ 2225.172139] PM: Entering mem sleep Jun 1 10:42:21 igor-laptop kernel: [ 2225.172169] Suspending console(s) (use no_console_suspend to debug) Jun 1 10:42:21 igor-laptop kernel: [ 2225.172895] sd 0:0:0:0: [sda] Synchronizing SCSI cache Jun 1 10:42:21 igor-laptop kernel: [ 2225.181767] sd 0:0:0:0: [sda] Stopping disk Jun 1 10:42:21 igor-laptop kernel: [ 2225.251089] ene_ir 00:0a: wake-up capability enabled by ACPI Jun 1 10:42:21 igor-laptop kernel: [ 2225.251115] i8042 aux 00:09: wake-up capability disabled by ACPI Jun 1 10:42:21 igor-laptop kernel: [ 2225.251133] i8042 kbd 00:08: wake-up capability enabled by ACPI Jun 1 10:42:21 igor-laptop kernel: [ 2225.251286] jmb38x_ms 0000:06:00.3: PCI INT A disabled Jun 1 10:42:21 igor-laptop kernel: [ 2225.252491] sdhci-pci 0000:06:00.1: PCI INT A disabled Jun 1 10:42:21 igor-laptop kernel: [ 2225.264130] uhci_hcd 0000:00:1d.2: PCI INT D disabled Jun 1 10:42:21 igor-laptop kernel: [ 2225.264142] uhci_hcd 0000:00:1d.1: PCI INT B disabled Jun 1 10:42:21 igor-laptop kernel: [ 2225.264325] uhci_hcd 0000:00:1a.1: PCI INT B disabled Jun 1 10:42:21 igor-laptop kernel: [ 2225.288059] uhci_hcd 0000:00:1a.0: PCI INT A disabled Jun 1 10:42:21 igor-laptop kernel: [ 2225.288097] uhci_hcd 0000:00:1d.3: PCI INT C disabled Jun 1 10:42:21 igor-laptop kernel: [ 2225.288135] uhci_hcd 0000:00:1d.0: PCI INT A disabled Jun 1 10:42:21 igor-laptop kernel: [ 2225.316051] ehci_hcd 0000:00:1d.7: PCI INT A disabled Jun 1 10:42:21 igor-laptop kernel: [ 2225.316068] ehci_hcd 0000:00:1a.7: PCI INT D disabled Jun 1 10:42:21 igor-laptop kernel: [ 2225.522872] PM: suspend of drv:sd dev:0:0:0:0 complete after 349.979 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.522901] PM: suspend of drv:scsi dev:target0:0:0 complete after 349.955 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.522927] PM: suspend of drv:scsi dev:host0 complete after 272.260 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.522969] ahci 0000:00:1f.2: BIOS update required for suspend/resume Jun 1 10:42:21 igor-laptop kernel: [ 2225.522976] pci_legacy_suspend(): ahci_pci_device_suspend+0x0/0x80 returns -5 Jun 1 10:42:21 igor-laptop kernel: [ 2225.522981] pm_op(): pci_pm_suspend+0x0/0x110 returns -5 Jun 1 10:42:21 igor-laptop kernel: [ 2225.522984] PM: suspend of drv:ahci dev:0000:00:1f.2 complete after 258.932 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.522987] PM: Device 0000:00:1f.2 failed to suspend async: error -5 Jun 1 10:42:21 igor-laptop kernel: [ 2225.576228] snd_hda_intel 0000:00:1b.0: PCI INT A disabled Jun 1 10:42:21 igor-laptop kernel: [ 2225.576270] ACPI handle has no context! Jun 1 10:42:21 igor-laptop kernel: [ 2225.592136] PM: suspend of drv:snd_hda_intel dev:0000:00:1b.0 complete after 327.889 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.592206] PM: Some devices failed to suspend Jun 1 10:42:21 igor-laptop kernel: [ 2225.592291] uhci_hcd 0000:00:1a.0: PCI INT A -> GSI 16 (level, low) -> IRQ 16 Jun 1 10:42:21 igor-laptop kernel: [ 2225.592298] uhci_hcd 0000:00:1a.0: setting latency timer to 64 Jun 1 10:42:21 igor-laptop kernel: [ 2225.592325] usb usb3: root hub lost power or was reset Jun 1 10:42:21 igor-laptop kernel: [ 2225.592339] uhci_hcd 0000:00:1a.1: PCI INT B -> GSI 21 (level, low) -> IRQ 21 Jun 1 10:42:21 igor-laptop kernel: [ 2225.592345] uhci_hcd 0000:00:1a.1: setting latency timer to 64 Jun 1 10:42:21 igor-laptop kernel: [ 2225.592371] usb usb4: root hub lost power or was reset Jun 1 10:42:21 igor-laptop kernel: [ 2225.592387] ehci_hcd 0000:00:1a.7: PCI INT D -> GSI 19 (level, low) -> IRQ 19 Jun 1 10:42:21 igor-laptop kernel: [ 2225.592395] ehci_hcd 0000:00:1a.7: setting latency timer to 64 Jun 1 10:42:21 igor-laptop kernel: [ 2225.592843] uhci_hcd 0000:00:1d.0: PCI INT A -> GSI 20 (level, low) -> IRQ 20 Jun 1 10:42:21 igor-laptop kernel: [ 2225.592851] uhci_hcd 0000:00:1d.0: setting latency timer to 64 Jun 1 10:42:21 igor-laptop kernel: [ 2225.592854] uhci_hcd 0000:00:1d.1: PCI INT B -> GSI 19 (level, low) -> IRQ 19 Jun 1 10:42:21 igor-laptop kernel: [ 2225.592863] uhci_hcd 0000:00:1d.1: setting latency timer to 64 Jun 1 10:42:21 igor-laptop kernel: [ 2225.592878] usb usb5: root hub lost power or was reset Jun 1 10:42:21 igor-laptop kernel: [ 2225.592892] usb usb6: root hub lost power or was reset Jun 1 10:42:21 igor-laptop kernel: [ 2225.592895] uhci_hcd 0000:00:1d.2: PCI INT D -> GSI 16 (level, low) -> IRQ 16 Jun 1 10:42:21 igor-laptop kernel: [ 2225.592903] uhci_hcd 0000:00:1d.2: setting latency timer to 64 Jun 1 10:42:21 igor-laptop kernel: [ 2225.592906] uhci_hcd 0000:00:1d.3: PCI INT C -> GSI 18 (level, low) -> IRQ 18 Jun 1 10:42:21 igor-laptop kernel: [ 2225.592915] uhci_hcd 0000:00:1d.3: setting latency timer to 64 Jun 1 10:42:21 igor-laptop kernel: [ 2225.592930] usb usb7: root hub lost power or was reset Jun 1 10:42:21 igor-laptop kernel: [ 2225.592946] usb usb8: root hub lost power or was reset Jun 1 10:42:21 igor-laptop kernel: [ 2225.592949] ehci_hcd 0000:00:1d.7: PCI INT A -> GSI 20 (level, low) -> IRQ 20 Jun 1 10:42:21 igor-laptop kernel: [ 2225.592957] ehci_hcd 0000:00:1d.7: setting latency timer to 64 Jun 1 10:42:21 igor-laptop kernel: [ 2225.592963] pci 0000:00:1e.0: setting latency timer to 64 Jun 1 10:42:21 igor-laptop kernel: [ 2225.597106] sd 0:0:0:0: [sda] Starting disk Jun 1 10:42:21 igor-laptop kernel: [ 2225.608138] snd_hda_intel 0000:00:1b.0: BAR 0: set to [mem 0xdf300000-0xdf303fff 64bit] (PCI address [0xdf300000-0xdf303fff]) Jun 1 10:42:21 igor-laptop kernel: [ 2225.608180] snd_hda_intel 0000:00:1b.0: restoring config space at offset 0xf (was 0x100, writing 0x10b) Jun 1 10:42:21 igor-laptop kernel: [ 2225.608233] snd_hda_intel 0000:00:1b.0: restoring config space at offset 0x3 (was 0x0, writing 0x10) Jun 1 10:42:21 igor-laptop kernel: [ 2225.608248] snd_hda_intel 0000:00:1b.0: restoring config space at offset 0x1 (was 0x100000, writing 0x100002) Jun 1 10:42:21 igor-laptop kernel: [ 2225.608299] snd_hda_intel 0000:00:1b.0: PCI INT A -> GSI 22 (level, low) -> IRQ 22 Jun 1 10:42:21 igor-laptop kernel: [ 2225.608313] snd_hda_intel 0000:00:1b.0: setting latency timer to 64 Jun 1 10:42:21 igor-laptop kernel: [ 2225.608420] snd_hda_intel 0000:00:1b.0: irq 50 for MSI/MSI-X Jun 1 10:42:21 igor-laptop kernel: [ 2225.612095] firewire_ohci 0000:06:00.0: restoring config space at offset 0x1 (was 0x100000, writing 0x100006) Jun 1 10:42:21 igor-laptop kernel: [ 2225.612181] sdhci-pci 0000:06:00.1: restoring config space at offset 0x1 (was 0x100003, writing 0x100007) Jun 1 10:42:21 igor-laptop kernel: [ 2225.612211] sdhci-pci 0000:06:00.1: PCI INT A -> GSI 16 (level, low) -> IRQ 16 Jun 1 10:42:21 igor-laptop kernel: [ 2225.612225] sdhci-pci 0000:06:00.1: setting latency timer to 64 Jun 1 10:42:21 igor-laptop kernel: [ 2225.612296] jmb38x_ms 0000:06:00.3: restoring config space at offset 0x1 (was 0x100003, writing 0x100007) Jun 1 10:42:21 igor-laptop kernel: [ 2225.612326] jmb38x_ms 0000:06:00.3: PCI INT A -> GSI 16 (level, low) -> IRQ 16 Jun 1 10:42:21 igor-laptop kernel: [ 2225.612332] jmb38x_ms 0000:06:00.3: setting latency timer to 64 Jun 1 10:42:21 igor-laptop kernel: [ 2225.699170] PM: resume of drv:uvcvideo dev:2-4:1.0 complete after 101.965 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.699179] PM: resume of drv:uvcvideo dev:2-4:1.1 complete after 101.932 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.699186] PM: resume of drv: dev:ep_00 complete after 101.917 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.699197] PM: resume of drv: dev:ep_83 complete after 101.972 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.716148] PM: resume of drv:hub dev:3-0:1.0 complete after 119.543 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.716155] PM: resume of drv: dev:ep_00 complete after 119.544 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.716161] PM: resume of drv:hub dev:5-0:1.0 complete after 119.420 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.716168] PM: resume of drv: dev:ep_00 complete after 119.381 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.716174] PM: resume of drv:hub dev:8-0:1.0 complete after 119.141 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.716181] PM: resume of drv: dev:ep_00 complete after 119.104 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.716186] PM: resume of drv: dev:ep_81 complete after 119.579 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.716191] PM: resume of drv: dev:ep_81 complete after 119.427 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.716197] PM: resume of drv: dev:ep_81 complete after 119.143 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.747148] firewire_core: skipped bus generations, destroying all nodes Jun 1 10:42:21 igor-laptop kernel: [ 2225.776093] PM: resume of drv:hp_accel dev:HPQ0004:00 complete after 167.225 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.777243] i8042 kbd 00:08: wake-up capability disabled by ACPI Jun 1 10:42:21 igor-laptop kernel: [ 2225.777278] ene_ir 00:0a: wake-up capability disabled by ACPI Jun 1 10:42:21 igor-laptop kernel: [ 2225.820100] PM: resume of drv:hub dev:4-0:1.0 complete after 223.436 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.820115] PM: resume of drv: dev:ep_00 complete after 223.444 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.820123] PM: resume of drv: dev:ep_81 complete after 223.456 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.820206] PM: resume of drv:hub dev:7-0:1.0 complete after 223.266 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.820221] PM: resume of drv: dev:ep_81 complete after 223.260 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.820238] PM: resume of drv: dev:ep_00 complete after 223.255 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.820295] PM: resume of drv:hub dev:6-0:1.0 complete after 223.453 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.820302] PM: resume of drv: dev:ep_00 complete after 223.415 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.820321] PM: resume of drv: dev:ep_81 complete after 223.457 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2225.932108] usb 4-2: reset full-speed USB device number 2 using uhci_hcd Jun 1 10:42:21 igor-laptop kernel: [ 2226.086714] PM: resume of drv:usbhid dev:4-2:1.0 complete after 489.393 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.086728] PM: resume of drv: dev:ep_81 complete after 489.384 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.086745] PM: resume of drv: dev:ep_00 complete after 489.329 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.086753] PM: resume of drv:usbhid dev:4-2:1.1 complete after 489.384 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.086764] PM: resume of drv: dev:ep_82 complete after 489.373 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.180555] usb 7-2: reset full-speed USB device number 2 using uhci_hcd Jun 1 10:42:21 igor-laptop kernel: [ 2226.244858] firewire_core: rediscovered device fw0 Jun 1 10:42:21 igor-laptop kernel: [ 2226.335066] btusb 7-2:1.0: no reset_resume for driver btusb? Jun 1 10:42:21 igor-laptop kernel: [ 2226.335068] btusb 7-2:1.1: no reset_resume for driver btusb? Jun 1 10:42:21 igor-laptop kernel: [ 2226.432082] usb 6-1: reset full-speed USB device number 2 using uhci_hcd Jun 1 10:42:21 igor-laptop kernel: [ 2226.578280] PM: resume of drv:nvidia dev:0000:01:00.0 complete after 985.301 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.584296] PM: resume of drv:usb dev:7-2:1.0 complete after 986.693 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.584308] PM: resume of drv: dev:ep_00 complete after 986.452 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.584311] PM: resume of drv:usb dev:7-2:1.1 complete after 986.616 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.584315] PM: resume of drv:usb dev:7-2:1.3 complete after 986.483 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.584320] PM: resume of drv:usb dev:7-2:1.2 complete after 986.556 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.584328] PM: resume of drv: dev:ep_03 complete after 986.588 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.584331] PM: resume of drv: dev:ep_81 complete after 986.704 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.584334] PM: resume of drv: dev:ep_83 complete after 986.617 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.584337] PM: resume of drv: dev:ep_82 complete after 986.688 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.584340] PM: resume