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• calling concurrently Graphics.Draw and new Bitmap from memory in thread take long time

  - by Abdul jalil

  Example1 public partial class Form1 : Form { public Form1() { InitializeComponent(); pro = new Thread(new ThreadStart(Producer)); con = new Thread(new ThreadStart(Consumer)); } private AutoResetEvent m_DataAvailableEvent = new AutoResetEvent(false); Queue<Bitmap> queue = new Queue<Bitmap>(); Thread pro; Thread con ; public void Producer() { MemoryStream[] ms = new MemoryStream[3]; for (int y = 0; y < 3; y++) { StreamReader reader = new StreamReader("image"+(y+1)+".JPG"); BinaryReader breader = new BinaryReader(reader.BaseStream); byte[] buffer=new byte[reader.BaseStream.Length]; breader.Read(buffer,0,buffer.Length); ms[y] = new MemoryStream(buffer); } while (true) { for (int x = 0; x < 3; x++) { Bitmap bmp = new Bitmap(ms[x]); queue.Enqueue(bmp); m_DataAvailableEvent.Set(); Thread.Sleep(6); } } } public void Consumer() { Graphics g= pictureBox1.CreateGraphics(); while (true) { m_DataAvailableEvent.WaitOne(); Bitmap bmp = queue.Dequeue(); if (bmp != null) { // Bitmap bmp = new Bitmap(ms); g.DrawImage(bmp,new Point(0,0)); bmp.Dispose(); } } } private void pictureBox1_Click(object sender, EventArgs e) { con.Start(); pro.Start(); } } when Creating bitmap and Drawing to picture box are in seperate thread then Bitmap bmp = new Bitmap(ms[x]) take 45.591 millisecond and g.DrawImage(bmp,new Point(0,0)) take 41.430 milisecond when i make bitmap from memoryStream and draw it to picture box in one thread then Bitmap bmp = new Bitmap(ms[x]) take 29.619 and g.DrawImage(bmp,new Point(0,0)) take 35.540 the code is for Example 2 is why it take more time to draw and bitmap take time in seperate thread and how to reduce the time when processing in seperate thread. i am using ANTS performance profiler 4.3 public Form1() { InitializeComponent(); pro = new Thread(new ThreadStart(Producer)); con = new Thread(new ThreadStart(Consumer)); } private AutoResetEvent m_DataAvailableEvent = new AutoResetEvent(false); Queue<MemoryStream> queue = new Queue<MemoryStream>(); Thread pro; Thread con ; public void Producer() { MemoryStream[] ms = new MemoryStream[3]; for (int y = 0; y < 3; y++) { StreamReader reader = new StreamReader("image"+(y+1)+".JPG"); BinaryReader breader = new BinaryReader(reader.BaseStream); byte[] buffer=new byte[reader.BaseStream.Length]; breader.Read(buffer,0,buffer.Length); ms[y] = new MemoryStream(buffer); } while (true) { for (int x = 0; x < 3; x++) { // Bitmap bmp = new Bitmap(ms[x]); queue.Enqueue(ms[x]); m_DataAvailableEvent.Set(); Thread.Sleep(6); } } } public void Consumer() { Graphics g= pictureBox1.CreateGraphics(); while (true) { m_DataAvailableEvent.WaitOne(); //Bitmap bmp = queue.Dequeue(); MemoryStream ms= queue.Dequeue(); if (ms != null) { Bitmap bmp = new Bitmap(ms); g.DrawImage(bmp,new Point(0,0)); bmp.Dispose(); } } } private void pictureBox1_Click(object sender, EventArgs e) { con.Start(); pro.Start(); }

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• Help with Silverlight Sockets and Message delivery

  - by pixel3cs

  There are 4 months since I stopped developing my Silverlight Multiplayer Chess game. The problem was a bug wich I couldn't reproduce. Sice I got some free time this week I managed to discover the problem and I am now able to reproduce the bug. It seems that if I send 10 messages from client, one after another, with no delay between them, just like in the below example // when I press Enter, the client will 10 messages with no delay between them private void textBox_KeyDown(object sender, KeyEventArgs e) { if (e.Key == Key.Enter && textBox.Text.Length > 0) { for (int i = 0; i < 10; i++) { MessageBuilder mb = new MessageBuilder(); mb.Writer.Write((byte)GameCommands.NewChatMessageInTable); mb.Writer.Write(string.Format("{0}{2}: {1}", ClientVars.PlayerNickname, textBox.Text, i)); SendChatMessageEvent(mb.GetMessage()); //System.Threading.Thread.Sleep(100); } textBox.Text = string.Empty; } } // the method used by client to send a message to server public void SendData(Message message) { if (socket.Connected) { SocketAsyncEventArgs myMsg = new SocketAsyncEventArgs(); myMsg.RemoteEndPoint = socket.RemoteEndPoint; byte[] buffer = message.Buffer; myMsg.SetBuffer(buffer, 0, buffer.Length); socket.SendAsync(myMsg); } else { string err = "Server does not respond. You are disconnected."; socket.Close(); uiContext.Post(this.uiClient.ProcessOnErrorData, err); } } // the method used by server to receive data from client private void OnDataReceived(IAsyncResult async) { ClientSocketPacket client = async.AsyncState as ClientSocketPacket; int count = 0; try { if (client.Socket.Connected) count = client.Socket.EndReceive(async); // THE PROBLEM IS HERE // IF SERVER WAS RECEIVE ALL MESSAGES SEPARATELY, ONE BY ONE, THE COUNT // WAS ALWAYS 15, BUT BECAUSE THE SERVER RECEIVE 3 MESSAGES IN 1, THE COUNT // IS SOMETIME 45 } catch { HandleException(client); } client.MessageStream.Write(client.Buffer, 0, count); Message message; while (client.MessageStream.Read(out message)) { message.Tag = client; ThreadPool.QueueUserWorkItem(new WaitCallback(this.processingThreadEvent.ServerGotData), message); totalReceivedBytes += message.Buffer.Length; } try { if (client.Socket.Connected) client.Socket.BeginReceive(client.Buffer, 0, client.Buffer.Length, 0, new AsyncCallback(OnDataReceived), client); } catch { HandleException(client); } } there are sent only 3 big messages, and every big message contain 3 or 4 small messages. This is not the behavior I want. If I put a 100 milliseconds delay between message delivery, everything is work fine, but in a real world scenario users can send messages to server even at 1 millisecond between them. Are there any settings to be done in order to make the client send only one message at a time, or Even if I receive 3 messages in 1, are they full messages all the time (I dont't want to receive 2.5 messages in one big message) ? because if they are, I can read them and treat this new situation

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• TCP client in C and server in Java

  - by faldren

  I would like to communicate with 2 applications : a client in C which send a message to the server in TCP and the server in Java which receive it and send an acknowledgement. Here is the client (the code is a thread) : static void *tcp_client(void *p_data) { if (p_data != NULL) { char const *message = p_data; int sockfd, n; struct sockaddr_in serv_addr; struct hostent *server; char buffer[256]; sockfd = socket(AF_INET, SOCK_STREAM, 0); if (sockfd < 0) { error("ERROR opening socket"); } server = gethostbyname(ALARM_PC_IP); if (server == NULL) { fprintf(stderr,"ERROR, no such host\n"); exit(0); } bzero((char *) &serv_addr, sizeof(serv_addr)); serv_addr.sin_family = AF_INET; bcopy((char *)server->h_addr, (char *)&serv_addr.sin_addr.s_addr, server->h_length); serv_addr.sin_port = htons(TCP_PORT); if (connect(sockfd,(struct sockaddr *) &serv_addr,sizeof(serv_addr)) < 0) { error("ERROR connecting"); } n = write(sockfd,message,strlen(message)); if (n < 0) { error("ERROR writing to socket"); } bzero(buffer,256); n = read(sockfd,buffer,255); if (n < 0) { error("ERROR reading from socket"); } printf("Message from the server : %s\n",buffer); close(sockfd); } return 0; } And the java server : try { int port = 9015; ServerSocket server=new ServerSocket(port); System.out.println("Server binded at "+((server.getInetAddress()).getLocalHost()).getHostAddress()+":"+port); System.out.println("Run the Client"); while (true) { Socket socket=server.accept(); BufferedReader in= new BufferedReader(new InputStreamReader(socket.getInputStream())); System.out.println(in.readLine()); PrintStream out=new PrintStream(socket.getOutputStream()); out.print("Welcome by server\n"); out.flush(); out.close(); in.close(); System.out.println("finished"); } } catch(Exception err) { System.err.println("* err"+err); } With n = read(sockfd,buffer,255); the client is waiting a response and for the server, the message is never ended so it doesn't send a response with PrintStream. If I remove these lines : bzero(buffer,256); n = read(sockfd,buffer,255); if (n < 0) { error("ERROR reading from socket"); } printf("Message from the server : %s\n",buffer); The server knows that the message is finished but the client can't receive the response. How solve that ? Thank you

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• SQL Server and Hyper-V Dynamic Memory - Part 1

