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  • Google I/O 2010 - Measure in milliseconds: Meet Speed Tracer

    Google I/O 2010 - Measure in milliseconds: Meet Speed Tracer Google I/O 2010 - Measure in milliseconds redux: Meet Speed Tracer GWT 201 Kelly Norton It turns out that web apps can be slow for all sorts of opaque and unintuitive reasons. Don't be fooled into thinking that bloated, slow JavaScript is the only culprit. This session introduces you to Speed Tracer, a new GWT tool that can tell you exactly where time is going within the browser. For all I/O 2010 sessions, please go to code.google.com From: GoogleDevelopers Views: 7 0 ratings Time: 01:00:53 More in Science & Technology

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  • 256 Windows Azure Worker Roles, Windows Kinect and a 90's Text-Based Ray-Tracer

    - by Alan Smith
    For a couple of years I have been demoing a simple render farm hosted in Windows Azure using worker roles and the Azure Storage service. At the start of the presentation I deploy an Azure application that uses 16 worker roles to render a 1,500 frame 3D ray-traced animation. At the end of the presentation, when the animation was complete, I would play the animation delete the Azure deployment. The standing joke with the audience was that it was that it was a “$2 demo”, as the compute charges for running the 16 instances for an hour was $1.92, factor in the bandwidth charges and it’s a couple of dollars. The point of the demo is that it highlights one of the great benefits of cloud computing, you pay for what you use, and if you need massive compute power for a short period of time using Windows Azure can work out very cost effective. The “$2 demo” was great for presenting at user groups and conferences in that it could be deployed to Azure, used to render an animation, and then removed in a one hour session. I have always had the idea of doing something a bit more impressive with the demo, and scaling it from a “$2 demo” to a “$30 demo”. The challenge was to create a visually appealing animation in high definition format and keep the demo time down to one hour.  This article will take a run through how I achieved this. Ray Tracing Ray tracing, a technique for generating high quality photorealistic images, gained popularity in the 90’s with companies like Pixar creating feature length computer animations, and also the emergence of shareware text-based ray tracers that could run on a home PC. In order to render a ray traced image, the ray of light that would pass from the view point must be tracked until it intersects with an object. At the intersection, the color, reflectiveness, transparency, and refractive index of the object are used to calculate if the ray will be reflected or refracted. Each pixel may require thousands of calculations to determine what color it will be in the rendered image. Pin-Board Toys Having very little artistic talent and a basic understanding of maths I decided to focus on an animation that could be modeled fairly easily and would look visually impressive. I’ve always liked the pin-board desktop toys that become popular in the 80’s and when I was working as a 3D animator back in the 90’s I always had the idea of creating a 3D ray-traced animation of a pin-board, but never found the energy to do it. Even if I had a go at it, the render time to produce an animation that would look respectable on a 486 would have been measured in months. PolyRay Back in 1995 I landed my first real job, after spending three years being a beach-ski-climbing-paragliding-bum, and was employed to create 3D ray-traced animations for a CD-ROM that school kids would use to learn physics. I had got into the strange and wonderful world of text-based ray tracing, and was using a shareware ray-tracer called PolyRay. PolyRay takes a text file describing a scene as input and, after a few hours processing on a 486, produced a high quality ray-traced image. The following is an example of a basic PolyRay scene file. background Midnight_Blue   static define matte surface { ambient 0.1 diffuse 0.7 } define matte_white texture { matte { color white } } define matte_black texture { matte { color dark_slate_gray } } define position_cylindrical 3 define lookup_sawtooth 1 define light_wood <0.6, 0.24, 0.1> define median_wood <0.3, 0.12, 0.03> define dark_wood <0.05, 0.01, 0.005>     define wooden texture { noise surface { ambient 0.2  diffuse 0.7  specular white, 0.5 microfacet Reitz 10 position_fn position_cylindrical position_scale 1  lookup_fn lookup_sawtooth octaves 1 turbulence 1 color_map( [0.0, 0.2, light_wood, light_wood] [0.2, 0.3, light_wood, median_wood] [0.3, 0.4, median_wood, light_wood] [0.4, 0.7, light_wood, light_wood] [0.7, 0.8, light_wood, median_wood] [0.8, 0.9, median_wood, light_wood] [0.9, 1.0, light_wood, dark_wood]) } } define glass texture { surface { ambient 0 diffuse 0 specular 0.2 reflection white, 0.1 transmission white, 1, 1.5 }} define shiny surface { ambient 0.1 diffuse 0.6 specular white, 0.6 microfacet Phong 7  } define steely_blue texture { shiny { color black } } define chrome texture { surface { color white ambient 0.0 diffuse 0.2 specular 0.4 microfacet Phong 10 reflection 0.8 } }   viewpoint {     from <4.000, -1.000, 1.000> at <0.000, 0.000, 0.000> up <0, 1, 0> angle 60     resolution 640, 480 aspect 1.6 image_format 0 }       light <-10, 30, 20> light <-10, 30, -20>   object { disc <0, -2, 0>, <0, 1, 0>, 30 wooden }   object { sphere <0.000, 0.000, 0.000>, 1.00 chrome } object { cylinder <0.000, 0.000, 0.000>, <0.000, 0.000, -4.000>, 0.50 chrome }   After setting up the background and defining colors and textures, the viewpoint is specified. The “camera” is located at a point in 3D space, and it looks towards another point. The angle, image resolution, and aspect ratio are specified. Two lights are present in the image at defined coordinates. The three objects in the image are a wooden disc to represent a table top, and a sphere and cylinder that intersect to form a pin that will be used for the pin board toy in the final animation. When the image is rendered, the following image is produced. The pins are modeled with a chrome surface, so they reflect the environment around them. Note that the scale of the pin shaft is not correct, this will be fixed later. Modeling the Pin Board The frame of the pin-board is made up of three boxes, and six cylinders, the front box is modeled using a clear, slightly reflective solid, with the same refractive index of glass. The other shapes are modeled as metal. object { box <-5.5, -1.5, 1>, <5.5, 5.5, 1.2> glass } object { box <-5.5, -1.5, -0.04>, <5.5, 5.5, -0.09> steely_blue } object { box <-5.5, -1.5, -0.52>, <5.5, 5.5, -0.59> steely_blue } object { cylinder <-5.2, -1.2, 1.4>, <-5.2, -1.2, -0.74>, 0.2 steely_blue } object { cylinder <5.2, -1.2, 1.4>, <5.2, -1.2, -0.74>, 0.2 steely_blue } object { cylinder <-5.2, 5.2, 1.4>, <-5.2, 5.2, -0.74>, 0.2 steely_blue } object { cylinder <5.2, 5.2, 1.4>, <5.2, 5.2, -0.74>, 0.2 steely_blue } object { cylinder <0, -1.2, 1.4>, <0, -1.2, -0.74>, 0.2 steely_blue } object { cylinder <0, 5.2, 1.4>, <0, 5.2, -0.74>, 0.2 steely_blue }   In order to create the matrix of pins that make up the pin board I used a basic console application with a few nested