of drv: dev:ep_02 complete after 986.667 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.584344] PM: resume of drv: dev:ep_84 complete after 986.558 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.584352] PM: resume of drv: dev:ep_04 complete after 986.542 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.590883] PM: resume of drv: dev:ep_00 complete after 993.327 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.590887] PM: resume of drv:usb dev:6-1:1.0 complete after 993.424 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.590927] PM: resume of drv: dev:ep_82 complete after 993.395 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.590934] PM: resume of drv: dev:ep_81 complete after 993.426 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.590940] PM: resume of drv: dev:ep_01 complete after 993.456 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.592450] PM: resume of drv:sd dev:0:0:0:0 complete after 995.343 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.592461] PM: resume of drv:scsi_disk dev:0:0:0:0 complete after 802.688 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.592472] PM: resume of drv:scsi_device dev:0:0:0:0 complete after 995.324 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.600339] PM: resume of devices complete after 1008.129 msecs Jun 1 10:42:21 igor-laptop kernel: [ 2226.601293] PM: resume devices took 1.008 seconds Jun 1 10:42:21 igor-laptop kernel: [ 2226.601330] PM: Finishing wakeup. Jun 1 10:42:21 igor-laptop kernel: [ 2226.601332] Restarting tasks ... done. Jun 1 10:42:21 igor-laptop kernel: [ 2226.625660] video LNXVIDEO:01: Restoring backlight state Jun 1 10:42:22 igor-laptop kernel: [ 2227.478921] iwlwifi 0000:02:00.0: L1 Disabled; Enabling L0S Jun 1 10:42:22 igor-laptop kernel: [ 2227.481981] iwlwifi 0000:02:00.0: Radio type=0x1-0x2-0x0 Jun 1 10:42:22 igor-laptop kernel: [ 2227.527727] ADDRCONF(NETDEV_UP): wlan0: link is not ready Jun 1 10:42:22 igor-laptop kernel: [ 2227.532468] r8169 0000:03:00.0: eth0: link down Jun 1 10:42:22 igor-laptop kernel: [ 2227.533967] ADDRCONF(NETDEV_UP): eth0: link is not ready pm_suspend.log: Fri Jun 1 10:42:14 MSK 2012: Running hooks for suspend. Running hook /usr/lib/pm-utils/sleep.d/000kernel-change suspend suspend: /usr/lib/pm-utils/sleep.d/000kernel-change suspend suspend: success. Running hook /usr/lib/pm-utils/sleep.d/00logging suspend suspend: Linux igor-laptop 3.2.0-25-generic #40-Ubuntu SMP Wed May 23 20:33:05 UTC 2012 i686 i686 i386 GNU/Linux Module Size Used by pci_stub 12550 1 vboxpci 22882 0 vboxnetadp 13328 0 vboxnetflt 27211 0 vboxdrv 252189 3 vboxpci,vboxnetadp,vboxnetflt dm_crypt 22528 0 snd_hda_codec_hdmi 31775 1 snd_hda_codec_idt 60251 1 arc4 12473 2 hp_wmi 13652 0 sparse_keymap 13658 1 hp_wmi rfcomm 38139 12 snd_hda_intel 32765 5 snd_hda_codec 109562 3 snd_hda_codec_hdmi,snd_hda_codec_idt,snd_hda_intel snd_hwdep 13276 1 snd_hda_codec bnep 17830 2 btusb 17912 2 bluetooth 158438 23 rfcomm,bnep,btusb joydev 17393 0 parport_pc 32114 0 snd_pcm 80845 4 snd_hda_codec_hdmi,snd_hda_intel,snd_hda_codec ppdev 12849 0 uvcvideo 67203 0 binfmt_misc 17292 1 videodev 86588 1 uvcvideo snd_seq_midi 13132 0 snd_rawmidi 25424 1 snd_seq_midi nvidia 10958194 43 snd_seq_midi_event 14475 1 snd_seq_midi snd_seq 51567 2 snd_seq_midi,snd_seq_midi_event ir_lirc_codec 12739 0 lirc_dev 18700 1 ir_lirc_codec snd_timer 28931 2 snd_pcm,snd_seq snd_seq_device 14172 3 snd_seq_midi,snd_rawmidi,snd_seq ir_mce_kbd_decoder 12681 0 ir_sony_decoder 12462 0 ir_jvc_decoder 12459 0 ir_rc6_decoder 12459 0 psmouse 87213 0 ir_rc5_decoder 12459 0 serio_raw 13027 0 iwlwifi 287934 0 rc_rc6_mce 12454 0 ir_nec_decoder 12459 0 ene_ir 18019 0 rc_core 21263 10 ir_lirc_codec,ir_mce_kbd_decoder,ir_sony_decoder,ir_jvc_decoder,ir_rc6_decoder,ir_rc5_decoder,rc_rc6_mce,ir_nec_decoder,ene_ir mac80211 436455 1 iwlwifi snd 62064 19 snd_hda_codec_hdmi,snd_hda_codec_idt,snd_hda_intel,snd_hda_codec,snd_hwdep,snd_pcm,snd_rawmidi,snd_seq,snd_timer,snd_seq_device cfg80211 178679 2 iwlwifi,mac80211 hp_accel 25728 0 lis3lv02d 19268 1 hp_accel input_polldev 13648 1 lis3lv02d mac_hid 13077 0 wmi 18744 1 hp_wmi jmb38x_ms 17406 0 soundcore 14635 1 snd snd_page_alloc 14115 2 snd_hda_intel,snd_pcm memstick 15857 1 jmb38x_ms firewire_sbp2 18346 0 lp 17455 0 parport 40930 3 parport_pc,ppdev,lp vesafb 13516 1 usbhid 41906 0 hid 77367 1 usbhid firewire_ohci 40180 0 firewire_core 56906 2 firewire_sbp2,firewire_ohci crc_itu_t 12627 1 firewire_core sdhci_pci 18324 0 sdhci 28241 1 sdhci_pci r8169 56321 0 video 19068 0 total used free shared buffers cached Mem: 3095544 2364260 731284 0 159020 1280240 -/+ buffers/cache: 925000 2170544 Swap: 1718916 0 1718916 /usr/lib/pm-utils/sleep.d/00logging suspend suspend: success. Running hook /usr/lib/pm-utils/sleep.d/00powersave suspend suspend: /usr/lib/pm-utils/sleep.d/00powersave suspend suspend: success. Running hook /usr/lib/pm-utils/sleep.d/01PulseAudio suspend suspend: Welcome to PulseAudio! Use "help" for usage information. >>> >>> Welcome to PulseAudio! Use "help" for usage information. >>> >>> Welcome to PulseAudio! Use "help" for usage information. >>> >>> /usr/lib/pm-utils/sleep.d/01PulseAudio suspend suspend: success. Running hook /etc/pm/sleep.d/10_grub-common suspend suspend: /etc/pm/sleep.d/10_grub-common suspend suspend: success. Running hook /etc/pm/sleep.d/10_unattended-upgrades-hibernate suspend suspend: /etc/pm/sleep.d/10_unattended-upgrades-hibernate suspend suspend: success. Running hook /usr/lib/pm-utils/sleep.d/55NetworkManager suspend suspend: Having NetworkManager put all interaces to sleep...Failed. /usr/lib/pm-utils/sleep.d/55NetworkManager suspend suspend: success. Running hook /usr/lib/pm-utils/sleep.d/60_wpa_supplicant suspend suspend: Failed to connect to wpa_supplicant - wpa_ctrl_open: No such file or directory /usr/lib/pm-utils/sleep.d/60_wpa_supplicant suspend suspend: success. Running hook /usr/lib/pm-utils/sleep.d/75modules suspend suspend: /usr/lib/pm-utils/sleep.d/75modules suspend suspend: success. Running hook /usr/lib/pm-utils/sleep.d/90clock suspend suspend: /usr/lib/pm-utils/sleep.d/90clock suspend suspend: not applicable. Running hook /usr/lib/pm-utils/sleep.d/94cpufreq suspend suspend: /usr/lib/pm-utils/sleep.d/94cpufreq suspend