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• SPARC T4-4 Delivers World Record First Result on PeopleSoft Combined Benchmark

  - by Brian

  Oracle's SPARC T4-4 servers running Oracle's PeopleSoft HCM 9.1 combined online and batch benchmark achieved World Record 18,000 concurrent users while executing a PeopleSoft Payroll batch job of 500,000 employees in 43.32 minutes and maintaining online users response time at < 2 seconds. This world record is the first to run online and batch workloads concurrently. This result was obtained with a SPARC T4-4 server running Oracle Database 11g Release 2, a SPARC T4-4 server running PeopleSoft HCM 9.1 application server and a SPARC T4-2 server running Oracle WebLogic Server in the web tier. The SPARC T4-4 server running the application tier used Oracle Solaris Zones which provide a flexible, scalable and manageable virtualization environment. The average CPU utilization on the SPARC T4-2 server in the web tier was 17%, on the SPARC T4-4 server in the application tier it was 59%, and on the SPARC T4-4 server in the database tier was 35% (online and batch) leaving significant headroom for additional processing across the three tiers. The SPARC T4-4 server used for the database tier hosted Oracle Database 11g Release 2 using Oracle Automatic Storage Management (ASM) for database files management with I/O performance equivalent to raw devices. This is the first three tier mixed workload (online and batch) PeopleSoft benchmark also processing PeopleSoft payroll batch workload. Performance Landscape PeopleSoft HR Self-Service and Payroll Benchmark Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-2 (web) SPARC T4-4 (app) SPARC T4-2 (db) 18,000 0.944 0.503 43.32 64 Configuration Summary Application Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 512 GB memory 5 x 300 GB SAS internal disks 1 x 100 GB and 2 x 300 GB internal SSDs 2 x 10 Gbe HBA Oracle Solaris 11 11/11 PeopleTools 8.52 PeopleSoft HCM 9.1 Oracle Tuxedo, Version 10.3.0.0, 64-bit, Patch Level 031 Java Platform, Standard Edition Development Kit 6 Update 32 Database Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 256 GB memory 3 x 300 GB SAS internal disks Oracle Solaris 11 11/11 Oracle Database 11g Release 2 Web Tier Configuration: 1 x SPARC T4-2 server with 2 x SPARC T4 processors, 2.85 GHz 256 GB memory 2 x 300 GB SAS internal disks 1 x 100 GB internal SSD Oracle Solaris 11 11/11 PeopleTools 8.52 Oracle WebLogic Server 10.3.4 Java Platform, Standard Edition Development Kit 6 Update 32 Storage Configuration: 1 x Sun Server X2-4 as a COMSTAR head for data 4 x Intel Xeon X7550, 2.0 GHz 128 GB memory 1 x Sun Storage F5100 Flash Array (80 flash modules) 1 x Sun Storage F5100 Flash Array (40 flash modules) 1 x Sun Fire X4275 as a COMSTAR head for redo logs 12 x 2 TB SAS disks with Niwot Raid controller Benchmark Description This benchmark combines PeopleSoft HCM 9.1 HR Self Service online and PeopleSoft Payroll batch workloads to run on a unified database deployed on Oracle Database 11g Release 2. The PeopleSoft HRSS benchmark kit is a Oracle standard benchmark kit run by all platform vendors to measure the performance. It's an OLTP benchmark where DB SQLs are moderately complex. The results are certified by Oracle and a white paper is published. PeopleSoft HR SS defines a business transaction as a series of HTML pages that guide a user through a particular scenario. Users are defined as corporate Employees, Managers and HR administrators. The benchmark consist of 14 scenarios which emulate users performing typical HCM transactions such as viewing paycheck, promoting and hiring employees, updating employee profile and other typical HCM application transactions. All these transactions are well-defined in the PeopleSoft HR Self-Service 9.1 benchmark kit. This benchmark metric is the weighted average response search/save time for all the transactions. The PeopleSoft 9.1 Payroll (North America) benchmark demonstrates system performance for a range of processing volumes in a specific configuration. This workload represents large batch runs typical of a ERP environment during a mass update. The benchmark measures five application business process run times for a database representing large organization. They are Paysheet Creation, Payroll Calculation, Payroll Confirmation, Print Advice forms, and Create Direct Deposit File. The benchmark metric is the cumulative elapsed time taken to complete the Paysheet Creation, Payroll Calculation and Payroll Confirmation business application processes. The benchmark metrics are taken for each respective benchmark while running simultaneously on the same database back-end. Specifically, the payroll batch processes are started when the online workload reaches steady state (the maximum number of online users) and overlap with online transactions for the duration of the steady state. Key Points and Best Practices Two Oracle PeopleSoft Domain sets with 200 application servers each on a SPARC T4-4 server were hosted in 2 separate Oracle Solaris Zones to demonstrate consolidation of multiple application servers, ease of administration and performance tuning. Each Oracle Solaris Zone was bound to a separate processor set, each containing 15 cores (total 120 threads). The default set (1 core from first and third processor socket, total 16 threads) was used for network and disk interrupt handling. This was done to improve performance by reducing memory access latency by using the physical memory closest to the processors and offload I/O interrupt handling to default set threads, freeing up cpu resources for Application Servers threads and balancing application workload across 240 threads. See Also Oracle PeopleSoft Benchmark White Papers oracle.com SPARC T4-2 Server oracle.com OTN SPARC T4-4 Server oracle.com OTN PeopleSoft Enterprise Human Capital Management oracle.com OTN PeopleSoft Enterprise Human Capital Management (Payroll) oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Disclosure Statement Oracle's PeopleSoft HR and Payroll combined benchmark, www.oracle.com/us/solutions/benchmark/apps-benchmark/peoplesoft-167486.html, results 09/30/2012.