loops to create two intersecting matrixes of pins, which models the layout used in the pin boards. The resulting image is shown below. The pin board contains 11,481 pins, with the scene file containing 23,709 lines of code. For the complete animation 2,000 scene files will be created, which is over 47 million lines of code. Each pin in the pin-board will slide out a specific distance when an object is pressed into the back of the board. This is easily modeled by setting the Z coordinate of the pin to a specific value. In order to set all of the pins in the pin-board to the correct position, a bitmap image can be used. The position of the pin can be set based on the color of the pixel at the appropriate position in the image. When the Windows Azure logo is used to set the Z coordinate of the pins, the following image is generated. The challenge now was to make a cool animation. The Azure Logo is fine, but it is static. Using a normal video to animate the pins would not work; the colors in the video would not be the same as the depth of the objects from the camera. In order to simulate the pin board accurately a series of frames from a depth camera could be used. Windows Kinect The Kenect controllers for the X-Box 360 and Windows feature a depth camera. The Kinect SDK for Windows provides a programming interface for Kenect, providing easy access for .NET developers to the Kinect sensors. The Kinect Explorer provided with the Kinect SDK is a great starting point for exploring Kinect from a developers perspective. Both the X-Box 360 Kinect and the Windows Kinect will work with the Kinect SDK, the Windows Kinect is required for commercial applications, but the X-Box Kinect can be used for hobby projects. The Windows Kinect has the advantage of providing a mode to allow depth capture with objects closer to the camera, which makes for a more accurate depth image for setting the pin positions. Creating a Depth Field Animation The depth field animation used to set the positions of the pin in the pin board was created using a modified version of the Kinect Explorer sample application. In order to simulate the pin board accurately, a small section of the depth range from the depth sensor will be used. Any part of the object in front of the depth range will result in a white pixel; anything behind the depth range will be black. Within the depth range the pixels in the image will be set to RGB values from 0,0,0 to 255,255,255. A screen shot of the modified Kinect Explorer application is shown below. The Kinect Explorer sample application was modified to include slider controls that are used to set the depth range that forms the image from the depth stream. This allows the fine tuning of the depth image that is required for simulating the position of the pins in the pin board. The Kinect Explorer was also modified to record a series of images from the depth camera and save them as a sequence JPEG files that will be used to animate the pins in the animation the Start and Stop buttons are used to start and stop the image recording. En example of one of the depth images is shown below. Once a series of 2,000 depth images has been captured, the task of creating the animation can begin. Rendering a Test Frame In order to test the creation of frames and get an approximation of the time required to render each frame a test frame was rendered on-premise using PolyRay. The output of the rendering process is shown below. The test frame contained 23,629 primitive shapes, most of which are the spheres and cylinders that are used for the 11,800 or so pins in the pin board. The 1280x720 image contains 921,600 pixels, but as anti-aliasing was used the number of rays that were calculated was 4,235,777, with 3,478,754,073 object boundaries checked. The test frame of the pin board with the depth field image applied is shown below. The tracing time for the test frame was 4 minutes 27 seconds, which means rendering the2,000 frames in the animation would take over 148 hours, or a little over 6 days. Although this is much faster that an old 486, waiting almost a week to see the results of an animation would make it challenging for animators to create, view, and refine their animations. It would be much better if the animation could be rendered in less than one hour. Windows Azure Worker Roles The cost of creating an on-premise render farm to render animations increases in proportion to the number of servers. The table below shows the cost of servers for creating a render farm, assuming a cost of $500 per server. Number of Servers Cost 1 $500 16 $8,000 256 $128,000   As well as the cost of the servers, there would be additional costs for networking, racks etc. Hosting an environment of 256 servers on-premise would require a server room with cooling, and some pretty hefty power cabling. The Windows Azure compute services provide worker roles, which are ideal for performing processor intensive compute tasks. With the scalability available in Windows Azure a job that takes 256 hours to complete could be perfumed using different numbers of worker roles. The time and cost of using 1, 16 or 256 worker roles is shown below. Number of Worker Roles Render Time Cost 1 256 hours $30.72 16 16 hours $30.72 256 1 hour $30.72   Using worker roles in Windows Azure provides the same cost for the 256 hour job, irrespective of the number of worker roles used. Provided the compute task can be broken down into many small units, and the worker role compute power can be used effectively, it makes sense to scale the application so that the task is completed quickly, making the results available in a timely fashion. The task of rendering 2,000 frames in an animation is one that can easily be broken down into 2,000 individual pieces, which can be performed by a number of worker roles. Creating a Render Farm in Windows Azure The architecture of the render farm is shown in the following diagram. The render farm is a hybrid application with the following components: ·         On-Premise o   Windows Kinect – Used combined with the Kinect Explorer to create a stream of depth images. o   Animation Creator – This application uses the depth images from the Kinect sensor to create scene description files for PolyRay. These files are then uploaded to the jobs blob container, and job messages added to the jobs queue. o   Process Monitor – This application queries the role instance lifecycle table and displays statistics about the render farm environment and render process. o   Image Downloader – This application polls the image queue and downloads the rendered animation files once they are complete. ·         Windows Azure o   Azure Storage – Queues and blobs are used for the scene description files and completed frames. A table is used to store the statistics about the rendering environment.   The architecture of each worker role is shown below.   The worker role is configured to use local storage, which provides file storage on the worker role instance that can be use by the applications to render the image and transform the format of the image. The service definition for the worker role with the local storage configuration highlighted is shown below. <?xml version="1.0" encoding="utf-8"?