suspend: success. Running hook /usr/lib/pm-utils/sleep.d/95anacron suspend suspend: stop: Unknown instance: /usr/lib/pm-utils/sleep.d/95anacron suspend suspend: success. Running hook /usr/lib/pm-utils/sleep.d/95hdparm-apm suspend suspend: /usr/lib/pm-utils/sleep.d/95hdparm-apm suspend suspend: not applicable. Running hook /usr/lib/pm-utils/sleep.d/95led suspend suspend: /usr/lib/pm-utils/sleep.d/95led suspend suspend: not applicable. Running hook /usr/lib/pm-utils/sleep.d/98video-quirk-db-handler suspend suspend: nVidia binary video drive detected, not using quirks. /usr/lib/pm-utils/sleep.d/98video-quirk-db-handler suspend suspend: success. Running hook /usr/lib/pm-utils/sleep.d/99video suspend suspend: kernel.acpi_video_flags = 0 /usr/lib/pm-utils/sleep.d/99video suspend suspend: success. Running hook /etc/pm/sleep.d/novatel_3g_suspend suspend suspend: /etc/pm/sleep.d/novatel_3g_suspend suspend suspend: success. Fri Jun 1 10:42:19 MSK 2012: performing suspend Fri Jun 1 10:42:21 MSK 2012: Awake. Fri Jun 1 10:42:21 MSK 2012: Running hooks for resume Running hook /etc/pm/sleep.d/novatel_3g_suspend resume suspend: /etc/pm/sleep.d/novatel_3g_suspend resume suspend: success. Running hook /usr/lib/pm-utils/sleep.d/99video resume suspend: /usr/lib/pm-utils/sleep.d/99video resume suspend: success. Running hook /usr/lib/pm-utils/sleep.d/98video-quirk-db-handler resume suspend: /usr/lib/pm-utils/sleep.d/98video-quirk-db-handler resume suspend: success. Running hook /usr/lib/pm-utils/sleep.d/95led resume suspend: /usr/lib/pm-utils/sleep.d/95led resume suspend: not applicable. Running hook /usr/lib/pm-utils/sleep.d/95hdparm-apm resume suspend: /dev/sda: setting Advanced Power Management level to 0xfe (254) APM_level = 254 /dev/sda: setting Advanced Power Management level to 0xfe (254) APM_level = 254 /usr/lib/pm-utils/sleep.d/95hdparm-apm resume suspend: success. Running hook /usr/lib/pm-utils/sleep.d/95anacron resume suspend: /usr/lib/pm-utils/sleep.d/95anacron resume suspend: success. Running hook /usr/lib/pm-utils/sleep.d/94cpufreq resume suspend: /usr/lib/pm-utils/sleep.d/94cpufreq resume suspend: success. Running hook /usr/lib/pm-utils/sleep.d/90clock resume suspend: /usr/lib/pm-utils/sleep.d/90clock resume suspend: not applicable. Running hook /usr/lib/pm-utils/sleep.d/75modules resume suspend: Reloaded unloaded modules. /usr/lib/pm-utils/sleep.d/75modules resume suspend: success. Running hook /usr/lib/pm-utils/sleep.d/60_wpa_supplicant resume suspend: Failed to connect to wpa_supplicant - wpa_ctrl_open: No such file or directory /usr/lib/pm-utils/sleep.d/60_wpa_supplicant resume suspend: success. Running hook /usr/lib/pm-utils/sleep.d/55NetworkManager resume suspend: Having NetworkManager wake interfaces back up...Failed. /usr/lib/pm-utils/sleep.d/55NetworkManager resume suspend: success. Running hook /etc/pm/sleep.d/10_unattended-upgrades-hibernate resume suspend: /etc/pm/sleep.d/10_unattended-upgrades-hibernate resume suspend: success. Running hook /etc/pm/sleep.d/10_grub-common resume suspend: /etc/pm/sleep.d/10_grub-common resume suspend: success. Running hook /usr/lib/pm-utils/sleep.d/01PulseAudio resume suspend: Welcome to PulseAudio! Use "help" for usage information. >>> >>> Welcome to PulseAudio! Use "help" for usage information. >>> >>> Welcome to PulseAudio! Use "help" for usage information. >>> >>> /usr/lib/pm-utils/sleep.d/01PulseAudio resume suspend: success. Running hook /usr/lib/pm-utils/sleep.d/00powersave resume suspend: /usr/lib/pm-utils/sleep.d/00powersave resume suspend: success. Running hook /usr/lib/pm-utils/sleep.d/00logging resume suspend: /usr/lib/pm-utils/sleep.d/00logging resume suspend: success. Running hook /usr/lib/pm-utils/sleep.d/000kernel-change resume suspend: /usr/lib/pm-utils/sleep.d/000kernel-change resume suspend: success. Fri Jun 1 10:42:22 MSK 2012: Finished.

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  • Podcast: Advanced MVVM with Josh Smith

    - by craigshoemaker
    Author, Microsoft MVP and accomplished pianist Josh Smith, Sr. UX Developer at IdentityMine, joins the show to discuss some of Model View ViewModel’s more advanced scenarios. Full Speed: download Fast Version: download Josh shares is experience using MVVM gives some real-world advice on: Using modal dialogs Evils and virtues of code behind in views Use of attached behaviors Undo/redo strategies Working with animations Building a task based architecture for managing communication between View and ViewModel Frameworks in the MVVM space The Book Get first-hand experience implementing the solutions to the challenges discussed in the show by reading Josh’s new book ‘Advanced MVVM’. Resources The following resources are mentioned in the show: Laurent Bugnion's mix talk ‘Understanding the Model-View-ViewModel Pattern Josh Smith’s MVVM Foundation Laurent Bugnion’s MVVM Light framework Rob Eisenberg's Caliburn

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  • Improving Partitioned Table Join Performance

    - by Paul White
    The query optimizer does not always choose an optimal strategy when joining partitioned tables. This post looks at an example, showing how a manual rewrite of the query can almost double performance, while reducing the memory grant to almost nothing. Test Data The two tables in this example use a common partitioning partition scheme. The partition function uses 41 equal-size partitions: CREATE PARTITION FUNCTION PFT (integer) AS RANGE RIGHT FOR VALUES ( 125000, 250000, 375000, 500000, 625000, 750000, 875000, 1000000, 1125000, 1250000, 1375000, 1500000, 1625000, 1750000, 1875000, 2000000, 2125000, 2250000, 2375000, 2500000, 2625000, 2750000, 2875000, 3000000, 3125000, 3250000, 3375000, 3500000, 3625000, 3750000, 3875000, 4000000, 4125000, 4250000, 4375000, 4500000, 4625000, 4750000, 4875000, 5000000 ); GO CREATE PARTITION SCHEME PST AS PARTITION PFT ALL TO ([PRIMARY]); There two tables are: CREATE TABLE dbo.T1 ( TID integer NOT NULL IDENTITY(0,1), Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T1 PRIMARY KEY CLUSTERED (TID) ON PST (TID) );   CREATE TABLE dbo.T2 ( TID integer NOT NULL, Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T2 PRIMARY KEY CLUSTERED (TID, Column1) ON PST (TID) ); The next script loads 5 million