  - by SQLOS Team

  SQL and Dynamic Memory Blog Post Series   Hyper-V Dynamic Memory is a new feature in Windows Server 2008 R2 SP1 that allows the memory assigned to guest virtual machines to vary according to demand. Using this feature with SQL Server is supported, but how well does it work in an environment where available memory can vary dynamically, especially since SQL Server likes memory, and is not very eager to let go of it? The next three posts will look at this question in detail. In Part 1 Serdar Sutay, a program manager in the Windows Hyper-V team, introduces Dynamic Memory with an overview of the basic architecture, configuration and monitoring concepts. In subsequent parts we will look at SQL Server memory handling, and develop some guidelines on using SQL Server with Dynamic Memory.   Part 1: Dynamic Memory Introduction   In virtualized environments memory is often the bottleneck for reaching higher VM densities. In Windows Server 2008 R2 SP1 Hyper-V introduced a new feature “Dynamic Memory” to improve VM densities on Hyper-V hosts. Dynamic Memory increases the memory utilization in virtualized environments by enabling VM memory to be changed dynamically when the VM is running.   This brings up the question of how to utilize this feature with SQL Server VMs as SQL Server performance is very sensitive to the memory being used. In the next three posts we’ll discuss the internals of Dynamic Memory, SQL Server Memory Management and how to use Dynamic Memory with SQL Server VMs.   Memory Utilization Efficiency in Virtualized Environments   The primary reason memory is usually the bottleneck for higher VM densities is that users tend to be generous when assigning memory to their VMs. Here are some memory sizing practices we’ve heard from customers:   ·         I assign 4 GB of memory to my VMs. I don’t know if all of it is being used by the applications but no one complains. ·         I take the minimum system requirements and add 50% more. ·         I go with the recommendations provided by my software vendor.   In reality correctly sizing a virtual machine requires significant effort to monitor the memory usage of the applications. Since this is not done in most environments, VMs are usually over-provisioned in terms of memory. In other words, a SQL Server VM that is assigned 4 GB of memory may not need to use 4 GB.   How does Dynamic Memory help?   Dynamic Memory improves the memory utilization by removing the requirement to determine the memory need for an application. Hyper-V determines the memory needed by applications in the VM by evaluating the memory usage information in the guest with Dynamic Memory. VMs can start with a small amount of memory and they can be assigned more memory dynamically based on the workload of applications running inside.   Overview of Dynamic Memory Concepts   ·         Startup Memory: Startup Memory is the starting amount of memory when Dynamic Memory is enabled for a VM. Dynamic Memory will make sure that this amount of memory is always assigned to the VMs by default.   ·         Maximum Memory: Maximum Memory specifies the maximum amount of memory that a VM can grow to with Dynamic Memory. ·         Memory Demand: Memory Demand is the amount determined by Dynamic Memory as the memory needed by the applications in the VM. In Windows Server 2008 R2 SP1, this is equal to the total amount of committed memory of the VM. ·         Memory Buffer: Memory Buffer is the amount of memory assigned to the VMs in addition to their memory demand to satisfy immediate memory requirements and file cache needs.   Once Dynamic Memory is enabled for a VM, it will start with the “Startup Memory”. After the boot process Dynamic Memory will determine the “Memory Demand” of the VM. Based on this memory demand it will determine the amount of “Memory Buffer” that needs to be assigned to the VM. Dynamic Memory will assign the total of “Memory Demand” and “Memory Buffer” to the VM as long as this value is less than “Maximum Memory” and as long as physical memory is available on the host.   What happens when there is not enough physical memory available on the host?   Once there is not enough physical memory on the host to satisfy VM needs, Dynamic Memory will assign less than needed amount of memory to the VMs based on their importance. A concept known as “Memory Weight” is used to determine how much VMs should be penalized based on their needed amount of memory. “Memory Weight” is a configuration setting on the VM. It can be configured to be higher for the VMs with high performance requirements. Under high memory pressure on the host, the “Memory Weight” of the VMs are evaluated in a relative manner and the VMs with lower relative “Memory Weight” will be penalized more than the ones with higher “Memory Weight”.   Dynamic Memory Configuration   Based on these concepts “Startup Memory”, “Maximum Memory”, “Memory Buffer” and “Memory Weight” can be configured as shown below in Windows Server 2008 R2 SP1 Hyper-V Manager. Memory Demand is automatically calculated by Dynamic Memory once VMs start running.     Dynamic Memory Monitoring    In Windows Server 2008 R2 SP1, Hyper-V Manager displays the memory status of VMs in the following three columns:         ·         Assigned Memory represents the current physical memory assigned to the VM. In regular conditions this will be equal to the sum of “Memory Demand” and “Memory Buffer” assigned to the VM. When there is not enough memory on the host, this value can go below the Memory Demand determined for the VM. ·         Memory Demand displays the current “Memory Demand” determined for the VM. ·         Memory Status displays the current memory status of the VM. This column can represent three values for a VM: o   OK: In this condition the VM is assigned the total of Memory Demand and Memory Buffer it needs. o   Low: In this condition the VM is assigned all the Memory Demand and a certain percentage of the Memory Buffer it needs. o   Warning: In this condition the VM is assigned a lower memory than its Memory Demand. When VMs are running in this condition, it’s likely that they will exhibit performance problems due to internal paging happening in the VM.    So far so good! But how does it work with SQL Server?   SQL Server is aggressive in terms of memory usage for good reasons. This raises the question: How do SQL Server and Dynamic Memory work together? To understand the full story, we’ll first need to understand how SQL Server Memory Management works. This will be covered in our second post in “SQL and Dynamic Memory” series. Meanwhile if you want to dive deeper into Dynamic Memory you can check the below posts from the Windows Virtualization Team Blog:   http://blogs.technet.com/virtualization/archive/2010/03/18/dynamic-memory-coming-to-hyper-v.aspx   http://blogs.technet.com/virtualization/archive/2010/03/25/dynamic-memory-coming-to-hyper-v-part-2.aspx   http://blogs.technet.com/virtualization/archive/2010/04/07/dynamic-memory-coming-to-hyper-v-part-3.aspx   http://blogs.technet.com/b/virtualization/archive/2010/04/21/dynamic-memory-coming-to-hyper-v-part-4.aspx   http://blogs.technet.com/b/virtualization/archive/2010/05/20/dynamic-memory-coming-to-hyper-v-part-5.aspx   http://blogs.technet.com/b/virtualization/archive/2010/07/12/dynamic-memory-coming-to-hyper-v-part-6.aspx   - Serdar Sutay   Originally posted at http://blogs.msdn.com/b/sqlosteam/

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• SQL Server and Hyper-V Dynamic Memory - Part 1

  - by SQLOS Team

  SQL and Dynamic Memory Blog Post Series   Hyper-V Dynamic Memory is a new feature in Windows Server 2008 R2 SP1 that allows the memory assigned to guest virtual machines to vary according to demand. Using this feature with SQL Server is supported, but how well does it work in an environment where available memory can vary dynamically, especially since SQL Server likes memory, and is not very eager to let go of it? The next three posts will look at this question in detail. In Part 1 Serdar Sutay, a program manager in the Windows Hyper-V team, introduces Dynamic Memory with an overview of the basic architecture, configuration and monitoring concepts. In subsequent parts we will look at SQL Server memory handling, and develop some guidelines on using SQL Server with Dynamic Memory.   Part 1: Dynamic Memory Introduction   In virtualized environments memory is often the bottleneck for reaching higher VM densities. In Windows Server 2008 R2 SP1 Hyper-V introduced a new feature “Dynamic Memory” to improve VM densities on Hyper-V hosts. Dynamic Memory increases the memory utilization in virtualized environments by enabling VM memory to be changed dynamically when the VM is running.   This brings up the question of how to utilize this feature with SQL Server VMs as SQL Server performance is very sensitive to the memory being used. In the next three posts we’ll discuss the internals of Dynamic Memory, SQL Server Memory Management and how to use Dynamic Memory with SQL Server VMs.   Memory Utilization Efficiency in Virtualized Environments   The primary reason memory is usually the bottleneck for higher VM densities is that users tend to be generous when assigning memory to their VMs. Here are some memory sizing practices we’ve heard from customers:   ·         I assign 4 GB of memory to my VMs. I don’t know if all of it is being used by the applications but no one complains. ·         I take the minimum system requirements and add 50% more. ·         I go with the recommendations provided by my software vendor.   In reality correctly sizing a virtual machine requires significant effort to monitor the memory usage of the applications. Since this is not done in most environments, VMs are usually over-provisioned in terms of memory. In other words, a SQL Server VM that is assigned 4 GB of memory may not need to use 4 GB.   How does Dynamic Memory help?   Dynamic Memory improves the memory utilization by removing the requirement to determine the memory need for an application. Hyper-V determines the memory needed by applications in the VM by evaluating the memory usage information in the guest with Dynamic Memory. VMs can start with a small amount of memory and they can be assigned more memory dynamically based on the workload of applications running inside.   Overview of Dynamic Memory Concepts   ·         Startup Memory: Startup Memory is the starting amount of memory when Dynamic Memory is enabled for a VM. Dynamic Memory will make sure that this amount of memory is always assigned to the VMs by default.   ·         Maximum Memory: Maximum Memory specifies the maximum amount of memory that a VM can grow to with Dynamic Memory. ·         Memory Demand: Memory Demand is the amount determined by Dynamic Memory as the memory needed by the applications in the VM. In Windows Server 2008 R2 SP1, this is equal to the total amount of committed memory of the VM. ·         Memory Buffer: Memory Buffer is the amount of memory assigned to the VMs in addition to their memory demand to satisfy immediate memory requirements and file cache needs.   Once Dynamic Memory is enabled for a VM, it will start with the “Startup Memory”. After the boot process Dynamic Memory will determine the “Memory Demand” of the VM. Based on this memory demand it will determine the amount of “Memory Buffer” that needs to be assigned to the VM. Dynamic Memory will assign the total of “Memory Demand” and “Memory Buffer” to the VM as long as this value is less than “Maximum Memory” and as long as physical memory is available on the host.   What happens when there is not enough physical memory available on the host?   Once there is not enough physical memory on the host to satisfy VM needs, Dynamic Memory will assign less than needed amount of memory to the VMs based on their importance. A concept known as “Memory Weight” is used to determine how much VMs should be penalized based on their needed amount of memory. “Memory Weight” is a configuration setting on the VM. It can be configured to be higher for the VMs with high performance requirements. Under high memory pressure on the host, the “Memory Weight” of the VMs are evaluated in a relative manner and the VMs with lower relative “Memory Weight” will be penalized more than the ones with higher “Memory Weight”.   Dynamic Memory Configuration   Based on these concepts “Startup Memory”, “Maximum Memory”, “Memory Buffer” and “Memory Weight” can be configured as shown below in Windows Server 2008 R2 SP1 Hyper-V Manager. Memory Demand is automatically calculated by Dynamic Memory once VMs start running.     Dynamic Memory Monitoring    In Windows Server 2008 R2 SP1, Hyper-V Manager displays the memory status of VMs in the following three columns:         ·         Assigned Memory represents the current physical memory assigned to the VM. In regular conditions this will be equal to the sum of “Memory Demand” and “Memory Buffer” assigned to the VM. When there is not enough memory on the host, this value can go below the Memory Demand determined for the VM. ·         Memory Demand displays the current “Memory Demand” determined for the VM. ·         Memory Status displays the current memory status of the VM. This column can represent three values for a VM: o   OK: In this condition the VM is assigned the total of Memory Demand and Memory Buffer it needs. o   Low: In this condition the VM is assigned all the Memory Demand and a certain percentage of the Memory Buffer it needs. o   Warning: In this condition the VM is assigned a lower memory than its Memory Demand. When VMs are running in this condition, it’s likely that they will exhibit performance problems due to internal paging happening in the VM.    So far so good! But how does it work with SQL Server?   SQL Server is aggressive in terms of memory usage for good reasons. This raises the question: How do SQL Server and Dynamic Memory work together? To understand the full story, we’ll first need to understand how SQL Server Memory Management works. This will be covered in our second post in “SQL and Dynamic Memory” series. Meanwhile if you want to dive deeper into Dynamic Memory you can check the below posts from the Windows Virtualization Team Blog:   http://blogs.technet.com/virtualization/archive/2010/03/18/dynamic-memory-coming-to-hyper-v.aspx   http://blogs.technet.com/virtualization/archive/2010/03/25/dynamic-memory-coming-to-hyper-v-part-2.aspx   http://blogs.technet.com/virtualization/archive/2010/04/07/dynamic-memory-coming-to-hyper-v-part-3.aspx   http://blogs.technet.com/b/virtualization/archive/2010/04/21/dynamic-memory-coming-to-hyper-v-part-4.aspx   http://blogs.technet.com/b/virtualization/archive/2010/05/20/dynamic-memory-coming-to-hyper-v-part-5.aspx   http://blogs.technet.com/b/virtualization/archive/2010/07/12/dynamic-memory-coming-to-hyper-v-part-6.aspx   - Serdar Sutay   Originally posted at http://blogs.msdn.com/b/sqlosteam/

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• SQL SERVER – Guest Post – Jonathan Kehayias – Wait Type – Day 16 of 28