> <ServiceDefinition name="CloudRay" >   <WorkerRole name="CloudRayWorkerRole" vmsize="Small">     <Imports>     </Imports>     <ConfigurationSettings>       <Setting name="DataConnectionString" />     </ConfigurationSettings>     <LocalResources>       <LocalStorage name="RayFolder" cleanOnRoleRecycle="true" />     </LocalResources>   </WorkerRole> </ServiceDefinition>     The two executable programs, PolyRay.exe and DTA.exe are included in the Azure project, with Copy Always set as the property. PolyRay will take the scene description file and render it to a Truevision TGA file. As the TGA format has not seen much use since the mid 90’s it is converted to a JPG image using Dave's Targa Animator, another shareware application from the 90’s. Each worker roll will use the following process to render the animation frames. 1.       The worker process polls the job queue, if a job is available the scene description file is downloaded from blob storage to local storage. 2.       PolyRay.exe is started in a process with the appropriate command line arguments to render the image as a TGA file. 3.       DTA.exe is started in a process with the appropriate command line arguments convert the TGA file to a JPG file. 4.       The JPG file is uploaded from local storage to the images blob container. 5.       A message is placed on the images queue to indicate a new image is available for download. 6.       The job message is deleted from the job queue. 7.       The role instance lifecycle table is updated with statistics on the number of frames rendered by the worker role instance, and the CPU time used. The code for this is shown below. public override void Run() {     // Set environment variables     string polyRayPath = Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), PolyRayLocation);     string dtaPath = Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), DTALocation);       LocalResource rayStorage = RoleEnvironment.GetLocalResource("RayFolder");     string localStorageRootPath = rayStorage.RootPath;       JobQueue jobQueue = new JobQueue("renderjobs");     JobQueue downloadQueue = new JobQueue("renderimagedownloadjobs");     CloudRayBlob sceneBlob = new CloudRayBlob("scenes");     CloudRayBlob imageBlob = new CloudRayBlob("images");     RoleLifecycleDataSource roleLifecycleDataSource = new RoleLifecycleDataSource();       Frames = 0;       while (true)     {         // Get the render job from the queue         CloudQueueMessage jobMsg = jobQueue.Get();           if (jobMsg != null)         {             // Get the file details             string sceneFile = jobMsg.AsString;             string tgaFile = sceneFile.Replace(".pi", ".tga");             string jpgFile = sceneFile.Replace(".pi", ".jpg");               string sceneFilePath = Path.Combine(localStorageRootPath, sceneFile);             string tgaFilePath = Path.Combine(localStorageRootPath, tgaFile);             string jpgFilePath = Path.Combine(localStorageRootPath, jpgFile);               // Copy the scene file to local storage             sceneBlob.DownloadFile(sceneFilePath);               // Run the ray tracer.             string polyrayArguments =                 string.Format("\"{0}\" -o \"{1}\" -a 2", sceneFilePath, tgaFilePath);             Process polyRayProcess = new Process();             polyRayProcess.StartInfo.FileName =                 Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), polyRayPath);             polyRayProcess.StartInfo.Arguments = polyrayArguments;             polyRayProcess.Start();             polyRayProcess.WaitForExit();               // Convert the image             string dtaArguments =                 string.Format(" {0} /FJ /P{1}", tgaFilePath, Path.GetDirectoryName (jpgFilePath));             Process dtaProcess = new Process();             dtaProcess.StartInfo.FileName =                 Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), dtaPath);             dtaProcess.StartInfo.Arguments = dtaArguments;             dtaProcess.Start();             dtaProcess.WaitForExit();               // Upload the image to blob storage             imageBlob.UploadFile(jpgFilePath);               // Add a download job.             downloadQueue.Add(jpgFile);               // Delete the render job message             jobQueue.Delete(jobMsg);               Frames++;         }         else         {             Thread.Sleep(1000);         }           // Log the worker role activity.         roleLifecycleDataSource.Alive             ("CloudRayWorker", RoleLifecycleDataSource.RoleLifecycleId, Frames);     } }     Monitoring Worker Role Instance Lifecycle In order to get more accurate statistics about the lifecycle of the worker role instances used to render the animation data was tracked in an Azure storage table. The following class was used to track the worker role lifecycles in Azure storage.   public class RoleLifecycle : TableServiceEntity {     public string ServerName { get; set; }     public string Status { get; set; }     public DateTime StartTime { get; set; }     public DateTime EndTime { get; set; }     public long SecondsRunning { get; set; }     public DateTime LastActiveTime { get; set; }     public int Frames { get; set; }     public string Comment { get; set; }       public RoleLifecycle()     {     }       public RoleLifecycle(string roleName)     {         PartitionKey = roleName;         RowKey = Utils.GetAscendingRowKey();         Status = "Started";         StartTime = DateTime.UtcNow;         LastActiveTime = StartTime;         EndTime = StartTime;         SecondsRunning = 0;         Frames = 0;     } }     A new instance of this class is created and added to the storage table when the role starts. It is then updated each time the worker renders a frame to record the total number of frames rendered and the total processing time. These statistics are used be the monitoring application to determine the effectiveness of use of resources in the render farm. Rendering the Animation The Azure solution was deployed to Windows Azure with the service configuration set to 16 worker role instances. This allows for the application to be tested in the cloud environment, and the performance of the application determined. When I demo the application at conferences and user groups I often start with 16 instances, and then scale up the application to the full 256 instances. The configuration to run 16 instances is shown below. <?xml version="1.0" encoding="utf-8"?> <ServiceConfiguration serviceName="CloudRay" xmlns="http://schemas.microsoft.com/ServiceHosting/2008/10/ServiceConfiguration" osFamily="1" osVersion="*">   <Role name="CloudRayWorkerRole">     <Instances count="16" />     <ConfigurationSettings>       <Setting name="DataConnectionString"         value="DefaultEndpointsProtocol=https;AccountName=cloudraydata;AccountKey=..." />     </ConfigurationSettings>   </Role> </ServiceConfiguration>     About six minutes after deploying the application the first worker roles become active and start to render the first frames of the animation. The CloudRay Monitor application displays an icon for each worker role instance, with a number indicating the number of frames that the worker role has rendered. The statistics on the left show the number of active worker roles and statistics about the render process. The render time is the time since the first worker role became active; the CPU time is the total amount of processing time used by all worker role instances to render the frames.   