rows into T1 with a pseudo-random value between 1 and 5 for Column1. The table is partitioned on the IDENTITY column TID: INSERT dbo.T1 WITH (TABLOCKX) (Column1) SELECT (ABS(CHECKSUM(NEWID())) % 5) + 1 FROM dbo.Numbers AS N WHERE n BETWEEN 1 AND 5000000; In case you don’t already have an auxiliary table of numbers lying around, here’s a script to create one with 10 million rows: CREATE TABLE dbo.Numbers (n bigint PRIMARY KEY);   WITH L0 AS(SELECT 1 AS c UNION ALL SELECT 1), L1 AS(SELECT 1 AS c FROM L0 AS A CROSS JOIN L0 AS B), L2 AS(SELECT 1 AS c FROM L1 AS A CROSS JOIN L1 AS B), L3 AS(SELECT 1 AS c FROM L2 AS A CROSS JOIN L2 AS B), L4 AS(SELECT 1 AS c FROM L3 AS A CROSS JOIN L3 AS B), L5 AS(SELECT 1 AS c FROM L4 AS A CROSS JOIN L4 AS B), Nums AS(SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS n FROM L5) INSERT dbo.Numbers WITH (TABLOCKX) SELECT TOP (10000000) n FROM Nums ORDER BY n OPTION (MAXDOP 1); Table T1 contains data like this: Next we load data into table T2. The relationship between the two tables is that table 2 contains ‘n’ rows for each row in table 1, where ‘n’ is determined by the value in Column1 of table T1. There is nothing particularly special about the data or distribution, by the way. INSERT dbo.T2 WITH (TABLOCKX) (TID, Column1) SELECT T.TID, N.n FROM dbo.T1 AS T JOIN dbo.Numbers AS N ON N.n >= 1 AND N.n <= T.Column1; Table T2 ends up containing about 15 million rows: The primary key for table T2 is a combination of TID and Column1. The data is partitioned according to the value in column TID alone. Partition Distribution The following query shows the number of rows in each partition of table T1: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T1 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are 40 partitions containing 125,000 rows (40 * 125k = 5m rows). The rightmost partition remains empty. The next query shows the distribution for table 2: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T2 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are roughly 375,000 rows in each partition (the rightmost partition is also empty): Ok, that’s the test data done. Test Query and Execution Plan The task is to count the rows resulting from joining tables 1 and 2 on the TID column: SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; The optimizer chooses a plan using parallel hash join, and partial aggregation: The Plan Explorer plan tree view shows accurate cardinality estimates and an even distribution of rows across threads (click to enlarge the image): With a warm data cache, the STATISTICS IO output shows that no physical I/O was needed, and all 41 partitions were touched: Running the query without actual execution plan or STATISTICS IO information for maximum performance, the query returns in around 2600ms. Execution Plan Analysis The first step toward improving on the execution plan produced by the query optimizer is to understand how it works, at least in outline. The two parallel Clustered Index Scans use multiple threads to read rows from tables T1 and T2. Parallel scan uses a demand-based scheme where threads are given page(s) to scan from the table as needed. This arrangement has certain important advantages, but does result in an unpredictable distribution of rows amongst threads. The point is that multiple threads cooperate to scan the whole table, but it is impossible to predict which rows end up on which threads. For correct results from the parallel hash join, the execution plan has to ensure that rows from T1 and T2 that might join are processed on the same thread. For example, if a row from T1 with join key value ‘1234’ is placed in thread 5’s hash table, the execution plan must guarantee that any rows from T2 that also have join key value ‘1234’ probe thread 5’s hash table for matches. The way this guarantee is enforced in this parallel hash join plan is by repartitioning rows to threads after each parallel scan. The two repartitioning exchanges route rows to threads using a hash function over the hash join keys. The two repartitioning exchanges use the same hash function so rows from T1 and T2 with the same join key must end up on the same hash join thread. Expensive Exchanges This business of repartitioning rows between threads can be very expensive, especially if a large number of rows is involved. The execution plan selected by the optimizer moves 5 million rows through one repartitioning exchange and around 15 million across the other. As a first step toward removing these exchanges, consider the execution plan selected by the optimizer if we join just one partition from each table, disallowing parallelism: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = 1 AND $PARTITION.PFT(T2.TID) = 1 OPTION (MAXDOP 1); The optimizer has chosen a (one-to-many) merge join instead of a hash join. The single-partition query completes in around 100ms. If everything scaled linearly, we would expect that extending this strategy to all 40 populated partitions would result in an execution time around 4000ms. Using parallelism could reduce that further, perhaps to be competitive with the parallel hash join chosen by the optimizer. This raises a question. If the most efficient way to join one partition from each of the tables is to use a merge join, why does the optimizer not choose a merge join for the full query? Forcing a Merge Join Let’s force the optimizer to use a merge join on the test query using a hint: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN); This is the execution plan selected by the optimizer: This plan results in the same number of logical reads reported previously, but instead of 2600ms the query takes 5000ms. The natural explanation for this drop in performance is that the merge join plan is only using a single thread, whereas the parallel hash join plan could use multiple threads. Parallel Merge Join We can get a parallel merge join plan using the same query hint as before, and adding trace flag 8649: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN, QUERYTRACEON 8649); The execution plan is: This looks promising. It uses a similar strategy to distribute work across threads as seen for the parallel hash join. In practice though, performance is disappointing. On a typical run, the