  - by pinaldave

  Jonathan Kehayias (Blog | Twitter) is a MCITP Database Administrator and Developer, who got started in SQL Server in 2004 as a database developer and report writer in the natural gas industry. After spending two and a half years working in TSQL, in late 2006, he transitioned to the role of SQL Database Administrator. His primary passion is performance tuning, where he frequently rewrites queries for better performance and performs in depth analysis of index implementation and usage. Jonathan blogs regularly on SQLBlog, and was a coauthor of Professional SQL Server 2008 Internals and Troubleshooting. On a personal note, I think Jonathan is extremely positive person. In every conversation with him I have found that he is always eager to help and encourage. Every time he finds something needs to be approved, he has contacted me without hesitation and guided me to improve, change and learn. During all the time, he has not lost his focus to help larger community. I am honored that he has accepted to provide his views on complex subject of Wait Types and Queues. Currently I am reading his series on Extended Events. Here is the guest blog post by Jonathan: SQL Server troubleshooting is all about correlating related pieces of information together to indentify where exactly the root cause of a problem lies. In my daily work as a DBA, I generally get phone calls like, “So and so application is slow, what’s wrong with the SQL Server.” One of the funny things about the letters DBA is that they go so well with Default Blame Acceptor, and I really wish that I knew exactly who the first person was that pointed that out to me, because it really fits at times. A lot of times when I get this call, the problem isn’t related to SQL Server at all, but every now and then in my initial quick checks, something pops up that makes me start looking at things further. The SQL Server is slow, we see a number of tasks waiting on ASYNC_IO_COMPLETION, IO_COMPLETION, or PAGEIOLATCH_* waits in sys.dm_exec_requests and sys.dm_exec_waiting_tasks. These are also some of the highest wait types in sys.dm_os_wait_stats for the server, so it would appear that we have a disk I/O bottleneck on the machine. A quick check of sys.dm_io_virtual_file_stats() and tempdb shows a high write stall rate, while our user databases show high read stall rates on the data files. A quick check of some performance counters and Page Life Expectancy on the server is bouncing up and down in the 50-150 range, the Free Page counter consistently hits zero, and the Free List Stalls/sec counter keeps jumping over 10, but Buffer Cache Hit Ratio is 98-99%. Where exactly is the problem? In this case, which happens to be based on a real scenario I faced a few years back, the problem may not be a disk bottleneck at all; it may very well be a memory pressure issue on the server. A quick check of the system spec’s and it is a dual duo core server with 8GB RAM running SQL Server 2005 SP1 x64 on Windows Server 2003 R2 x64. Max Server memory is configured at 6GB and we think that this should be enough to handle the workload; or is it? This is a unique scenario because there are a couple of things happening inside of this system, and they all relate to what the root cause of the performance problem is on the system. If we were to query sys.dm_exec_query_stats for the TOP 10 queries, by max_physical_reads, max_logical_reads, and max_worker_time, we may be able to find some queries that were using excessive I/O and possibly CPU against the system in their worst single execution. We can also CROSS APPLY to sys.dm_exec_sql_text() and see the statement text, and also CROSS APPLY sys.dm_exec_query_plan() to get the execution plan stored in cache. Ok, quick check, the plans are pretty big, I see some large index seeks, that estimate 2.8GB of data movement between operators, but everything looks like it is optimized the best it can be. Nothing really stands out in the code, and the indexing looks correct, and I should have enough memory to handle this in cache, so it must be a disk I/O problem right? Not exactly! If we were to look at how much memory the plan cache is taking by querying sys.dm_os_memory_clerks for the CACHESTORE_SQLCP and CACHESTORE_OBJCP clerks we might be surprised at what we find. In SQL Server 2005 RTM and SP1, the plan cache was allowed to take up to 75% of the memory under 8GB. I’ll give you a second to go back and read that again. Yes, you read it correctly, it says 75% of the memory under 8GB, but you don’t have to take my word for it, you can validate this by reading Changes in Caching Behavior between SQL Server 2000, SQL Server 2005 RTM and SQL Server 2005 SP2. In this scenario the application uses an entirely adhoc workload against SQL Server and this leads to plan cache bloat, and up to 4.5GB of our 6GB of memory for SQL can be consumed by the plan cache in SQL Server 2005 SP1. This in turn reduces the size of the buffer cache to just 1.5GB, causing our 2.8GB of data movement in this expensive plan to cause complete flushing of the buffer cache, not just once initially, but then another time during the queries execution, resulting in excessive physical I/O from disk. Keep in mind that this is not the only query executing at the time this occurs. Remember the output of sys.dm_io_virtual_file_stats() showed high read stalls on the data files for our user databases versus higher write stalls for tempdb? The memory pressure is also forcing heavier use of tempdb to handle sorting and hashing in the environment as well. The real clue here is the Memory counters for the instance; Page Life Expectancy, Free List Pages, and Free List Stalls/sec. The fact that Page Life Expectancy is fluctuating between 50 and 150 constantly is a sign that the buffer cache is experiencing constant churn of data, once every minute to two and a half minutes. If you add to the Page Life Expectancy counter, the consistent bottoming out of Free List Pages along with Free List Stalls/sec consistently spiking over 10, and you have the perfect memory pressure scenario. All of sudden it may not be that our disk subsystem is the problem, but is instead an innocent bystander and victim. Side Note: The Page Life Expectancy counter dropping briefly and then returning to normal operating values intermittently is not necessarily a sign that the server is under memory pressure. The Books Online and a number of other references will tell you that this counter should remain on average above 300 which is the time in seconds a page will remain in cache before being flushed or aged out. This number, which equates to just five minutes, is incredibly low for modern systems and most published documents pre-date the predominance of 64 bit computing and easy availability to larger amounts of memory in SQL Servers. As food for thought, consider that my personal laptop has more memory in it than most SQL Servers did at the time those numbers were posted. I would argue that today, a system churning the buffer cache every five minutes is in need of some serious tuning or a hardware upgrade. Back to our problem and its investigation: There are two things really wrong with this server; first the plan cache is excessively consuming memory and bloated in size and we need to look at that and second we need to evaluate upgrading the memory to accommodate the workload being performed. In the case of the server I was working on there were a lot of single use plans found in sys.dm_exec_cached_plans (where usecounts=1). Single use plans waste space in the plan cache, especially when they are adhoc plans for statements that had concatenated filter criteria that is not likely to reoccur with any frequency.  SQL Server 2005 doesn’t natively have a way to evict a single plan from cache like SQL Server 2008 does, but MVP Kalen Delaney, showed a hack to evict a single plan by creating a plan guide for the statement and then dropping that plan guide in her blog post Geek City: Clearing a Single Plan from Cache. We could put that hack in place in a job to automate cleaning out all the single use plans periodically, minimizing the size of the plan cache, but a better solution would be to fix the application so that it uses proper parameterized calls to the database. You didn’t write the app, and you can’t change its design? Ok, well you could try to force parameterization to occur by creating and keeping plan guides in place, or we can try forcing parameterization at the database level by using ALTER DATABASE <dbname> SET PARAMETERIZATION FORCED and that might help. If neither of these help, we could periodically dump the plan cache for that database, as discussed as being a problem in Kalen’s blog post referenced above; not an ideal scenario. The other option is to increase the memory on the server to 16GB or 32GB, if the hardware allows it, which will increase the size of the plan cache as well as the buffer cache. In SQL Server 2005 SP1, on a system with 16GB of memory, if we set max server memory to 14GB the plan cache could use at most 9GB  [(8GB*.75)+(6GB*.5)=(6+3)=9GB], leaving 5GB for the buffer cache.  If we went to 32GB of memory and set max server memory to 28GB, the plan cache could use at most 16GB [(8*.75)+(20*.5)=(6+10)=16GB], leaving 12GB for the buffer cache. Thankfully we have SQL Server 2005 Service Pack 2, 3, and 4 these days which include the changes in plan cache sizing discussed in the Changes to Caching Behavior between SQL Server 2000, SQL Server 2005 RTM and SQL Server 2005 SP2 blog post. In real life, when I was troubleshooting this problem, I spent a week trying to chase down the cause of the disk I/O bottleneck with our Server Admin and SAN Admin, and there wasn’t much that could be done immediately there, so I finally asked if we could increase the memory on the server to 16GB, which did fix the problem. It wasn’t until I had this same problem occur on another system that I actually figured out how to really troubleshoot this down to the root cause.  I couldn’t believe the size of the plan cache on the server with 16GB of memory when I actually learned about this and went back to look at it. SQL Server is constantly telling a story to anyone that will listen. As the DBA, you have to sit back and listen to all that it’s telling you and then evaluate the big picture and how all the data you can gather from SQL about performance relate to each other. One of the greatest tools out there is actually a free in the form of Diagnostic Scripts for SQL Server 2005 and 2008, created by MVP Glenn Alan Berry. Glenn’s scripts collect a majority of the information that SQL has to offer for rapid troubleshooting of problems, and he includes a lot of notes about what the outputs of each individual query might be telling you. When I read Pinal’s blog post SQL SERVER – ASYNC_IO_COMPLETION – Wait Type – Day 11 of 28, I noticed that he referenced Checking Memory Related Performance Counters in his post, but there was no real explanation about why checking memory counters is so important when looking at an I/O related wait type. I thought I’d chat with him briefly on Google Talk/Twitter DM and point this out, and offer a couple of other points I noted, so that he could add the information to his blog post if he found it useful.  Instead he asked that I write a guest blog for this. I am honored to be a guest blogger, and to be able to share this kind of information with the community. The information contained in this blog post is a glimpse at how I do troubleshooting almost every day of the week in my own environment. SQL Server provides us with a lot of information about how it is running, and where it may be having problems, it is up to us to play detective and find out how all that information comes together to tell us what’s really the problem. This blog post is written by Jonathan Kehayias (Blog | Twitter). Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: MVP, Pinal Dave, PostADay, Readers Contribution, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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• 550 “Overwrite permission denied” when editing a file via FTP

  - by nodebunny

  DreamHost recently moved my accounts to a new shared box, and now I can't edit files via UltraEdit's built in FTP client, which messes up my work flow! What did they do that this is not working now? It stopped working after they moved me. Here's the output from the FTP console in UltraEdit 10/26/2011 10:42:36 AM: 220 DreamHost FTP Server 10/26/2011 10:42:36 AM: USER nodebunny 10/26/2011 10:42:36 AM: 331 Password required for ninjawww 10/26/2011 10:42:36 AM: PASS xxxxxxxx 10/26/2011 10:42:36 AM: 230 User nodebunny logged in 10/26/2011 10:42:36 AM: FEAT 10/26/2011 10:42:36 AM: 211-Features: LANG ja-JP.UTF-8;ja-JP;zh-TW;fr-FR;zh-CN;en-US*;bg-BG;ko-KR.UTF-8;ko-KR MDTM MFMT TVFS UTF8 MFF modify;UNIX.group;UNIX.mode; MLST modify*;perm*;size*;type*;unique*;UNIX.group*;UNIX.mode*;UNIX.owner*; REST STREAM SIZE 211 End 10/26/2011 10:42:36 AM: OPTS UTF8 ON 10/26/2011 10:42:36 AM: 200 UTF8 set to on 10/26/2011 10:42:36 AM: PWD 10/26/2011 10:42:36 AM: 257 "/" is the current directory 10/26/2011 10:42:36 AM: PWD 10/26/2011 10:42:36 AM: 257 "/" is the current directory 10/26/2011 10:42:36 AM: CWD /dev/proj/nodebunny 10/26/2011 10:42:36 AM: 250 CWD command successful 10/26/2011 10:42:36 AM: PWD 10/26/2011 10:42:36 AM: 257 "/dev/proj/nodebunny/lib/Buffer" is the current directory 10/26/2011 10:42:36 AM: PWD 10/26/2011 10:42:37 AM: 257 "/dev/proj/nodebunny/lib/Buffer" is the current directory 10/26/2011 10:42:37 AM: TYPE I 10/26/2011 10:42:37 AM: 200 Type set to I 10/26/2011 10:42:37 AM: PORT 10,15,55,125,226,16 10/26/2011 10:42:37 AM: 200 PORT command successful 10/26/2011 10:42:37 AM: STOR Buffer.pm 10/26/2011 10:42:37 AM: 550 Buffer.pm: Overwrite permission denied

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• Rendering with Direct3D

  - by Jamie

  Hi, I'm slightly confused about how Direct3D rendering works. Basically, as long as I render to one surface, everything is fine. But when I try rendering to multiple surfaces, it seems like everything is still rendered to one surface. I think there's something wrong with my calls. For each update cycle this is what I do 1. device-BeginScene() 2. sprite-Begin(...) ... A bunch of GetRenderTarget to store the old render target, then SetRenderTarget to set a new surface, and then things like CreateVertexBuffer, SetTexture, etc to draw on the new render target. Then resetting to the old render target. sprite-Draw([the back buffer]) (the back buffer is actually another surface, not the actual back buffer. But here it is being drawn onto the actual back buffer, I think) sprite-End() device-EndScene() device-Present(...) Also, it seems like if I mix sprite drawing and non-sprite drawing onto a surface, that first one set of render commands is executed and then the other set, rather than in order by when each command was called. If anyone could shed light on any of this, it would be much appreciated.