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• Query on simple C++ threadpool implementation

  - by ticketman

  Stackoverflow has been a tremendous help to me and I'd to give something back to the community. I have been implementing a simple threadpool using the tinythread C++ portable thread library, using what I have learnt from Stackoverflow. I am new to thread programming, so not that comfortable with mutexes, etc. I have a question best asked after presenting the code (which runs quite well under Linux): // ThreadPool.h class ThreadPool { public: ThreadPool(); ~ThreadPool(); // Creates a pool of threads and gets them ready to be used void CreateThreads(int numOfThreads); // Assigns a job to a thread in the pool, but doesn't start the job // Each SubmitJob call will use up one thread of the pool. // This operation can only be undone by calling StartJobs and // then waiting for the jobs to complete. On completion, // new jobs may be submitted. void SubmitJob( void (*workFunc)(void *), void *workData ); // Begins execution of all the jobs in the pool. void StartJobs(); // Waits until all jobs have completed. // The wait will block the caller. // On completion, new jobs may be submitted. void WaitForJobsToComplete(); private: enum typeOfWorkEnum { e_work, e_quit }; class ThreadData { public: bool ready; // thread has been created and is ready for work bool haveWorkToDo; typeOfWorkEnum typeOfWork; // Pointer to the work function each thread has to call. void (*workFunc)(void *); // Pointer to work data void *workData; ThreadData() : ready(false), haveWorkToDo(false) { }; }; struct ThreadArgStruct { ThreadPool *threadPoolInstance; int threadId; }; // Data for each thread ThreadData *m_ThreadData; ThreadPool(ThreadPool const&); // copy ctor hidden ThreadPool& operator=(ThreadPool const&); // assign op. hidden // Static function that provides the function pointer that a thread can call // By including the ThreadPool instance in the void * parameter, // we can use it to access other data and methods in the ThreadPool instance. static void ThreadFuncWrapper(void *arg) { ThreadArgStruct *threadArg = static_cast<ThreadArgStruct *>(arg); threadArg->threadPoolInstance->ThreadFunc(threadArg->threadId); } // The function each thread calls void ThreadFunc( int threadId ); // Called by the thread pool destructor void DestroyThreadPool(); // Total number of threads available // (fixed on creation of thread pool) int m_numOfThreads; int m_NumOfThreadsDoingWork; int m_NumOfThreadsGivenJobs; // List of threads std::vector<tthread::thread *> m_ThreadList; // Condition variable to signal each thread has been created and executing tthread::mutex m_ThreadReady_mutex; tthread::condition_variable m_ThreadReady_condvar; // Condition variable to signal each thread to start work tthread::mutex m_WorkToDo_mutex; tthread::condition_variable m_WorkToDo_condvar; // Condition variable to signal the main thread that // all threads in the pool have completed their work tthread::mutex m_WorkCompleted_mutex; tthread::condition_variable m_WorkCompleted_condvar; }; cpp file: // // ThreadPool.cpp // #include "ThreadPool.h" // This is the thread function for each thread. // All threads remain in this function until // they are asked to quit, which only happens // when terminating the thread pool. void ThreadPool::ThreadFunc( int threadId ) { ThreadData *myThreadData = &m_ThreadData[threadId]; std::cout << "Hello world: Thread " << threadId << std::endl; // Signal that this thread is ready m_ThreadReady_mutex.lock(); myThreadData->ready = true; m_ThreadReady_condvar.notify_one(); // notify the main thread m_ThreadReady_mutex.unlock(); while(true) { //tthread::lock_guard<tthread::mutex> guard(m); m_WorkToDo_mutex.lock(); while(!myThreadData->haveWorkToDo) // check for work to do m_WorkToDo_condvar.wait(m_WorkToDo_mutex); // if no work, wait here myThreadData->haveWorkToDo = false; // need to do this before unlocking the mutex m_WorkToDo_mutex.unlock(); // Do the work switch(myThreadData->typeOfWork) { case e_work: std::cout << "Thread " << threadId << ": Woken with work to do\n"; // Do work myThreadData->workFunc(myThreadData->workData); std::cout << "#Thread " << threadId << ": Work is completed\n"; break; case e_quit: std::cout << "Thread " << threadId << ": Asked to quit\n"; return; // ends the thread } // Now to signal the main thread that my work is completed m_WorkCompleted_mutex.lock(); m_NumOfThreadsDoingWork--; // Unsure if this 'if' would make the program more efficient // if(NumOfThreadsDoingWork == 0) m_WorkCompleted_condvar.notify_one(); // notify the main thread m_WorkCompleted_mutex.unlock(); } } ThreadPool::ThreadPool() { m_numOfThreads = 0; m_NumOfThreadsDoingWork = 0; m_NumOfThreadsGivenJobs = 0; } ThreadPool::~ThreadPool() { if(m_numOfThreads) { DestroyThreadPool(); delete [] m_ThreadData; } } void ThreadPool::CreateThreads(int numOfThreads) { // Check a thread pool has already been created if(m_numOfThreads > 0) return; m_NumOfThreadsGivenJobs = 0; m_NumOfThreadsDoingWork = 0; m_numOfThreads = numOfThreads; m_ThreadData = new ThreadData[m_numOfThreads]; ThreadArgStruct threadArg; for(int i=0; i<m_numOfThreads; ++i) { threadArg.threadId = i; threadArg.threadPoolInstance = this; // Creates the thread and save in a list so we can destroy it later m_ThreadList.push_back( new tthread::thread( ThreadFuncWrapper, (void *)&threadArg ) ); // It takes a little time for a thread to get established. // Best wait until it gets established before creating the next thread. m_ThreadReady_mutex.lock(); while(!m_ThreadData[i].ready) // Check if thread is ready m_ThreadReady_condvar.wait(m_ThreadReady_mutex); // If not, wait here m_ThreadReady_mutex.unlock(); } } // Adds a job to the batch, but doesn't start the job void ThreadPool::SubmitJob(void (*workFunc)(void *), void *workData) { // Check that the thread pool has been created if(!m_numOfThreads) return; if(m_NumOfThreadsGivenJobs >= m_numOfThreads) return; m_ThreadData[m_NumOfThreadsGivenJobs].workFunc = workFunc; m_ThreadData[m_NumOfThreadsGivenJobs].workData = workData; std::cout << "Submitted job " << m_NumOfThreadsGivenJobs << std::endl; m_NumOfThreadsGivenJobs++; } void ThreadPool::StartJobs() { // Check that