Five minutes after the first worker role became active the last of the 16 worker roles activated. By this time the first seven worker roles had each rendered one frame of the animation.   With 16 worker roles u and running it can be seen that one hour and 45 minutes CPU time has been used to render 32 frames with a render time of just under 10 minutes.     At this rate it would take over 10 hours to render the 2,000 frames of the full animation. In order to complete the animation in under an hour more processing power will be required. Scaling the render farm from 16 instances to 256 instances is easy using the new management portal. The slider is set to 256 instances, and the configuration saved. We do not need to re-deploy the application, and the 16 instances that are up and running will not be affected. Alternatively, the configuration file for the Azure service could be modified to specify 256 instances.   <?xml version="1.0" encoding="utf-8"?> <ServiceConfiguration serviceName="CloudRay" xmlns="http://schemas.microsoft.com/ServiceHosting/2008/10/ServiceConfiguration" osFamily="1" osVersion="*">   <Role name="CloudRayWorkerRole">     <Instances count="256" />     <ConfigurationSettings>       <Setting name="DataConnectionString"         value="DefaultEndpointsProtocol=https;AccountName=cloudraydata;AccountKey=..." />     </ConfigurationSettings>   </Role> </ServiceConfiguration>     Six minutes after the new configuration has been applied 75 new worker roles have activated and are processing their first frames.   Five minutes later the full configuration of 256 worker roles is up and running. We can see that the average rate of frame rendering has increased from 3 to 12 frames per minute, and that over 17 hours of CPU time has been utilized in 23 minutes. In this test the time to provision 140 worker roles was about 11 minutes, which works out at about one every five seconds.   We are now half way through the rendering, with 1,000 frames complete. This has utilized just under three days of CPU time in a little over 35 minutes.   The animation is now complete, with 2,000 frames rendered in a little over 52 minutes. The CPU time used by the 256 worker roles is 6 days, 7 hours and 22 minutes with an average frame rate of 38 frames per minute. The rendering of the last 1,000 frames took 16 minutes 27 seconds, which works out at a rendering rate of 60 frames per minute. The frame counts in the server instances indicate that the use of a queue to distribute the workload has been very effective in distributing the load across the 256 worker role instances. The first 16 instances that were deployed first have rendered between 11 and 13 frames each, whilst the 240 instances that were added when the application was scaled have rendered between 6 and 9 frames each.   Completed Animation I’ve uploaded the completed animation to YouTube, a low resolution preview is shown below. Pin Board Animation Created using Windows Kinect and 256 Windows Azure Worker Roles   The animation can be viewed in 1280x720 resolution at the following link: http://www.youtube.com/watch?v=n5jy6bvSxWc Effective Use of Resources According to the CloudRay monitor statistics the animation took 6 days, 7 hours and 22 minutes CPU to render, this works out at 152 hours of compute time, rounded up to the nearest hour. As the usage for the worker role instances are billed for the full hour, it may have been possible to render the animation using fewer than 256 worker roles. When deciding the optimal usage of resources, the time required to provision and start the worker roles must also be considered. In the demo I started with 16 worker roles, and then scaled the application to 256 worker roles. It would have been more optimal to start the application with maybe 200 worker roles, and utilized the full hour that I was being billed for. This would, however, have prevented showing the ease of scalability of the application. The new management portal displays the CPU usage across the worker roles in the deployment. The average CPU usage across all instances is 93.27%, with over 99% used when all the instances are up and running. This shows that the worker role resources are being used very effectively. Grid Computing Scenarios Although I am using this scenario for a hobby project, there are many scenarios where a large amount of compute power is required for a short period of time. Windows Azure provides a great platform for developing these types of grid computing applications, and can work out very cost effective. ·         Windows Azure can provide massive compute power, on demand, in a matter of minutes. ·         The use of queues to manage the load balancing of jobs between role instances is a simple and effective solution. ·         Using a cloud-computing platform like Windows Azure allows proof-of-concept scenarios to be tested and evaluated on a very low budget. ·         No charges for inbound data transfer makes the uploading of large data sets to Windows Azure Storage services cost effective. (Transaction charges still apply.) Tips for using Windows Azure for Grid Computing Scenarios I found the implementation of a render farm using Windows Azure a fairly simple scenario to implement. I was impressed by ease of scalability that Azure provides, and by the short time that the application took to scale from 16 to 256 worker role instances. In this case it was around 13 minutes, in other tests it took between 10 and 20 minutes. The following tips may be useful when implementing a grid computing project in Windows Azure. ·         Using an Azure Storage queue to load-balance the units of work across multiple worker roles is simple and very effective. The design I have used in this scenario could easily scale to many thousands of worker role instances. ·         Windows Azure accounts are typically limited to 20 cores. If you need to use more than this, a call to support and a credit card check will be required. ·         Be aware of how the billing model works. You will be charged for worker role instances for the full clock our in which the instance is deployed. Schedule the workload to start just after the clock hour has started. ·         Monitor the utilization of the resources you are provisioning, ensure that you are not paying for worker roles that are idle. ·         If you are deploying third party applications to worker roles, you may well run into licensing issues. Purchasing software licenses on a per-processor basis when using hundreds of processors for a short time period would not be cost effective. ·         Third party software may also require installation onto the worker roles, which can be accomplished using start-up tasks. Bear in mind that adding a startup task and possible re-boot will add to the time required for the worker role instance to start and activate. An alternative may be to use a prepared VM and use VM roles. ·         Consider using the Windows Azure Autoscaling Application Block (WASABi) to autoscale the worker roles in your application. When using a large number of worker roles, the utilization must be carefully monitored, if the scaling algorithms are not optimal it could get very expensive!