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• JavaCV IplImage to LWJGL Texture

  - by rendrag

  As a side project I've been attempting to make a dynamic display (for example a screen within a game) that shows images from my webcam. I've been messing around with JavaCV and LWJGL for the past few months and have a basic understanding of how they both work. I found this after scouring google, but I get an error that the ByteBuffer isn't big enough. IplImage img = cam.getFrame(); ByteBuffer buffer = img.asByteBuffer(); int textureID = glGenTextures(); //Generate texture ID glBindTexture(GL_TEXTURE_2D, textureID); //Bind texture ID //I don't know how much of the following is necessary //Setup wrap mode glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL12.GL_CLAMP_TO_EDGE); glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL12.GL_CLAMP_TO_EDGE); //Setup texture scaling filtering glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_NEAREST); glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_NEAREST); //Send texture data to OpenGL - this is the line that actually does stuff and that OpenGL has a problem with glTexImage2D(GL_TEXTURE_2D, 0, GL_RGB, width, height, 0, GL12.GL_BGR, GL_UNSIGNED_BYTE, buffer); That last line throws this- Exception in thread "Thread-0" java.lang.IllegalArgumentException: Number of remaining buffer elements is 144, must be at least 921600. Because at most 921600 elements can be returned, a buffer with at least 921600 elements is required, regardless of actual returned element count at org.lwjgl.BufferChecks.throwBufferSizeException(BufferChecks.java:162) at org.lwjgl.BufferChecks.checkBufferSize(BufferChecks.java:189) at org.lwjgl.BufferChecks.checkBuffer(BufferChecks.java:230) at org.lwjgl.opengl.GL11.glTexImage2D(GL11.java:2845) at tests.TextureTest.getTexture(TextureTest.java:78) at tests.TextureTest.update(TextureTest.java:43) at lib.game.AbstractGame$1.run(AbstractGame.java:52) at java.lang.Thread.run(Thread.java:679)

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• Perl - can't flush STDOUT or STDERR

  - by Jim Salter

  Perl 5.14 from stock Ubuntu Precise repos. Trying to write a simple wrapper to monitor progress on copying from one stream to another: use IO::Handle; while ($bufsize = read (SOURCE, $buffer, 1048576)) { STDERR->printflush ("Transferred $xferred of $sendsize bytes\n"); $xferred += $bufsize; print TARGET $buffer; } This does not perform as expected (writing a line each time the 1M buffer is read). I end up seeing the first line (with a blank value of $xferred), and then the 7th and 8th lines (on an 8MB transfer). Been pounding my brains out on this for hours - I've read the perldocs, I've read the classic "Suffering from Buffering" article, I've tried everything from select and $|++ to IO::Handle to binmode (STDERR, "::unix") to you name it. I've also tried flushing TARGET with each line using IO::Handle (TARGET-flush). No dice. Has anybody else ever encountered this? I don't have any ideas left. Sleeping one second "fixes" the problem, but obviously I don't want to sleep a second every time I read a buffer just so my progress will output on the screen! FWIW, the problem is exactly the same whether I'm outputting to STDERR or STDOUT.

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• How to create per-vertex normals when reusing vertex data?

  - by Chris Smith

  I am displaying a cube using a vertex buffer object (gl.ELEMENT_ARRAY_BUFFER). This allows me to specify vertex indicies, rather than having duplicate vertexes. In the case of displaying a simple cube, this means I only need to have eight vertices total. Opposed to needing three vertices per triangle, times two triangles per face, times six faces. Sound correct so far? My question is, how do I now deal with vertex attribute data such as color, texture coordinates, and normals when reusing vertices using the vertex buffer object? If I am reusing the same vertex data in my indexed vertex buffer, how can I differentiate when vertex X is used as part of the cube's front face versus the cube's left face? In both cases I would like the surface normal and texture coordinates to be different. I understand I could average the surface normal, however I would like to render a cube. Also, this still doesn't work for texture coordinates. Is there a way to save memory using a vertex buffer object while being able to provide different vertex attribute data based on context? (Per-triangle would be idea.) Or should I just duplicate each vertex for each context in which it gets rendered. (So there is a one-to-one mapping between vertex, normal, color, etc.) Note: I'm using OpenGL ES.

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• Netcat I/O enhancements

  - by user13277689

  When Netcat integrated into OpenSolaris it was already clear that there will be couple of enhancements needed. The biggest set of the changes made after Solaris 11 Express was released brings various I/O enhancements to netcat shipped with Solaris 11. Also, since Solaris 11, the netcat package is installed by default in all distribution forms (live CD, text install, ...). Now, let's take a look at the new functionality: /usr/bin/netcat alternative program name (symlink) -b bufsize I/O buffer size -E use exclusive bind for the listening socket -e program program to execute -F no network close upon EOF on stdin -i timeout extension of timeout specification -L timeout linger on close timeout -l -p port addr previously not allowed usage -m byte_count Quit after receiving byte_count bytes -N file pattern for UDP scanning -I bufsize size of input socket buffer -O bufsize size of output socket buffer -R redir_spec port redirection addr/port[/{tcp,udp}] syntax of redir_spec -Z bypass zone boundaries -q timeout timeout after EOF on stdin Obviously, the Swiss army knife of networking tools just got a bit thicker. While by themselves the options are pretty self explanatory, their combination together with other options, context of use or boundary values of option arguments make it possible to construct small but powerful tools. For example: the port redirector allows to convert TCP stream to UDP datagrams. the buffer size specification makes it possible to send one byte TCP segments or to produce IP fragments easily. the socket linger option can be used to produce TCP RST segments by setting the timeout to 0 execute option makes it possible to simulate TCP/UDP servers or clients with shell/python/Perl/whatever script etc. If you find some other helpful ways use please share via comments. Manual page nc(1) contains more details, along with examples on how to use some of these new options.

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• What is UVIndex and how do I use it on OpenGL?

  - by Delta

  I am a noob in OpenGL ES 2.0 (for WebGL) and I'm trying to draw a simple model I've made with a 3D tool and exported to .fbx format. I've been able to draw some models that only have: A vertex buffer, a index buffer for the vertices, a normal buffer and a texture coordinate buffer, but this model now has a "UVIndex" and I'm not sure where am I supposed to put this UVIndex. My code looks like this: GL.bindBuffer(GL.ARRAY_BUFFER, this.Model.House.VertexBuffer); GL.vertexAttribPointer(this.Shader.TextureAndLighting.Attribute["vPosition"],3,GL.FLOAT, false, 0, 0); GL.bindBuffer(GL.ARRAY_BUFFER, this.Model.House.NormalBuffer); GL.vertexAttribPointer(this.Shader.TextureAndLighting.Attribute["vNormal"], 3, GL.FLOAT, false, 0, 0); GL.bindBuffer(GL.ARRAY_BUFFER, this.Model.House.TexCoordBuffer); GL.vertexAttribPointer(this.Shader.TextureAndLighting.Attribute["TexCoord"], 2, GL.FLOAT, false, 0, 0); GL.bindBuffer(GL.ELEMENT_ARRAY_BUFFER, this.Model.House.IndexBuffer); GL.bindTexture(GL.TEXTURE_2D, this.Texture.HTex1); GL.activeTexture(GL.TEXTURE0); GL.drawElements(GL.TRIANGLES, this.Model.House.IndexBuffer.Length, GL.UNSIGNED_SHORT, 0); But my model renders totally incorrect and I think it has to do with the fact that I am ignoring this "UVIndex" in the .fbx file, since I've never drawn any model that uses this UVIndex I really have no clue on what to do with it. This is the json file containing the model's data: http://pastebin.com/raw.php?i=G294TVmz

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• What calls trigger a new batch?

  - by sebf

  I am finding my project is starting to show performance degradation and I need to optimize it. The answer to my previous question and this presentation from NVidia have helped greatly in understanding the performance characteristics of code using the GPU but there are a couple of things that aren't clear that I need to know to optimize my drawing. Specifically, what calls make the distinction between batches. I know that any state changes cause a new batch, so that includes: Render State Changes Buffer Changes Shader Changes Render Target Changes Correct? What else counts as a 'state change'? Does each Draw**Primitive() call constitute a new batch? Even if I were to issue the same call twice, with no state changes, or call it once on on part of the buffer, then again on another? If I were to update a buffer, but not change the bindings, would that be a new batch? That presentation and a DX9 page suggest using all of the texture slots available, which I take to mean loading multiple objects in 'parallel' by mapping their buffers/shaders/textures to slots 1-16. But I am not sure how this works - surely to do this you would need to change the buffer binding and that would count as a state change? (or is it a case of you do but it saves 16 calls so its OK?)

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• Emacs stops taking input when a file has changed on disk [migrated]

  - by recf

  I'm using Emacs v24.3.1 on Windows 8. I had a file change on disk while I had an Emacs buffer open with that file. As soon as I attempt to make a change to the buffer, a message appears in the minibuffer. Fileblah.txt changed on disk; really edit the buffer? (y, n, r or C-h) I would expect to be able to hit r to have it reload the disk version of the file, but nothing happens. Emacs completely stops responding to input. None of the listed keys work, nor do any other keys as far as I can tell. I can't C-g out of the minibuffer. Alt-F4 doesn't work, not does Close window from the task bar. I have to kill the process from task manager. Anyone have any idea what I'm doing wrong here? In cases it's various modes not playing nice with each other, for reference, my init.el is here. Nothing complex. Here's the breakdown: better-defaults (ido-mode, remove menu-bar, uniquify buffer `forward, saveplace) recentf-mode custom frame title visual-line-mode require final newline and delete trailing whitespace on save Markdown mode with auto-mode-alist Flyspell with Aspell backend Powershell mode with auto-mode-alist Ruby auto-mode-alist Puppet mode with auto-mode-alist Feature (Gherkin) mode with auto-mode-alist The specific file was a markdown file with Github-flavored Markdown mode and Flyspell mode enabled.

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• Ignoring focusLost(), SWT.Verify, or other SWT listeners in Java code.