parallel merge plan runs for around 8400ms; slower than the single-threaded merge join plan (5000ms) and much worse than the 2600ms for the parallel hash join. We seem to be going backwards! The logical reads for the parallel merge are still exactly the same as before, with no physical IOs. The cardinality estimates and thread distribution are also still very good (click to enlarge): A big clue to the reason for the poor performance is shown in the wait statistics (captured by Plan Explorer Pro): CXPACKET waits require careful interpretation, and are most often benign, but in this case excessive waiting occurs at the repartitioning exchanges. Unlike the parallel hash join, the repartitioning exchanges in this plan are order-preserving ‘merging’ exchanges (because merge join requires ordered inputs): Parallelism works best when threads can just grab any available unit of work and get on with processing it. Preserving order introduces inter-thread dependencies that can easily lead to significant waits occurring. In extreme cases, these dependencies can result in an intra-query deadlock, though the details of that will have to wait for another time to explore in detail. The potential for waits and deadlocks leads the query optimizer to cost parallel merge join relatively highly, especially as the degree of parallelism (DOP) increases. This high costing resulted in the optimizer choosing a serial merge join rather than parallel in this case. The test results certainly confirm its reasoning. Collocated Joins In SQL Server 2008 and later, the optimizer has another available strategy when joining tables that share a common partition scheme. This strategy is a collocated join, also known as as a per-partition join. It can be applied in both serial and parallel execution plans, though it is limited to 2-way joins in the current optimizer. Whether the optimizer chooses a collocated join or not depends on cost estimation. The primary benefits of a collocated join are that it eliminates an exchange and requires less memory, as we will see next. Costing and Plan Selection The query optimizer did consider a collocated join for our original query, but it was rejected on cost grounds. The parallel hash join with repartitioning exchanges appeared to be a cheaper option. There is no query hint to force a collocated join, so we have to mess with the costing framework to produce one for our test query. Pretending that IOs cost 50 times more than usual is enough to convince the optimizer to use collocated join with our test query: -- Pretend IOs are 50x cost temporarily DBCC SETIOWEIGHT(50);   -- Co-located hash join SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (RECOMPILE);   -- Reset IO costing DBCC SETIOWEIGHT(1); Collocated Join Plan The estimated execution plan for the collocated join is: The Constant Scan contains one row for each partition of the shared partitioning scheme, from 1 to 41. The hash repartitioning exchanges seen previously are replaced by a single Distribute Streams exchange using Demand partitioning. Demand partitioning means that the next partition id is given to the next parallel thread that asks for one. My test machine has eight logical processors, and all are available for SQL Server to use. As a result, there are eight threads in the single parallel branch in this plan, each processing one partition from each table at a time. Once a thread finishes processing a partition, it grabs a new partition number from the Distribute Streams exchange…and so on until all partitions have been processed. It is important to understand that the parallel scans in this plan are different from the parallel hash join plan. Although the scans have the same parallelism icon, tables T1 and T2 are not being co-operatively scanned by multiple threads in the same way. Each thread reads a single partition of T1 and performs a hash match join with the same partition from table T2. The properties of the two Clustered Index Scans show a Seek Predicate (unusual for a scan!) limiting the rows to a single partition: The crucial point is that the join between T1 and T2 is on TID, and TID is the partitioning column for both tables. A thread that processes partition ‘n’ is guaranteed to see all rows that can possibly join on TID for that partition. In addition, no other thread will see rows from that partition, so this removes the need for repartitioning exchanges. CPU and Memory Efficiency Improvements The collocated join has removed two expensive repartitioning exchanges and added a single exchange processing 41 rows (one for each partition id). Remember, the parallel hash join plan exchanges had to process 5 million and 15 million rows. The amount of processor time spent on exchanges will be much lower in the collocated join plan. In addition, the collocated join plan has a maximum of 8 threads processing single partitions at any one time. The 41 partitions will all be processed eventually, but a new partition is not started until a thread asks for it. Threads can reuse hash table memory for the new partition. The parallel hash join plan also had 8 hash tables, but with all 5,000,000 build rows loaded at the same time. The collocated plan needs memory for only 8 * 125,000 = 1,000,000 rows at any one time. Collocated Hash Join Performance The collated join plan has disappointing performance in this case. The query runs for around 25,300ms despite the same IO statistics as usual. This is much the worst result so far, so what went wrong? It turns out that cardinality estimation for the single partition scans of table T1 is slightly low. The properties of the Clustered Index Scan of T1 (graphic immediately above) show the estimation was for 121,951 rows. This is a small shortfall compared with the 125,000 rows actually encountered, but it was enough to cause the hash join to spill to physical tempdb: A level 1 spill doesn’t sound too bad, until you realize that the spill to tempdb probably occurs for each of the 41 partitions. As a side note, the cardinality estimation error is a little surprising because the system tables accurately show there are 125,000 rows in every partition of T1. Unfortunately, the optimizer uses regular column and index statistics to derive cardinality estimates