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  • How do I run Cisco Packet Tracer 6.0.1?

    - by user186296
    I have a problem running Packet tracer 6.0.1 on Ubuntu 13.04. I have a downloaded file from Cisco Netspace - this file had not extension. I added the extension tar.gz to this file and I could unpack it to a location with other folders and files, there was a file install too. This was a .bin file and I used sudo chmod +x on this file and I used ./filename and installation has been launched I had to accept licence and so on. After successfully installation I tried to launch Packet Tracer and nothing happened. Please help me, how can I correctly install and run Packet Tracer. Note that I'm new in using Linux.

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  • Literature and Tutorials for Writing a Ray Tracer

    - by grrussel
    I am interested in finding recommendations on books on writing a raytracer, simple and clear implementations of ray tracing that can be seen on the web, and online resources on introductory raytracing. Ideally, the approach would be incremental and tutorial in style, and explain both the programming techniques and underyling mathematics, starting from the basics.

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  • AT&T brevète une technologie permettant de tracer le téléchargement de contenu illicite sur les réseaux P2P

    Serait ce vraiment la fin du réseau peer-to-peer ? AT&T vient de breveter une technologie permettant de tracer le téléchargement de contenu illicite dans les réseaux P2PLes réseaux P2P : adulés par une très forte communauté d'utilisateurs, détestés par les organismes de protection de la propriété intellectuelle attirent de nouveau l'attention des médias. Cette fois-ci, le P2P est mis au-devant de la scène par la compagnie AT&T. Cette dernière vient de breveter une technologie qui permet une bonne traçabilité du contenu des réseaux peer-to-peer.La principale cible de cette technologie sont les fichiers torrents auxquels les utilisateurs ont accès par le biais des flux RSS et moteurs de recherche de ...

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  • Fortinet: Is there any equivalent of the ASA's packet-tracer command?

    - by Kedare
    I would like to know if there is not Fortigates an equivalent of the packet-tracer command that we can find on the ASA. Here is an example of execution for those who don't know it: NAT and pass : lev5505# packet-tracer input inside tcp 192.168.3.20 9876 8.8.8.8 80 Phase: 1 Type: ACCESS-LIST Subtype: Result: ALLOW Config: Implicit Rule Additional Information: MAC Access list Phase: 2 Type: ROUTE-LOOKUP Subtype: input Result: ALLOW Config: Additional Information: in 0.0.0.0 0.0.0.0 outside Phase: 3 Type: ACCESS-LIST Subtype: log Result: ALLOW Config: access-group inside-in in interface inside access-list inside-in extended permit tcp any any eq www access-list inside-in remark Allows DNS Additional Information: Phase: 4 Type: IP-OPTIONS Subtype: Result: ALLOW Config: Additional Information: Phase: 5 Type: VPN Subtype: ipsec-tunnel-flow Result: ALLOW Config: Additional Information: Phase: 6 Type: NAT Subtype: Result: ALLOW Config: object network inside-network nat (inside,outside) dynamic interface Additional Information: Dynamic translate 192.168.3.20/9876 to 81.56.15.183/9876 Phase: 7 Type: IP-OPTIONS Subtype: Result: ALLOW Config: Additional Information: Phase: 8 Type: FLOW-CREATION Subtype: Result: ALLOW Config: Additional Information: New flow created with id 94755, packet dispatched to next module Result: input-interface: inside input-status: up input-line-status: up output-interface: outside output-status: up output-line-status: up Action: allow Blocked by ACL: lev5505# packet-tracer input inside tcp 192.168.3.20 9876 8.8.8.8 81 Phase: 1 Type: ROUTE-LOOKUP Subtype: input Result: ALLOW Config: Additional Information: in 0.0.0.0 0.0.0.0 outside Phase: 2 Type: ACCESS-LIST Subtype: Result: DROP Config: Implicit Rule Additional Information: Result: input-interface: inside input-status: up input-line-status: up output-interface: outside output-status: up output-line-status: up Action: drop Drop-reason: (acl-drop) Flow is denied by configured rule Is there any equivalent on the Fortigates ?