  - by Zoot

  Outside of the actual SWT listener, is there any way to ignore a listener via code? For example, I have a java program that implements SWT Text Widgets, and the widgets have: SWT.Verify listeners to filter out unwanted text input. ModifyListeners to wait for the correct number of valid input characters and automatically set focus (using setFocus())to the next valid field, skipping the other text widgets in the tab order. focusLost(FocusEvent) FocusListeners that wait for the loss of focus from the text widget to perform additional input verification and execute an SQL query based on the user input. The issue I run into is clearing the text widgets. One of the widgets has the format "####-##" (Four Numbers, a hyphen, then two numbers) and I have implemented this listener, which is a modified version of SWT Snippet Snippet179. The initial text for this text widget is " - " to provide visual feedback to the user as to the expected format. Only numbers are acceptable input, and the program automatically skips past the hyphen at the appropriate point. /* * This listener was adapted from the "verify input in a template (YYYY/MM/DD)" SWT Code * Snippet (also known as Snippet179), from the Snippets page of the SWT Project. * SWT Code Snippets can be found at: * http://www.eclipse.org/swt/snippets/ */ textBox.addListener(SWT.Verify, new Listener() { boolean ignore; public void handleEvent(Event e) { if (ignore) return; e.doit = false; StringBuffer buffer = new StringBuffer(e.text); char[] chars = new char[buffer.length()]; buffer.getChars(0, chars.length, chars, 0); if (e.character == '\b') { for (int i = e.start; i < e.end; i++) { switch (i) { case 0: /* [x]xxx-xx */ case 1: /* x[x]xx-xx */ case 2: /* xx[x]x-xx */ case 3: /* xxx[x]-xx */ case 5: /* xxxx-[x]x */ case 6: /* xxxx-x[x] */ { buffer.append(' '); break; } case 4: /* xxxx[-]xx */ { buffer.append('-'); break; } default: return; } } textBox.setSelection(e.start, e.start + buffer.length()); ignore = true; textBox.insert(buffer.toString()); ignore = false; textBox.setSelection(e.start, e.start); return; } int start = e.start; if (start > 6) return; int index = 0; for (int i = 0; i < chars.length; i++) { if (start + index == 4) { if (chars[i] == '-') { index++; continue; } buffer.insert(index++, '-'); } if (chars[i] < '0' || '9' < chars[i]) return; index++; } String newText = buffer.toString(); int length = newText.length(); textBox.setSelection(e.start, e.start + length); ignore = true; textBox.insert(newText); ignore = false; /* * After a valid key press, verifying if the input is completed * and passing the cursor to the next text box. */ if (7 == textBox.getCaretPosition()) { /* * Attempting to change the text after receiving a known valid input that has no results (0000-00). */ if ("0000-00".equals(textBox.getText())) { // "0000-00" is the special "Erase Me" code for these text boxes. ignore = true; textBox.setText(" - "); ignore = false; } // Changing focus to a different textBox by using "setFocus()" method. differentTextBox.setFocus(); } } } ); As you can see, the only method I've figured out to clear this text widget from a different point in the code is by assigning "0000-00" textBox.setText("000000") and checking for that input in the listener. When that input is received, the listener changes the text back to " - " (four spaces, a hyphen, then two spaces). There is also a focusLost Listener that parses this text widget for spaces, then in order to avoid unnecessary SQL queries, it clears/resets all fields if the input is invalid (i.e contains spaces). // Adding focus listener to textBox to wait for loss of focus to perform SQL statement. textBox.addFocusListener(new FocusAdapter() { @Override public void focusLost(FocusEvent evt) { // Get the contents of otherTextBox and textBox. (otherTextBox must be <= textBox) String boxFour = otherTextBox.getText(); String boxFive = textBox.getText(); // If either text box has spaces in it, don't perform the search. if (boxFour.contains(" ") || boxFive.contains(" ")) { // Don't perform SQL statements. Debug statement. System.out.println("Tray Position input contains spaces. Ignoring."); //Make all previous results invisible, if any. labels.setVisible(false); differentTextBox.setText(""); labelResults.setVisible(false); } else { //... Perform SQL statement ... } } } ); OK. Often, I use SWT MessageBox widgets in this code to communicate to the user, or wish to change the text widgets back to an empty state after verifying the input. The problem is that messageboxes seem to create a focusLost event, and using the .setText(string) method is subject to SWT.Verify listeners that are present on the text widget. Any suggestions as to selectively ignoring these listeners in code, but keeping them present for all other user input? Thank you in advance for your assistance.

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• Unknown error in Producer/Consumer program, believe it to be an infinite loop.

  - by ray2k

  Hello, I am writing a program that is solving the producer/consumer problem, specifically the bounded-buffer version(i believe they mean the same thing). The producer will be generating x number of random numbers, where x is a command line parameter to my program. At the current moment, I believe my program is entering an infinite loop, but I'm not sure why it is occurring. I believe I am executing the semaphores correctly. You compile it like this: gcc -o prodcon prodcon.cpp -lpthread -lrt Then to run, ./prodcon 100(the number of randum nums to produce) This is my code. typedef int buffer_item; #include <stdlib.h> #include <stdio.h> #include <pthread.h> #include <semaphore.h> #include <unistd.h> #define BUFF_SIZE 10 #define RAND_DIVISOR 100000000 #define TRUE 1 //two threads void *Producer(void *param); void *Consumer(void *param); int insert_item(buffer_item item); int remove_item(buffer_item *item); int returnRandom(); //the global semaphores sem_t empty, full, mutex; //the buffer buffer_item buf[BUFF_SIZE]; //buffer counter int counter; //number of random numbers to produce int numRand; int main(int argc, char** argv) { /* thread ids and attributes */ pthread_t pid, cid; pthread_attr_t attr; pthread_attr_init(&attr); pthread_attr_setscope(&attr, PTHREAD_SCOPE_SYSTEM); numRand = atoi(argv[1]); sem_init(&empty,0,BUFF_SIZE); sem_init(&full,0,0); sem_init(&mutex,0,0); printf("main started\n"); pthread_create(&pid, &attr, Producer, NULL); pthread_create(&cid, &attr, Consumer, NULL); printf("main gets here"); pthread_join(pid, NULL); pthread_join(cid, NULL); printf("main done\n"); return 0; } //generates a randum number between 1 and 100 int returnRandom() { int num; srand(time(NULL)); num = rand() % 100 + 1; return num; } //begin producing items void *Producer(void *param) { buffer_item item; int i; for(i = 0; i < numRand; i++) { //sleep for a random period of time int rNum = rand() / RAND_DIVISOR; sleep(rNum); //generate a random number item = returnRandom(); //acquire the empty lock sem_wait(&empty); //acquire the mutex lock sem_wait(&mutex); if(insert_item(item)) { fprintf(stderr, " Producer report error condition\n"); } else { printf("producer produced %d\n", item); } /* release the mutex lock */ sem_post(&mutex); /* signal full */ sem_post(&full); } return NULL; } /* Consumer Thread */ void *Consumer(void *param) { buffer_item item; int i; for(i = 0; i < numRand; i++) { /* sleep for a random period of time */ int rNum = rand() / RAND_DIVISOR; sleep(rNum); /* aquire the full lock */ sem_wait(&full); /* aquire the mutex lock */ sem_wait(&mutex); if(remove_item(&item)) { fprintf(stderr, "Consumer report error condition\n"); } else { printf("consumer consumed %d\n", item); } /* release the mutex lock */ sem_post(&mutex); /* signal empty */ sem_post(&empty); } return NULL; } /* Add an item to the buffer */ int insert_item(buffer_item item) { /* When the buffer is not full add the item and increment the counter*/ if(counter < BUFF_SIZE) { buf[counter] = item; counter++; return 0; } else { /* Error the buffer is full */ return -1; } } /* Remove an item from the buffer */ int remove_item(buffer_item *item) { /* When the buffer is not empty remove the item and decrement the counter */ if(counter > 0) { *item = buf[(counter-1)]; counter--; return 0; } else { /* Error buffer empty */ return -1; } }

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• Producer and Consumer Threads Hang

  - by user972425

  So this is my first foray into threads and thus far it is driving me insane. My problem seems to be some kind of synchronization error that causes my consumer thread to hang. I've looked at other code and just about everything I could find and I can't find what my error is. There also seems to be a discrepancy between the code being executed in Eclipse and via javac in the command line. Intention - Using a bounded buffer (with 1000 slots) create and consume 1,000,000 doubles. Use only notify and wait. Problem - In Eclipse the consumer thread will occasionally hang around 940,000 iterations, but other times completes. In the command line the consumer thread always hangs. Output - Eclipse - Successful Producer has produced 100000 doubles. Consumer has consumed 100000 doubles. Producer has produced 200000 doubles. Consumer has consumed 200000 doubles. Producer has produced 300000 doubles. Consumer has consumed 300000 doubles. Producer has produced 400000 doubles. Consumer has consumed 400000 doubles. Producer has produced 500000 doubles. Consumer has consumed 500000 doubles. Producer has produced 600000 doubles. Consumer has consumed 600000 doubles. Producer has produced 700000 doubles. Consumer has consumed 700000 doubles. Producer has produced 800000 doubles. Consumer has consumed 800000 doubles. Producer has produced 900000 doubles. Consumer has consumed 900000 doubles. Producer has produced 1000000 doubles. Producer has produced all items. Consumer has consumed 1000000 doubles. Consumer has consumed all items. Exitting Output - Command Line/Eclipse - Unsuccessful Producer has produced 100000 doubles. Consumer has consumed 100000 doubles. Producer has produced 200000 doubles. Consumer has consumed 200000 doubles. Producer has produced 300000 doubles. Consumer has consumed 300000 doubles. Producer has produced 400000 doubles. Consumer has consumed 400000 doubles. Producer has produced 500000 doubles. Consumer has consumed 500000 doubles. Producer has produced 600000 doubles. Consumer has consumed 600000 doubles. Producer has produced 700000 doubles. Consumer has consumed 700000 doubles. Producer has produced 800000 doubles. Consumer has consumed 800000 doubles. Producer has produced 900000 doubles. Consumer has consumed 900000 doubles. Producer has produced 1000000 doubles. Producer has produced all items. At this point it just sits and hangs. Any help you can provide about where I might have misstepped is greatly appreciated. Code - Producer thread import java.text.DecimalFormat;+ " doubles. Cumulative value of generated items= " + temp) import java.util.*; import java.io.*; public class producer implements Runnable{ private buffer produceBuff; public producer (buffer buff){ produceBuff = buff; } public void run(){ Random random = new Random(); double temp = 0, randomElem; DecimalFormat df = new DecimalFormat("#.###"); for(int i = 1; i<=1000000; i++) { randomElem = (Double.parseDouble( df.format(random.nextDouble() * 100.0))); try { produceBuff.add(randomElem); } catch (InterruptedException e) { // TODO Auto-generated catch block e.printStackTrace(); } temp+= randomElem; if(i%100000 == 0) {produceBuff.print("Producer has produced "+ i ); } } produceBuff.print("Producer has produced all items."); } } Consumer thread import java.util.*; import java.io.*; public class consumer implements Runnable{ private buffer consumBuff; public consumer (buffer buff){ consumBuff = buff; } public void run(){ double temp = 0; for(int i = 1; i<=1000000; i++) { try { temp += consumBuff.get(); } catch (InterruptedException e) { // TODO Auto-generated catch block e.printStackTrace(); } if(i%100000 == 0) {consumBuff.print("Consumer has consumed "+ i ); //if(i>999000) //{System.out.println("Consuming item " + i);} } consumBuff.print("Consumer has consumed all items."); } } Buffer/Main import java.util.*; import java.io.*; public class buffer { private double buff[]; private int addPlace; private int getPlace; public buffer(){ buff = new double[1000]; addPlace = 0; getPlace = 0; } public synchronized void add(double add) throws InterruptedException{ if((addPlace+1 == getPlace) ) { try { wait(); } catch (InterruptedException e) {throw e;} } buff[addPlace] = add; addPlace = (addPlace+1)%1000; notify(); } public synchronized double get()throws InterruptedException{ if(getPlace == addPlace) { try { wait(); } catch (InterruptedException e) {throw e;} } double temp = buff[getPlace]; getPlace = (getPlace+1)%1000; notify(); return temp; } public synchronized void print(String view) { System.out.println(view); } public static void main(String args[]){ buffer buf = new buffer(); Thread produce = new Thread(new producer(buf)); Thread consume = new Thread(new consumer(buf)); produce.start(); consume.start(); try { produce.join(); consume.join(); } catch (InterruptedException e) {return;} System.out.println("Exitting"); } }

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• Using R to Analyze G1GC Log Files