here rather than system table information (e.g. sys.partitions). Collocated Merge Join We will never know how well the collocated parallel hash join plan might have worked without the cardinality estimation error (and the resulting 41 spills to tempdb) but we do know: Merge join does not require a memory grant; and Merge join was the optimizer’s preferred join option for a single partition join Putting this all together, what we would really like to see is the same collocated join strategy, but using merge join instead of hash join. Unfortunately, the current query optimizer cannot produce a collocated merge join; it only knows how to do collocated hash join. So where does this leave us? CROSS APPLY sys.partitions We can try to write our own collocated join query. We can use sys.partitions to find the partition numbers, and CROSS APPLY to get a count per partition, with a final step to sum the partial counts. The following query implements this idea: SELECT row_count = SUM(Subtotals.cnt) FROM ( -- Partition numbers SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1 ) AS P CROSS APPLY ( -- Count per collocated join SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals; The estimated plan is: The cardinality estimates aren’t all that good here, especially the estimate for the scan of the system table underlying the sys.partitions view. Nevertheless, the plan shape is heading toward where we would like to be. Each partition number from the system table results in a per-partition scan of T1 and T2, a one-to-many Merge Join, and a Stream Aggregate to compute the partial counts. The final Stream Aggregate just sums the partial counts. Execution time for this query is around 3,500ms, with the same IO statistics as always. This compares favourably with 5,000ms for the serial plan produced by the optimizer with the OPTION (MERGE JOIN) hint. This is another case of the sum of the parts being less than the whole – summing 41 partial counts from 41 single-partition merge joins is faster than a single merge join and count over all partitions. Even so, this single-threaded collocated merge join is not as quick as the original parallel hash join plan, which executed in 2,600ms. On the positive side, our collocated merge join uses only one logical processor and requires no memory grant. The parallel hash join plan used 16 threads and reserved 569 MB of memory:   Using a Temporary Table Our collocated merge join plan should benefit from parallelism. The reason parallelism is not being used is that the query references a system table. We can work around that by writing the partition numbers to a temporary table (or table variable): SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   CREATE TABLE #P ( partition_number integer PRIMARY KEY);   INSERT #P (partition_number) SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1;   SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals;   DROP TABLE #P;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; Using the temporary table adds a few logical reads, but the overall execution time is still around 3500ms, indistinguishable from the same query without the temporary table. The problem is that the query optimizer still doesn’t choose a parallel plan for this query, though the removal of the system table reference means that it could if it chose to: In fact the optimizer did enter the parallel plan phase of query optimization (running search 1 for a second time): Unfortunately, the parallel plan found seemed to be more expensive than the serial plan. This is a crazy result, caused by the optimizer’s cost model not reducing operator CPU costs on the inner side of a nested loops join. Don’t get me started on that, we’ll be here all night. In this plan, everything expensive happens on the inner side of a nested loops join. Without a CPU cost reduction to compensate for the added cost of exchange operators, candidate parallel plans always look more expensive to the optimizer than the equivalent serial plan. Parallel Collocated Merge Join We can produce the desired parallel plan using trace flag 8649 again: SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: One difference between this plan and the collocated hash join plan is that a Repartition Streams exchange operator is used instead of Distribute Streams. The effect is similar, though not quite identical. The Repartition uses round-robin partitioning, meaning the next partition id is pushed to the next thread in sequence. The Distribute Streams exchange seen earlier used Demand partitioning, meaning the next partition id is pulled across the exchange by the next thread that is ready for more work. There are subtle performance implications for each partitioning option, but going into that would again take us too far off the main point of this post. Performance The important thing is the performance of this parallel collocated merge join – just 1350ms on a typical run. The list below shows all the alternatives from this post (all timings include creation, population, and deletion of the temporary table where appropriate) from quickest to slowest: Collocated parallel merge join: 1350ms Parallel hash join: 2600ms Collocated serial merge join: 3500ms Serial merge join: 5000ms Parallel merge join: 8400ms Collated parallel hash join: 25,300ms (hash spill per partition) The parallel collocated merge join requires no memory grant (aside from a paltry 1.2MB used for exchange buffers). This plan uses 16 threads at DOP 8; but 8 of those are (rather pointlessly) allocated to the parallel scan of the temporary table. These are minor concerns, but it turns out there is a way to address them if it bothers you. Parallel Collocated Merge Join with Demand Partitioning This final tweak replaces the temporary table with a hard-coded list of partition ids (dynamic SQL could be used to generate this query from sys.partitions): SELECT row_count = SUM(Subtotals.cnt) FROM ( VALUES (1),(2),(3),(4),(5),(6),(7),(8),(9),(10), (11),(12),(13),(14),(15),(16),(17),(18),(19),(20), (21),(22),(23),(24),(25),(26),(27),(28),(29),(30), (31),(32),(33),(34),(35),(36),(37),(38),(39),(40),(41) ) AS P (partition_number) CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: The parallel collocated hash join