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  • Google Chrome Speed Tracer what does Request Timing and Response Timing actually measure?

    - by Bryce Thomas
    I'm testing out the Google Chrome Speed Tracer on a few common web pages and taking a look through the results. One thing I'm not sure I understand is what the "Request Timing" and "Response Timing" properties of resources are actually measuring. Initially I thought Request Timing must measure the time from a request for a resource being sent and when that request arrived at the server. However, I then wondered how the Speed Tracer would actually have any way of measuring this. Furthermore, the Response Timing that I'm getting for resources tends to be far less than the Request Timing (e.g. 500ms request, 1ms response), which is a little bit suss. So is anyone able to explain exactly what Request Timing and Response Timing are measuring?

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  • How do I use texture-mapping in a simple ray tracer?

    - by fastrack20
    I am attempting to add features to a ray tracer in C++. Namely, I am trying to add texture mapping to the spheres. For simplicity, I am using an array to store the texture data. I obtained the texture data by using a hex editor and copying the correct byte values into an array in my code. This was just for my testing purposes. When the values of this array correspond to an image that is simply red, it appears to work close to what is expected except there is no shading. The bottom right of the image shows what a correct sphere should look like. This sphere's colour using one set colour, not a texture map. Another problem is that when the texture map is of something other than just one colour pixels, it turns white. My test image is a picture of water, and when it maps, it shows only one ring of bluish pixels surrounding the white colour. When this is done, it simply appears as this: Here are a few code snippets: Color getColor(const Object *object,const Ray *ray, float *t) { if (object->materialType == TEXTDIF || object->materialType == TEXTMATTE) { float distance = *t; Point pnt = ray->origin + ray->direction * distance; Point oc = object->center; Vector ve = Point(oc.x,oc.y,oc.z+1) - oc; Normalize(&ve); Vector vn = Point(oc.x,oc.y+1,oc.z) - oc; Normalize(&vn); Vector vp = pnt - oc; Normalize(&vp); double phi = acos(-vn.dot(vp)); float v = phi / M_PI; float u; float num1 = (float)acos(vp.dot(ve)); float num = (num1 /(float) sin(phi)); float theta = num /(float) (2 * M_PI); if (theta < 0 || theta == NAN) {theta = 0;} if (vn.cross(ve).dot(vp) > 0) { u = theta; } else { u = 1 - theta; } int x = (u * IMAGE_WIDTH) -1; int y = (v * IMAGE_WIDTH) -1; int p = (y * IMAGE_WIDTH + x)*3; return Color(TEXT_DATA[p+2],TEXT_DATA[p+1],TEXT_DATA[p]); } else { return object->color; } }; I call the colour code here in Trace: if (object->materialType == MATTE) return getColor(object, ray, &t); Ray shadowRay; int isInShadow = 0; shadowRay.origin.x = pHit.x + nHit.x * bias; shadowRay.origin.y = pHit.y + nHit.y * bias; shadowRay.origin.z = pHit.z + nHit.z * bias; shadowRay.direction = light->object->center - pHit; float len = shadowRay.direction.length(); Normalize(&shadowRay.direction); float LdotN = shadowRay.direction.dot(nHit); if (LdotN < 0) return 0; Color lightColor = light->object->color; for (int k = 0; k < numObjects; k++) { if (Intersect(objects[k], &shadowRay, &t) && !objects[k]->isLight) { if (objects[k]->materialType == GLASS) lightColor *= getColor(objects[k], &shadowRay, &t); // attenuate light color by glass color else isInShadow = 1; break; } } lightColor *= 1.f/(len*len); return (isInShadow) ? 0 : getColor(object, &shadowRay, &t) * lightColor * LdotN; } I left out the rest of the code as to not bog down the post, but it can be seen here. Any help is greatly appreciated. The only portion not included in the code, is where I define the texture data, which as I said, is simply taken straight from a bitmap file of the above image. Thanks.

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  • The Return Of __FILE__ And __LINE__ In .NET 4.5