  - by user12620111

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  Using R to Analyze G1GC Log Files   Using R to Analyze G1GC Log Files Introduction Working in Oracle Platform Integration gives an engineer opportunities to work on a wide array of technologies. My team’s goal is to make Oracle applications run best on the Solaris/SPARC platform. When looking for bottlenecks in a modern applications, one needs to be aware of not only how the CPUs and operating system are executing, but also network, storage, and in some cases, the Java Virtual Machine. I was recently presented with about 1.5 GB of Java Garbage First Garbage Collector log file data. If you’re not familiar with the subject, you might want to review Garbage First Garbage Collector Tuning by Monica Beckwith. The customer had been running Java HotSpot 1.6.0_31 to host a web application server. I was told that the Solaris/SPARC server was running a Java process launched using a commmand line that included the following flags: -d64 -Xms9g -Xmx9g -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -XX:InitiatingHeapOccupancyPercent=80 -XX:PermSize=256m -XX:MaxPermSize=256m -XX:+PrintGC -XX:+PrintGCTimeStamps -XX:+PrintHeapAtGC -XX:+PrintGCDateStamps -XX:+PrintFlagsFinal -XX:+DisableExplicitGC -XX:+UnlockExperimentalVMOptions -XX:ParallelGCThreads=8 Several sources on the internet indicate that if I were to print out the 1.5 GB of log files, it would require enough paper to fill the bed of a pick up truck. Of course, it would be fruitless to try to scan the log files by hand. Tools will be required to summarize the contents of the log files. Others have encountered large Java garbage collection log files. There are existing tools to analyze the log files: IBM’s GC toolkit The chewiebug GCViewer gchisto HPjmeter Instead of using one of the other tools listed, I decide to parse the log files with standard Unix tools, and analyze the data with R. Data Cleansing The log files arrived in two different formats. I guess that the difference is that one set of log files was generated using a more verbose option, maybe -XX:+PrintHeapAtGC, and the other set of log files was generated without that option. Format 1 In some of the log files, the log files with the less verbose format, a single trace, i.e. the report of a singe garbage collection event, looks like this: {Heap before GC invocations=12280 (full 61): garbage-first heap total 9437184K, used 7499918K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 1 young (4096K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. 2014-05-14T07:24:00.988-0700: 60586.353: [GC pause (young) 7324M->7320M(9216M), 0.1567265 secs] Heap after GC invocations=12281 (full 61): garbage-first heap total 9437184K, used 7496533K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 0 young (0K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. } A simple grep can be used to extract a summary: $ grep "\[ GC pause (young" g1gc.log 2014-05-13T13:24:35.091-0700: 3.109: [GC pause (young) 20M->5029K(9216M), 0.0146328 secs] 2014-05-13T13:24:35.440-0700: 3.459: [GC pause (young) 9125K->6077K(9216M), 0.0086723 secs] 2014-05-13T13:24:37.581-0700: 5.599: [GC pause (young) 25M->8470K(9216M), 0.0203820 secs] 2014-05-13T13:24:42.686-0700: 10.704: [GC pause (young) 44M->15M(9216M), 0.0288848 secs] 2014-05-13T13:24:48.941-0700: 16.958: [GC pause (young) 51M->20M(9216M), 0.0491244 secs] 2014-05-13T13:24:56.049-0700: 24.066: [GC pause (young) 92M->26M(9216M), 0.0525368 secs] 2014-05-13T13:25:34.368-0700: 62.383: [GC pause (young) 602M->68M(9216M), 0.1721173 secs] But that format wasn't easily read into R, so I needed to be a bit more tricky. I used the following Unix command to create a summary file that was easy for R to read. $ echo "SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime" $ grep "\[GC pause (young" g1gc.log | grep -v mark | sed -e 's/[A-SU-z\(\),]/ /g' -e 's/->/ /' -e 's/: / /g' | more SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime 2014-05-13T13:24:35.091-0700 3.109 20 5029 9216 0.0146328 2014-05-13T13:24:35.440-0700 3.459 9125 6077 9216 0.0086723 2014-05-13T13:24:37.581-0700 5.599 25 8470 9216 0.0203820 2014-05-13T13:24:42.686-0700 10.704 44 15 9216 0.0288848 2014-05-13T13:24:48.941-0700 16.958 51 20 9216 0.0491244 2014-05-13T13:24:56.049-0700 24.066 92 26 9216 0.0525368 2014-05-13T13:25:34.368-0700 62.383 602 68 9216 0.1721173 Format 2 In some of the log files, the log files with the more verbose format, a single trace, i.e. the report of a singe garbage collection event, was more complicated than Format 1. Here is a text file with an example of a single G1GC trace in the second format. As you can see, it is quite complicated. It is nice that there is so much information available, but the level of detail can be overwhelming. I wrote this awk script (download) to summarize each trace on a single line. #!/usr/bin/env awk -f BEGIN { printf("SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize\n") } ###################### # Save count data from lines that are at the start of each G1GC trace. # Each trace starts out like this: # {Heap before GC invocations=14 (full 0): # garbage-first heap total 9437184K, used 325496K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) ###################### /{Heap.*full/{ gsub ( "\\)" , "" ); nf=split($0,a,"="); split(a[2],b," "); getline; if ( match($0, "first") ) { G1GC=1; IncrementalCount=b[1]; FullCount=substr( b[3], 1, length(b[3])-1 ); } else { G1GC=0; } } ###################### # Pull out time stamps that are in lines with this format: # 2014-05-12T14:02:06.025-0700: 94.312: [GC pause (young), 0.08870154 secs] ###################### /GC pause/ { DateTime=$1; SecondsSinceLaunch=substr($2, 1, length($2)-1); } ###################### # Heap sizes are in lines that look like this: # [ 4842M->4838M(9216M)] ###################### /\[ .*]$/ { gsub ( "\\[" , "" ); gsub ( "\ \]" , "" ); gsub ( "->" , " " ); gsub ( "\\( " , " " ); gsub ( "\ \)" , " " ); split($0,a," "); if ( split(a[1],b,"M") > 1 ) {BeforeSize=b[1]*1024;} if ( split(a[1],b,"K") > 1 ) {BeforeSize=b[1];} if ( split(a[2],b,"M") > 1 ) {AfterSize=b[1]*1024;} if ( split(a[2],b,"K") > 1 ) {AfterSize=b[1];} if ( split(a[3],b,"M") > 1 ) {TotalSize=b[1]*1024;} if ( split(a[3],b,"K") > 1 ) {TotalSize=b[1];} } ###################### # Emit an output line when you find input that looks like this: # [Times: user=1.41 sys=0.08, real=0.24 secs] ###################### /\[Times/ { if (G1GC==1) { gsub ( "," , "" ); split($2,a,"="); UserTime=a[2]; split($3,a,"="); SysTime=a[2]; split($4,a,"="); RealTime=a[2]; print DateTime,SecondsSinceLaunch,IncrementalCount,FullCount,UserTime,SysTime,RealTime,BeforeSize,AfterSize,TotalSize; G1GC=0; } } The resulting summary is about 25X smaller that the original file, but still difficult for a human to digest. SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ... 2014-05-12T18:36:34.669-0700: 3985.744 561 0 0.57 0.06 0.16 1724416 1720320 9437184 2014-05-12T18:36:34.839-0700: 3985.914 562 0 0.51 0.06 0.19 1724416 1720320 9437184 2014-05-12T18:36:35.069-0700: 3986.144 563 0 0.60 0.04 0.27 1724416 1721344 9437184 2014-05-12T18:36:35.354-0700: 3986.429 564 0 0.33 0.04 0.09 1725440 1722368 9437184 2014-05-12T18:36:35.545-0700: 3986.620 565 0 0.58 0.04 0.17 1726464 1722368 9437184 2014-05-12T18:36:35.726-0700: 3986.801 566 0 0.43 0.05 0.12 1726464 1722368 9437184 2014-05-12T18:36:35.856-0700: 3986.930 567 0 0.30 0.04 0.07 1726464 1723392 9437184 2014-05-12T18:36:35.947-0700: 3987.023 568 0 0.61 0.04 0.26 1727488 1723392 9437184 2014-05-12T18:36:36.228-0700: 3987.302 569 0 0.46 0.04 0.16 1731584 1724416 9437184 Reading the Data into R Once the GC log data had been cleansed, either by processing the first format with the shell script, or by processing the second format with the awk script, it was easy to read the data into R. g1gc.df = read.csv("summary.txt", row.names = NULL, stringsAsFactors=FALSE,sep="") str(g1gc.df) ## 'data.frame': 8307 obs. of 10 variables: ## $ row.names : chr "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ... ## $ SecondsSinceLaunch: num 1.16 1.47 1.97 3.83 6.1 ... ## $ IncrementalCount : int 0 1 2 3 4 5 6 7 8 9 ... ## $ FullCount : int 0 0 0 0 0 0 0 0 0 0 ... ## $ UserTime : num 0.11 0.05 0.04 0.21 0.08 0.26 0.31 0.33 0.34 0.56 ... ## $ SysTime : num 0.04 0.01 0.01 0.05 0.01 0.06 0.07 0.06 0.07 0.09 ... ## $ RealTime : num 0.02 0.02 0.01 0.04 0.02 0.04 0.05 0.04 0.04 0.06 ... ## $ BeforeSize : int 8192 5496 5768 22528 24576 43008 34816 53248 55296 93184 ... ## $ AfterSize : int 1400 1672 2557 4907 7072 14336 16384 18432 19456 21504 ... ## $ TotalSize : int 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 ... head(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount ## 1 2014-05-12T14:00:32.868-0700: 1.161 0 ## 2 2014-05-12T14:00:33.179-0700: 1.472 1 ## 3 2014-05-12T14:00:33.677-0700: 1.969 2 ## 4 2014-05-12T14:00:35.538-0700: 3.830 3 ## 5 2014-05-12T14:00:37.811-0700: 6.103 4 ## 6 2014-05-12T14:00:41.428-0700: 9.720 5 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 1 0 0.11 0.04 0.02 8192 1400 9437184 ## 2 0 0.05 0.01 0.02 5496 1672 9437184 ## 3 0 0.04 0.01 0.01 5768 2557 9437184 ## 4 0 0.21 0.05 0.04 22528 4907 9437184 ## 5 0 0.08 0.01 0.02 24576 7072 9437184 ## 6 0 0.26 0.06 0.04 43008 14336 9437184 Basic Statistics Once the data has been read into R, simple statistics are very easy to generate. All of the numbers from high school statistics are available via simple commands. For example, generate a summary of every column: summary(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount FullCount ## Length:8307 Min. : 1 Min. : 0 Min. : 0.0 ## Class :character 1st Qu.: 9977 1st Qu.:2048 1st Qu.: 0.0 ## Mode :character Median :12855 Median :4136 Median : 12.0 ## Mean :12527 Mean :4156 Mean : 31.6 ## 3rd Qu.:15758 3rd Qu.:6262 3rd Qu.: 61.0 ## Max. :55484 Max. :8391 Max. :113.0 ## UserTime SysTime RealTime BeforeSize ## Min. :0.040 Min. :0.0000 Min. : 0.0 Min. : 5476 ## 1st Qu.:0.470 1st Qu.:0.0300 1st Qu.: 0.1 1st Qu.:5137920 ## Median :0.620 Median :0.0300 Median : 0.1 Median :6574080 ## Mean :0.751 Mean :0.0355 Mean : 0.3 Mean :5841855 ## 3rd Qu.:0.920 3rd Qu.:0.0400 3rd Qu.: 0.2 3rd Qu.:7084032 ## Max. :3.370 Max. :1.5600 Max. :488.1 Max. :8696832 ## AfterSize TotalSize ## Min. : 1380 Min. :9437184 ## 1st Qu.:5002752 1st Qu.:9437184 ## Median :6559744 Median :9437184 ## Mean :5785454 Mean :9437184 ## 3rd Qu.:7054336 3rd Qu.:9437184 ## Max. :8482816 Max. :9437184 Q: What is the total amount of User CPU time spent in garbage collection? sum(g1gc.df$UserTime) ## [1] 6236 As you can see, less than two hours of CPU time was spent in garbage collection. Is that too much? To find the percentage of time spent in garbage collection, divide the number above by total_elapsed_time*CPU_count. In this case, there are a lot of CPU’s and it turns out the the overall amount of CPU time spent in garbage collection isn’t a problem when viewed in isolation. When calculating rates, i.e. events per unit time, you need to ask yourself if the rate is homogenous across the time period in the log file. Does the log file include spikes of high activity that should be separately analyzed? Averaging in data from nights and weekends with data from business hours may alias problems. If you have a reason to suspect that the garbage collection rates include peaks and valleys that need independent analysis, see the “Time Series” section, below. Q: How much garbage is collected on each pass? The amount of heap space that is recovered per GC pass is surprisingly low: At least one collection didn’t recover any data. (“Min.