plan is reproduced below for comparison: The manual rewrite has another advantage that has not been mentioned so far: the partial counts (per partition) can be computed earlier than the partial counts (per thread) in the optimizer’s collocated join plan. The earlier aggregation is performed by the extra Stream Aggregate under the nested loops join. The performance of the parallel collocated merge join is unchanged at around 1350ms. Final Words It is a shame that the current query optimizer does not consider a collocated merge join (Connect item closed as Won’t Fix). The example used in this post showed an improvement in execution time from 2600ms to 1350ms using a modestly-sized data set and limited parallelism. In addition, the memory requirement for the query was almost completely eliminated  – down from 569MB to 1.2MB. The problem with the parallel hash join selected by the optimizer is that it attempts to process the full data set all at once (albeit using eight threads). It requires a large memory grant to hold all 5 million rows from table T1 across the eight hash tables, and does not take advantage of the divide-and-conquer opportunity offered by the common partitioning. The great thing about the collocated join strategies is that each parallel thread works on a single partition from both tables, reading rows, performing the join, and computing a per-partition subtotal, before moving on to a new partition. From a thread’s point of view… If you have trouble visualizing what is happening from just looking at the parallel collocated merge join execution plan, let’s look at it again, but from the point of view of just one thread operating between the two Parallelism (exchange) operators. Our thread picks up a single partition id from the Distribute Streams exchange, and starts a merge join using ordered rows from partition 1 of table T1 and partition 1 of table T2. By definition, this is all happening on a single thread. As rows join, they are added to a (per-partition) count in the Stream Aggregate immediately above the Merge Join. Eventually, either T1 (partition 1) or T2 (partition 1) runs out of rows and the merge join stops. The per-partition count from the aggregate passes on through the Nested Loops join to another Stream Aggregate, which is maintaining a per-thread subtotal. Our same thread now picks up a new partition id from the exchange (say it gets id 9 this time). The count in the per-partition aggregate is reset to zero, and the processing of partition 9 of both tables proceeds just as it did for partition 1, and on the same thread. Each thread picks up a single partition id and processes all the data for that partition, completely independently from other threads working on other partitions. One thread might eventually process partitions (1, 9, 17, 25, 33, 41) while another is concurrently processing partitions (2, 10, 18, 26, 34) and so on for the other six threads at DOP 8. The point is that all 8 threads can execute independently and concurrently, continuing to process new partitions until the wider job (of which the thread has no knowledge!) is done. This divide-and-conquer technique can be much more efficient than simply splitting the entire workload across eight threads all at once. Related Reading Understanding and Using Parallelism in SQL Server Parallel Execution Plans Suck © 2013 Paul White – All Rights Reserved Twitter: @SQL_Kiwi

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  • Ubuntu 10.10 Mouse and Keyboard Freeze

    - by Kev
    I installed Ubuntu 10.10 today and have had mouse problem since. Symptoms: At some arbitrary point in time (frequency: 2-3 times per hour), the mouse and keyboard stops working for ever(may be). I start System monitor, I found out network was shutdown just before mouse freeze. Some time my keyboard keep typing one key. For example:77777777777777777777777777777777777777777777777777777.....(it keep typing for 20 sec) I found out a script just solve the freeze problem:(I hit Powerbutton) -----------------/etc/acpi/powerbtn.sh------------------------ event=button[ /]power action=/usr/sbin/fix_mouse.sh -----------------/usr/sbin/fix_mouse.sh------------------------ rmmod psmouse modprobe psmouse Yesterday I install Ubuntu 10.04 FAILED also have mouse problem. When I switch back to Windows XP. The network card is down. It kept connecting and disconnecting 1 time per sec. CPU: i5 Motherboard: ASUS P7P55D OS: Windows XP + Ubuntu 10.10 Video Card: ATI 5770 Mouse,Keyboard: PS/2

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  • Dell Latitude E6520 overheating

    - by Wu Yi Han
    I'm a newcomer to Ubuntu 11.10. My laptop is a Dell Latitude E6520, Sandy Bridge platform. The system cooling fan is crazy all the time. I don't do any intensive tasks. I really hope my laptop doesn't become a mushroom cloud. I suppose there's no perfect way to solve this... Can I lower the CPU frequency? Jupiter 0.0.51 was installed (power save mode). Cooling worked in my Windows 7 system until I deleted it. (I won't go back to Windows 7.)

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  • Ubuntu 12.04 installation aborts without giving any errors on Sony Vaio

    - by Guilherme Simoni
    I'm not able to install the release ubuntu-12.04-desktop-i386 on the laptop below: Sony Vaio VGN-FE21H CPU: Intel Core Duo T2300 1.66GHz Memory: 2GB DDR2 533MHz HDD: 100GB Graphics: NVIDIA GeForce 7400 256MB I'm using the ISO "ubuntu-12.04-desktop-i386.iso" burned into a DVD. I know the ISO is OK because I used it to successfully install on Virtualbox. Live DVD boots and runs OK, but I cannot install from it or directly from the boot menu. The installation goes through all the steps until the final part where is asked the Name, Name of PC and password. The problem is in the next step where it should start copying files and present some screens and features of Ubuntu. In this part the installation just close without any error message. If I am running the installation inside the live DVD it closes and returns to the home screen of the Live. If I am running straight from the boot it closes the graphic interface and restarts the PC. Does anybody know or faced the same problem?

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