    - by Alois Kraus
    Good things are hard to kill. One of the most useful predefined compiler macros in C/C++ were __FILE__ and __LINE__ which do expand to the compilation units file name and line number where this value is encountered by the compiler. After 4.5 versions of .NET we are on par with C/C++ again. It is of course not a simple compiler expandable macro it is an attribute but it does serve exactly the same purpose. Now we do get CallerLineNumberAttribute  == __LINE__ CallerFilePathAttribute        == __FILE__ CallerMemberNameAttribute  == __FUNCTION__ (MSVC Extension)   The most important one is CallerMemberNameAttribute which is very useful to implement the INotifyPropertyChanged interface without the need to hard code the name of the property anymore. Now you can simply decorate your change method with the new CallerMemberName attribute and you get the property name as string directly inserted by the C# compiler at compile time.   public string UserName { get { return _userName; } set { _userName=value; RaisePropertyChanged(); // no more RaisePropertyChanged(“UserName”)! } } protected void RaisePropertyChanged([CallerMemberName] string member = "") { var copy = PropertyChanged; if(copy != null) { copy(new PropertyChangedEventArgs(this, member)); } } Nice and handy. This was obviously the prime reason to implement this feature in the C# 5.0 compiler. You can repurpose this feature for tracing to get your hands on the method name of your caller along other stuff very fast now. All infos are added during compile time which is much faster than other approaches like walking the stack. The example on MSDN shows the usage of this attribute with an example public static void TraceMessage(string message, [CallerMemberName] string memberName = "", [CallerFilePath] string sourceFilePath = "", [CallerLineNumber] int sourceLineNumber = 0) { Console.WriteLine("Hi {0} {1} {2}({3})", message, memberName, sourceFilePath, sourceLineNumber); }   When I do think of tracing I do usually want to have a API which allows me to Trace method enter and leave Trace messages with a severity like Info, Warning, Error When I do print a trace message it is very useful to print out method and type name as well. So your API must either be able to pass the method and type name as strings or extract it automatically via walking back one Stackframe and fetch the infos from there. The first glaring deficiency is that there is no CallerTypeAttribute yet because the C# compiler team was not satisfied with its performance.   A usable Trace Api might therefore look like   enum TraceTypes { None = 0, EnterLeave = 1 << 0, Info = 1 << 1, Warn = 1 << 2, Error = 1 << 3 } class Tracer : IDisposable { string Type; string Method; public Tracer(string type, string method) { Type = type; Method = method; if (IsEnabled(TraceTypes.EnterLeave,Type, Method)) { } } private bool IsEnabled(TraceTypes traceTypes, string Type, string Method) { // Do checking here if tracing is enabled return false; } public void Info(string fmt, params object[] args) { } public void Warn(string fmt, params object[] args) { } public void Error(string fmt, params object[] args) { } public static void Info(string type, string method, string fmt, params object[] args) { } public static void Warn(string type, string method, string fmt, params object[] args) { } public static void Error(string type, string method, string fmt, params object[] args) { } public void Dispose() { // trace method leave } } This minimal trace API is very fast but hard to maintain since you need to pass in the type and method name as hard coded strings which can change from time to time. But now we have at least CallerMemberName to rid of the explicit method parameter right? Not really. Since any acceptable usable trace Api should have a method signature like Tracexxx(… string fmt, params [] object args) we not able to add additional optional parameters after the args array. If we would put it before the format string we would need to make it optional as well which would mean the compiler would need to figure out what our trace message and arguments are (not likely) or we would need to specify everything explicitly just like before . There are ways around this by providing a myriad of overloads which in the end are routed to the very same method but that is ugly. I am not sure if nobody inside MS agrees that the above API is reasonable to have or (more likely) that the whole talk about you can use this feature for diagnostic purposes was not a core feature at all but a simple byproduct of making the life of INotifyPropertyChanged implementers easier. A way around this would be to allow for variable argument arrays after the params keyword another set of optional arguments which are always filled by the compiler but I do not know if this is an easy one. The thing I am missing much more is the not provided CallerType attribute. But not in the way you would think of. In the API above I did add some filtering based on method and type to stay as fast as possible for types where tracing is not enabled at all. It should be no more expensive than an additional method call and a bool variable check if tracing for this type is enabled at all. The data is tightly bound to the calling type and method and should therefore become part of the static type instance. Since extending the CLR type system for tracing is not something I do expect to happen I have come up with an alternative approach which allows me basically to attach run time data to any existing type object in super fast way. The key to success is the usage of generics.   class Tracer<T> : IDisposable { string Method; public Tracer(string method) { if (TraceData<T>.Instance.Enabled.HasFlag(TraceTypes.EnterLeave)) { } } public void Dispose() { if (TraceData<T>.Instance.Enabled.HasFlag(TraceTypes.EnterLeave)) { } } public static void Info(string fmt, params object[] args) { } /// <summary> /// Every type gets its own instance with a fresh set of variables to describe the /// current filter status. /// </summary> /// <typeparam name="T"></typeparam> internal class TraceData<UsingType> { internal static TraceData<UsingType> Instance = new TraceData<UsingType>(); public bool IsInitialized = false; // flag if we need to reinit the trace data in case of reconfigured trace settings at runtime public TraceTypes Enabled = TraceTypes.None; // Enabled trace levels for this type } } We do not need to pass the type as string or Type object to the trace Api. Instead we define a generic Api that accepts the using type as generic parameter. Then we can create a TraceData static instance which is due to the nature of generics a fresh instance for every new type parameter. My tests on my home machine have shown that this approach is as fast as a simple bool flag check. If you have an application with many types using tracing you do not want to bring the app down by simply enabling tracing for one special rarely used type. The trace filter performance for the types which are not enabled must be therefore the fasted code path. This approach has the nice side effect that if you store the TraceData instances in one global list you can reconfigure tracing at runtime safely by simply setting the IsInitialized flag to false. A similar effect can be achieved with a global static Dictionary<Type,TraceData> object but big hash tables have random memory access semantics which is bad for cache locality and you always need to pay for the lookup which involves hash code generation, equality check and an indexed array access. The generic version is wicked fast and allows you to add more features to your tracing Api with minimal perf overhead. But it is cumbersome to write the generic type argument always explicitly and worse if you do refactor code and move parts of it to other classes it might be that you cannot configure tracing correctly. I would like therefore to decorate my type with an attribute [CallerType] class Tracer<T> : IDisposable to tell the compiler to fill in the generic type argument automatically. class Program { static void Main(string[] args) { using (var t = new Tracer()) // equivalent to new Tracer<Program>() { That would be really useful and super fast since you do not need to pass any type object around but you do have full type infos at hand. This change would be breaking if another non generic type exists in the same namespace where now the generic counterpart would be preferred. But this is an acceptable risk in my opinion since you can today already get conflicts if two generic types of the same name are defined in different namespaces. This would be only a variation of this issue. When you do think about this further you can add more features like to trace the exception in your Dispose method if the method is left with an exception with that little trick I did write some time ago. You can think of tracing as a super fast and configurable switch to write data to an output destination or to execute alternative actions. With such an infrastructure you can e.g. Reconfigure tracing at run time. Take a memory dump when a specific method is left with a specific exception. Throw an exception when a specific trace statement is hit (useful for testing error conditions). Execute a passed delegate which e.g. dumps additional state when enabled. Write data to an in memory ring buffer and dump it when specific events do occur (e.g. method is left with an exception, triggered from outside). Write data to an output device. …. This stuff is really useful to have when your code is in production on a mission critical server and you need to find the root cause of sporadic crashes of your application. It could be a buggy graphics card driver which throws access violations into your application (ok with .NET 4 not anymore except if you enable a compatibility flag) where you would like to have a minidump or you have reached after two weeks of operation a state where you need a full memory dump at a specific point in time in the middle of an transaction. At my older machine I do get with this super fast approach 50 million traces/s when tracing is disabled. When I do know that tracing is enabled for this type I can walk the stack by using StackFrameHelper.GetStackFramesInternal to check further if a specific action or output device is configured for this method which is about 2-3 times faster than the regular StackTrace class. Even with one String.Format I am down to 3 million traces/s so performance is not so important anymore since I do want to do something now. The CallerMemberName feature of the C# 5 compiler is nice but I would have preferred to get direct access to the MethodHandle and not to the stringified version of it. But I really would like to see a CallerType attribute implemented to fill in the generic type argument of the call site to augment the static CLR type data with run time data.