=0”) 25% of the passes recovered 3MB or less. (“1st Qu.=3072”) Half of the GC passes recovered 4MB or less. (“Median=4096”) The average amount recovered was 56MB. (“Mean=56390”) 75% of the passes recovered 36MB or less. (“3rd Qu.=36860”) At least one pass recovered 2GB. (“Max.=2121000”) g1gc.df$Delta = g1gc.df$BeforeSize - g1gc.df$AfterSize summary(g1gc.df$Delta) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0 3070 4100 56400 36900 2120000 Q: What is the maximum User CPU time for a single collection? The worst garbage collection (“Max.”) is many standard deviations away from the mean. The data appears to be right skewed. summary(g1gc.df$UserTime) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.040 0.470 0.620 0.751 0.920 3.370 sd(g1gc.df$UserTime) ## [1] 0.3966 Basic Graphics Once the data is in R, it is trivial to plot the data with formats including dot plots, line charts, bar charts (simple, stacked, grouped), pie charts, boxplots, scatter plots histograms, and kernel density plots. Histogram of User CPU Time per Collection I don't think that this graph requires any explanation. hist(g1gc.df$UserTime, main="User CPU Time per Collection", xlab="Seconds", ylab="Frequency") Box plot to identify outliers When the initial data is viewed with a box plot, you can see the one crazy outlier in the real time per GC. Save this data point for future analysis and drop the outlier so that it’s not throwing off our statistics. Now the box plot shows many outliers, which will be examined later, using times series analysis. Notice that the scale of the x-axis changes drastically once the crazy outlier is removed. par(mfrow=c(2,1)) boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(dominated by a crazy outlier)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") crazy.outlier.df=g1gc.df[g1gc.df$RealTime > 400,] g1gc.df=g1gc.df[g1gc.df$RealTime < 400,] boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(crazy outlier excluded)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") box(which = "outer", lty = "solid") Here is the crazy outlier for future analysis: crazy.outlier.df ## row.names SecondsSinceLaunch IncrementalCount ## 8233 2014-05-12T23:15:43.903-0700: 20741 8316 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 8233 112 0.55 0.42 488.1 8381440 8235008 9437184 ## Delta ## 8233 146432 R Time Series Data To analyze the garbage collection as a time series, I’ll use Z’s Ordered Observations (zoo). “zoo is the creator for an S3 class of indexed totally ordered observations which includes irregular time series.” require(zoo) ## Loading required package: zoo ## ## Attaching package: 'zoo' ## ## The following objects are masked from 'package:base': ## ## as.Date, as.Date.numeric head(g1gc.df[,1]) ## [1] "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" ## [3] "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ## [5] "2014-05-12T14:00:37.811-0700:" "2014-05-12T14:00:41.428-0700:" options("digits.secs"=3) times=as.POSIXct( g1gc.df[,1], format="%Y-%m-%dT%H:%M:%OS%z:") g1gc.z = zoo(g1gc.df[,-c(1)], order.by=times) head(g1gc.z) ## SecondsSinceLaunch IncrementalCount FullCount ## 2014-05-12 17:00:32.868 1.161 0 0 ## 2014-05-12 17:00:33.178 1.472 1 0 ## 2014-05-12 17:00:33.677 1.969 2 0 ## 2014-05-12 17:00:35.538 3.830 3 0 ## 2014-05-12 17:00:37.811 6.103 4 0 ## 2014-05-12 17:00:41.427 9.720 5 0 ## UserTime SysTime RealTime BeforeSize AfterSize ## 2014-05-12 17:00:32.868 0.11 0.04 0.02 8192 1400 ## 2014-05-12 17:00:33.178 0.05 0.01 0.02 5496 1672 ## 2014-05-12 17:00:33.677 0.04 0.01 0.01 5768 2557 ## 2014-05-12 17:00:35.538 0.21 0.05 0.04 22528 4907 ## 2014-05-12 17:00:37.811 0.08 0.01 0.02 24576 7072 ## 2014-05-12 17:00:41.427 0.26 0.06 0.04 43008 14336 ## TotalSize Delta ## 2014-05-12 17:00:32.868 9437184 6792 ## 2014-05-12 17:00:33.178 9437184 3824 ## 2014-05-12 17:00:33.677 9437184 3211 ## 2014-05-12 17:00:35.538 9437184 17621 ## 2014-05-12 17:00:37.811 9437184 17504 ## 2014-05-12 17:00:41.427 9437184 28672 Example of Two Benchmark Runs in One Log File The data in the following graph is from a different log file, not the one of primary interest to this article. I’m including this image because it is an example of idle periods followed by busy periods. It would be uninteresting to average the rate of garbage collection over the entire log file period. More interesting would be the rate of garbage collect in the two busy periods. Are they the same or different? Your production data may be similar, for example, bursts when employees return from lunch and idle times on weekend evenings, etc. Once the data is in an R Time Series, you can analyze isolated time windows. Clipping the Time Series data Flashing back to our test case… Viewing the data as a time series is interesting. You can see that the work intensive time period is between 9:00 PM and 3:00 AM. Lets clip the data to the interesting period:     par(mfrow=c(2,1)) plot(g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Complete Log File", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") clipped.g1gc.z=window(g1gc.z, start=as.POSIXct("2014-05-12 21:00:00"), end=as.POSIXct("2014-05-13 03:00:00")) plot(clipped.g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Limited to Benchmark Execution", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") box(which = "outer", lty = "solid") Cumulative Incremental and Full GC count Here is the cumulative incremental and full GC count. When the line is very steep, it indicates that the GCs are repeating very quickly. Notice that the scale on the Y axis is different for full vs. incremental. plot(clipped.g1gc.z[,c(2:3)], main="Cumulative Incremental and Full GC count", xlab="Time of Day", col="#1b9e77") GC Analysis of Benchmark Execution using Time Series data In the following series of 3 graphs: The “After Size” show the amount of heap space in use after each garbage collection. Many Java objects are still referenced, i.e. alive, during each garbage collection. This may indicate that the application has a memory leak, or may indicate that the application has a very large memory footprint. Typically, an application's memory footprint plateau's in the early stage of execution. One would expect this graph to have a flat top. The steep decline in the heap space may indicate that the application crashed after 2:00. The second graph shows that the outliers in real execution time, discussed above, occur near 2:00. when the Java heap seems to be quite full. The third graph shows that Full GCs are infrequent during the first few hours of execution. The rate of Full GC's, (the slope of the cummulative Full GC line), changes near midnight.   plot(clipped.g1gc.z[,c("AfterSize","RealTime","FullCount")], xlab="Time of Day", col=c("#1b9e77","red","#1b9e77")) GC Analysis of heap recovered Each GC trace includes the amount of heap space in use before and after the individual GC event. During garbage coolection, unreferenced objects are identified, the space holding the unreferenced objects is freed, and thus, the difference in before and after usage indicates how much space has been freed. The following box plot and bar chart both demonstrate the same point - the amount of heap space freed per garbage colloection is surprisingly low. par(mfrow=c(2,1)) boxplot(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", horizontal = TRUE, col="red") hist(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", breaks=100, col="red") box(which = "outer", lty = "solid") This graph is the most interesting. The dark blue area shows how much heap is occupied by referenced Java objects. This represents memory that holds live data. The red fringe at the top shows how much data was recovered after each garbage collection. barplot(clipped.g1gc.z[,c("AfterSize","Delta")], col=c("#7570b3","#e7298a"), xlab="Time of Day", border=NA) legend("topleft", c("Live Objects","Heap Recovered on GC"), fill=c("#7570b3","#e7298a")) box(which = "outer", lty = "solid") When I discuss the data in the log files with the customer, I will ask for an explaination for the large amount of referenced data resident in the Java heap. There are two are posibilities: There is a memory leak and the amount of space required to hold referenced objects will continue to grow, limited only by the maximum heap size. After the maximum heap size is reached, the JVM will throw an “Out of Memory” exception every time that the application tries to allocate a new object. If this is the case, the aplication needs to be debugged to identify why old objects are referenced when they are no longer needed. The application has a legitimate requirement to keep a large amount of data in memory. The customer may want to further increase the maximum heap size. Another possible solution would be to partition the application across multiple cluster nodes, where each node has responsibility for managing a unique subset of the data. Conclusion In conclusion, R is a very powerful tool for the analysis of Java garbage collection log files. The primary difficulty is data cleansing so that information can be read into an R data frame. Once the data has been read into R, a rich set of tools may be used for thorough evaluation.