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  • ICMP Redirect Theory VS. Application

    - by joeqwerty
    I'm trying to watch ICMP redirects in a lab using Cisco Packet Tracer (version 5.3.2) and I'm not seeing them, which leads me to believe that either my lab configuration isn't correct or my understanding of ICMP redirects isn't correct or that Packet Tracer doesn't support/use ICMP redirects. Here's what I believe to be true regarding ICMP redirects: Routers send ICMP redirects when all of these conditions are met: The interface on which the packet comes into the router is the same interface on which the packet gets routed out. The subnet or network of the source IP address is on the same subnet or network of the next-hop IP address of the routed packet. The datagram is not source-routed. The router kernel is configured to send redirects. I have the lab set up in Cisco Packet Tracer as displayed in the image and would expect to see an ICMP redirect from Router1 when pinging from PC1 to PC3. I'm not seeing the ICMP redirect and it looks like Router1 is actually routing all of the packets via Router2. I have IP ICMP debugging enabled on Router1 (and Router2) and I'm not seeing any ICMP redirect activity in either console. I'm also not seeing a route to the PC3 network in the routing table on PC1, which I think confirms that the ICMP redirect is not occurring. I'm using only static routing on Routers 1 and 2. Is my understanding of ICMP redirects incorrect, or is there a problem with my lab configuration or does Packet Tracer not support/use ICMP redirects?

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  • cisco asa + action drop issue

    - by ghp
    Have created a tunnel between 10.x.y.z network and 122.a.b.c ..the tunnel is up and active, but when I try the packet tracer output ..I get the ACTION as drop. I have also enabled same-security-traffic permit intra-interface. Can someone help me what does this drop mean? Result: input-interface: inside input-status: up input-line-status: up output-interface: outside output-status: up output-line-status: up Action: drop Drop-reason: (acl-drop) Flow is denied by configured rule Packet Tracer output @Shane Madden: please find below the packet tracer output. CASA5K-A# CASA5K-A# config t CASA5K-A(config)# packet-tracer input inside tcp 10.x.y.112 0 122.a.b.c 0 Phase: 1 Type: ROUTE-LOOKUP Subtype: input Result: ALLOW Config: Additional Information: in 0.0.0.0 0.0.0.0 outside Phase: 2 Type: ACCESS-LIST Subtype: Result: DROP Config: Implicit Rule Additional Information: Result: input-interface: inside input-status: up input-line-status: up output-interface: outside output-status: up output-line-status: up Action: drop Drop-reason: (acl-drop) Flow is denied by configured rule CASA5K-A(config)# ======================================================================== The access-group are as follows : access-group acl-inbound in interface outside access-group acl-outbound in interface inside and the access-list's are access-list acl-inbound extended permit tcp any any gt 1023 access-list acl-outbound extended permit ip object-group net-Source object net-dest

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  • How to set up VirtualBox Bridged Network on Windows 7 Host?

    - by Tong Wang
    I have virtualbox running on a Windows 2008 server, with a guest running ubuntu 10.04. The ubuntu guest is given a static IP of 192.168.1.4, which also has openssh installed. The guest has bridged network setup, I can ping 192.168.1.4 from any machine in the LAN, the ubuntu guest can also access the LAN. However, when I try to PuTTY into the ubuntu machine, I always get "connection refused". Below are some setup details: ubuntu IP: 192.168.1.4 hosts.allow sshd : 192.168.1.38 hosts.deny ALL : ALL when I the following command, I can see that sshd is listening on port 22: lsof -i tcp:22 Any idea? EDIT: It turned out to be a wrong VirtualBox Bridged Network setup. I give the Ubuntu guest a static IP of 192.168.1.4 (assigned to eth0). Then in the Windows 7 host, in the Network and Sharing Center, there is a new connection named "VirtualBox Host-Only Network" after the bridge is setup, that connection is again given the same static IP of 192.168.1.4. Once I change the "VirtualBox Host-Only Network" to automatically obtain an IP address, it's getting a different IP address of 169.254.249.70(Tentative). And now I can SSH into 192.168.1.4 with no problem, even without touching hosts.allow and hosts.deny. I've also noticed that in the properties windows (see screenshot below) of the "VirtualBox Host-Only Network", the second checkbox, "VirtualBox Bridged Networking Driver" is unchecked. While the same checkbox of the physical NIC (that is bridged to) is checked. So my further question is: is this how VBox bridged networking supposed to be setup? Any rationale behind this? I'd appreciate if someone could provide some explaination on VBox bridged networking setup on Windows host and I'll accept it as an answer.

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