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• *DX11, HLSL* - Colour as 4 floats or one UINT

  - by Paul

  With the DX11 pipeline, would it be much quicker for the vertex buffer to pass one single UINT with one byte per channel to the input assembler, as opposed to three floats? Then the vertex shader would convert the four bytes to four floats, which I guess is the required colour format for the pipeline. In this instance, colour accuracy isn't an issue. The vertex buffer would need to be updated many times per frame, so using a single UINT and saving 12 bytes for every vertex could well be worth it: quicker uploads to vram and also less memory used. But the cost is the extra shader work for every vertex to convert each 8 bits of the input UNIT into a float. Anyone have an idea if it might be worth doing? Or, is it possible for the pipeline to be set to just internally use a four-byte colour format? The swap chain buffer has been initialised as DXGI_FORMAT_R8G8B8A8_UNORM, so ultimately that's how the colour will be written. Thanks!

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• Message Buffers in cloud

  - by kaleidoscope

  Message Buffer is WCF queue in the cloud (although currently it does not provide all features of WCF queue). With on-premise WCF, you can take advantage of MSMQ, so that a message is sent to MSMQ by one endpoint, and another endpoint can get the message in a later time. The message is usually a SOAP message so that you can generate a client proxy and invoke the service operations just as invoking a normal WCF operation. Message Buffer is similar, but it also provides a REST API for you to work with the messages. Use it when you need a reliable WCF service. Message buffers can be consumed by non-azure components, "Message  buffers are accessible to applications using HTTP and do not require the Windows Azure platform AppFabric SDK"              How to: Configure an AppFabric Service Bus Message Buffer :    please find below link for more details: http://msdn.microsoft.com/en-us/library/ee794877.aspx http://msdn.microsoft.com/en-us/library/ee794877.aspx   Chandraprakash, S

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• Optimizing a thread safe Java NIO / Serialization / FIFO Queue [migrated]

  - by trialcodr

  I've written a thread safe, persistent FIFO for Serializable items. The reason for reinventing the wheel is that we simply can't afford any third party dependencies in this project and want to keep this really simple. The problem is it isn't fast enough. Most of it is undoubtedly due to reading and writing directly to disk but I think we should be able to squeeze a bit more out of it anyway. Any ideas on how to improve the performance of the 'take'- and 'add'-methods? /** * <code>DiskQueue</code> Persistent, thread safe FIFO queue for * <code>Serializable</code> items. */ public class DiskQueue<ItemT extends Serializable> { public static final int EMPTY_OFFS = -1; public static final int LONG_SIZE = 8; public static final int HEADER_SIZE = LONG_SIZE * 2; private InputStream inputStream; private OutputStream outputStream; private RandomAccessFile file; private FileChannel channel; private long offs = EMPTY_OFFS; private long size = 0; public DiskQueue(String filename) { try { boolean fileExists = new File(filename).exists(); file = new RandomAccessFile(filename, "rwd"); if (fileExists) { size = file.readLong(); offs = file.readLong(); } else { file.writeLong(size); file.writeLong(offs); } } catch (FileNotFoundException e) { throw new RuntimeException(e); } catch (IOException e) { throw new RuntimeException(e); } channel = file.getChannel(); inputStream = Channels.newInputStream(channel); outputStream = Channels.newOutputStream(channel); } /** * Add item to end of queue. */ public void add(ItemT item) { try { synchronized (this) { channel.position(channel.size()); ObjectOutputStream s = new ObjectOutputStream(outputStream); s.writeObject(item); s.flush(); size++; file.seek(0); file.writeLong(size); if (offs == EMPTY_OFFS) { offs = HEADER_SIZE; file.writeLong(offs); } notify(); } } catch (IOException e) { throw new RuntimeException(e); } } /** * Clears overhead by moving the remaining items up and shortening the file. */ public synchronized void defrag() { if (offs > HEADER_SIZE && size > 0) { try { long totalBytes = channel.size() - offs; ByteBuffer buffer = ByteBuffer.allocateDirect((int) totalBytes); channel.position(offs); for (int bytes = 0; bytes < totalBytes;) { int res = channel.read(buffer); if (res == -1) { throw new IOException("Failed to read data into buffer"); } bytes += res; } channel.position(HEADER_SIZE); buffer.flip(); for (int bytes = 0; bytes < totalBytes;) { int res = channel.write(buffer); if (res == -1) { throw new IOException("Failed to write buffer to file"); } bytes += res; } offs = HEADER_SIZE; file.seek(LONG_SIZE); file.writeLong(offs); file.setLength(HEADER_SIZE + totalBytes); } catch (IOException e) { throw new RuntimeException(e); } } } /** * Returns the queue overhead in bytes. */ public synchronized long overhead() { return (offs == EMPTY_OFFS) ? 0 : offs - HEADER_SIZE; } /** * Returns the first item in the queue, blocks if queue is empty. */ public ItemT peek() throws InterruptedException { block(); synchronized (this) { if (offs != EMPTY_OFFS) { return readItem(); } } return peek(); } /** * Returns the number of remaining items in queue. */ public synchronized long size() { return size; } /** * Removes and returns the first item in the queue, blocks if queue is empty. */ public ItemT take() throws InterruptedException { block(); try { synchronized (this) { if (offs != EMPTY_OFFS) { ItemT result = readItem(); size--; offs = channel.position(); file.seek(0); if (offs == channel.size()) { truncate(); } file.writeLong(size); file.writeLong(offs); return result; } } return take(); } catch (IOException e) { throw new RuntimeException(e); } } /** * Throw away all items and reset the file. */ public synchronized void truncate() { try { offs = EMPTY_OFFS; file.setLength(HEADER_SIZE); size = 0; } catch (IOException e) { throw new RuntimeException(e); } } /** * Block until an item is available. */ protected void block() throws InterruptedException { while (offs == EMPTY_OFFS) { try { synchronized (this) { wait(); file.seek(LONG_SIZE); offs = file.readLong(); } } catch (IOException e) { throw new RuntimeException(e); } } } /** * Read and return item. */ @SuppressWarnings("unchecked") protected ItemT readItem() { try { channel.position(offs); return (ItemT) new ObjectInputStream(inputStream).readObject(); } catch (ClassNotFoundException e) { throw new RuntimeException(e); } catch (IOException e) { throw new RuntimeException(e); } } }

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• Indexed Drawing in OpenGL not working

  - by user2050846

  I am trying to render 2 types of primitives- - points ( a Point Cloud ) - triangles ( a Mesh ) I am rendering points simply without any index arrays and they are getting rendered fine. To render the meshes I am using indexed drawing with the face list array having the indices of the vertices to be rendered as Triangles. Vertices and their corresponding vertex colors are stored in their corresponding buffers. But the indexed drawing command do not draw anything. The code is as follows- Main Display Function: void display() { simple->enable(); simple->bindUniform("MV",modelview); simple->bindUniform("P", projection); // rendering Point Cloud glBindVertexArray(vao); // Vertex buffer Point Cloud glBindBuffer(GL_ARRAY_BUFFER,vertexbuffer); glEnableVertexAttribArray(0); glVertexAttribPointer(0,3,GL_FLOAT,GL_FALSE,0,0); // Color Buffer point Cloud glBindBuffer(GL_ARRAY_BUFFER,colorbuffer); glEnableVertexAttribArray(1); glVertexAttribPointer(1,3,GL_FLOAT,GL_FALSE,0,0); // Render Colored Point Cloud //glDrawArrays(GL_POINTS,0,model->vertexCount); glDisableVertexAttribArray(0); glDisableVertexAttribArray(1); // ---------------- END---------------------// //// Floor Rendering glBindBuffer(GL_ARRAY_BUFFER,fl); glEnableVertexAttribArray(0); glEnableVertexAttribArray(1); glVertexAttribPointer(0,3,GL_FLOAT,GL_FALSE,0,0); glVertexAttribPointer(1,4,GL_FLOAT,GL_FALSE,0,(void *)48); glDrawArrays(GL_QUADS,0,4); glDisableVertexAttribArray(0); glDisableVertexAttribArray(1); // -----------------END---------------------// //Rendering the Meshes //////////// PART OF CODE THAT IS NOT DRAWING ANYTHING //////////////////// glBindVertexArray(vid); for(int i=0;i<NUM_MESHES;i++) { glBindBuffer(GL_ARRAY_BUFFER,mVertex[i]); glEnableVertexAttribArray(0); glEnableVertexAttribArray(1); glVertexAttribPointer(0,3,GL_FLOAT,GL_FALSE,0,0); glVertexAttribPointer(1,3,GL_FLOAT,GL_FALSE,0,(void *)(meshes[i]->vertexCount*sizeof(glm::vec3))); //glDrawArrays(GL_TRIANGLES,0,meshes[i]->vertexCount); glBindBuffer(GL_ELEMENT_ARRAY_BUFFER,mFace[i]); //cout<<gluErrorString(glGetError()); glDrawElements(GL_TRIANGLES,meshes[i]->faceCount*3,GL_FLOAT,(void *)0); glDisableVertexAttribArray(0); glDisableVertexAttribArray(1); } glUseProgram(0); glutSwapBuffers(); glutPostRedisplay(); } Point Cloud Buffer Allocation Initialization: void initGLPointCloud() { glGenBuffers(1,&vertexbuffer); glGenBuffers(1,&colorbuffer); glGenBuffers(1,&fl); //Populates the position buffer glBindBuffer(GL_ARRAY_BUFFER,vertexbuffer); glBufferData(GL_ARRAY_BUFFER, model->vertexCount * sizeof (glm::vec3), &model->positions[0], GL_STATIC_DRAW); //Populates the color buffer glBindBuffer(GL_ARRAY_BUFFER, colorbuffer); glBufferData(GL_ARRAY_BUFFER, model->vertexCount * sizeof (glm::vec3), &model->colors[0], GL_STATIC_DRAW); model->FreeMemory(); // To free the not needed memory, as the data has been already // copied on graphic card, and wont be used again. glBindBuffer(GL_ARRAY_BUFFER,0); } Meshes Buffer Initialization: void initGLMeshes(int i) { glBindBuffer(GL_ARRAY_BUFFER,mVertex[i]); glBufferData(GL_ARRAY_BUFFER,meshes[i]->vertexCount*sizeof(glm::vec3)*2,NULL,GL_STATIC_DRAW); glBufferSubData(GL_ARRAY_BUFFER,0,meshes[i]->vertexCount*sizeof(glm::vec3),&meshes[i]->positions[0]); glBufferSubData(GL_ARRAY_BUFFER,meshes[i]->vertexCount*sizeof(glm::vec3),meshes[i]->vertexCount*sizeof(glm::vec3),&meshes[i]->colors[0]); glBindBuffer(GL_ELEMENT_ARRAY_BUFFER,mFace[i]); glBufferData(GL_ELEMENT_ARRAY_BUFFER,meshes[i]->faceCount*sizeof(glm::vec3), &meshes[i]->faces[0],GL_STATIC_DRAW); meshes[i]->FreeMemory(); //glBindBuffer(GL_ELEMENT_ARRAY_BUFFER,0); } Initialize the Rendering, load and create shader and calls the mesh and PCD initializers. void initRender() { simple= new GLSLShader("shaders/simple.vert","shaders/simple.frag"); //Point Cloud //Sets up VAO glGenVertexArrays(1, &vao); glBindVertexArray(vao); initGLPointCloud(); //floorData glBindBuffer(GL_ARRAY_BUFFER, fl); glBufferData(GL_ARRAY_BUFFER, sizeof(floorData), &floorData[0], GL_STATIC_DRAW); glBindBuffer(GL_ARRAY_BUFFER,0); glBindVertexArray(0); //Meshes for(int i=0;i<NUM_MESHES;i++) { if(i==0) // SET up the new vertex array state for indexed Drawing { glGenVertexArrays(1, &vid); glBindVertexArray(vid); glGenBuffers(NUM_MESHES,mVertex); glGenBuffers(NUM_MESHES,mColor); glGenBuffers(NUM_MESHES,mFace); } initGLMeshes(i); } glEnable(GL_DEPTH_TEST); } Any help would be much appreciated, I have been breaking my head on this problem since 3 days, and still it is unsolved.

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• fast java2d translucency

  - by mdriesen

  I'm trying to draw a bunch of translucent circles on a Swing JComponent. This isn't exactly fast, and I was wondering if there is a way to speed it up. My custom JComponent has the following paintComponent method: public void paintComponent(Graphics g) { Rectangle view = g.getClipBounds(); VolatileImage image = createVolatileImage(view.width, view.height); Graphics2D buffer = image.createGraphics(); // translate to camera location buffer.translate(-cx, -cy); // renderables contains all currently visible objects for(Renderable r : renderables) { r.paint(buffer); } g.drawImage(image.getSnapshot(), view.x, view.y, this); } The paint method of my circles is as follows: public void paint(Graphics2D graphics) { graphics.setPaint(paint); graphics.fillOval(x, y, radius, radius); } The paint is just an rgba color with a < 255: Color(int r, int g, int b, int a) It works fast enough for opaque objects, but is there a simple way to speed this up for translucent ones?

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• How do I build a matrix to translate one set of points to another?

  - by dotminic

  I've got 3 points in space that define a triangle. I've also got a vertex buffer made up of three vertices, that also represent a triangle that I will refer to as a "model". How can I can I find the matrix M that will transform vertex in my buffer to those 3 points in space ? For example, let's say my three points A, B, C are at locations: A.x = 10, A.y = 16, A.z = 8 B.x = 12, B.y = 11, B.z = 1 C.x = 19, C.y = 12, C.z = 3 given these coordinates how can I build a matrix that will translate and rotate my model such that both triangles have the exact same world space ? That is, I want the first vertex in my triangle model to have the same coordinates as A, the second to have the same coordinates as B, and same goes for C. nb: I'm using instanced rendering so I can't just give each vertex the same position as my 3 points. I have a set of three points defining a triangle, and only three vertices in my vertex buffer.

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