the thread pool has been created // and some jobs have been assigned if(!m_numOfThreads || !m_NumOfThreadsGivenJobs) return; // Set 'haveworkToDo' flag for all threads m_WorkToDo_mutex.lock(); for(int i=0; i<m_NumOfThreadsGivenJobs; ++i) m_ThreadData[i].haveWorkToDo = true; m_NumOfThreadsDoingWork = m_NumOfThreadsGivenJobs; // Reset this counter so we can resubmit jobs later m_NumOfThreadsGivenJobs = 0; // Notify all threads they have work to do m_WorkToDo_condvar.notify_all(); m_WorkToDo_mutex.unlock(); } void ThreadPool::WaitForJobsToComplete() { // Check that a thread pool has been created if(!m_numOfThreads) return; m_WorkCompleted_mutex.lock(); while(m_NumOfThreadsDoingWork > 0) // Check if all threads have completed their work m_WorkCompleted_condvar.wait(m_WorkCompleted_mutex); // If not, wait here m_WorkCompleted_mutex.unlock(); } void ThreadPool::DestroyThreadPool() { std::cout << "Ask threads to quit\n"; m_WorkToDo_mutex.lock(); for(int i=0; i<m_numOfThreads; ++i) { m_ThreadData[i].haveWorkToDo = true; m_ThreadData[i].typeOfWork = e_quit; } m_WorkToDo_condvar.notify_all(); m_WorkToDo_mutex.unlock(); // As each thread terminates, catch them here for(int i=0; i<m_numOfThreads; ++i) { tthread::thread *t = m_ThreadList[i]; // Wait for thread to complete t->join(); } m_numOfThreads = 0; } Example of usage: (this calculates pi-squared/6) struct CalculationDataStruct { int inputVal; double outputVal; }; void LongCalculation( void *theSums ) { CalculationDataStruct *sums = (CalculationDataStruct *)theSums; int terms = sums->inputVal; double sum; for(int i=1; i<terms; i++) sum += 1.0/( double(i)*double(i) ); sums->outputVal = sum; } int main(int argc, char** argv) { int numThreads = 10; // Create pool ThreadPool threadPool; threadPool.CreateThreads(numThreads); // Create thread workspace CalculationDataStruct sums[numThreads]; // Set up jobs for(int i=0; i<numThreads; i++) { sums[i].inputVal = 3000*(i+1); threadPool.SubmitJob(LongCalculation, &sums[i]); } // Run the jobs threadPool.StartJobs(); threadPool.WaitForJobsToComplete(); // Print results for(int i=0; i<numThreads; i++) std::cout << "Sum of " << sums[i].inputVal << " terms is " << sums[i].outputVal << std::endl; return 0; } Question: In the ThreadPool::ThreadFunc method, would better performance be obtained if the following if statement if(NumOfThreadsDoingWork == 0) was included? Also, I'd be grateful of criticisms and ways to improve the code. At the same time, I hope the code is of use to others.

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• Suggestions for duplicate file finder algorithm (using C)

  - by Andrei Ciobanu

  Hello, I wanted to write a program that test if two files are duplicates (have exactly the same content). First I test if the files have the same sizes, and if they have i start to compare their contents. My first idea, was to "split" the files into fixed size blocks, then start a thread for every block, fseek to startup character of every block and continue the comparisons in parallel. When a comparison from a thread fails, the other working threads are canceled, and the program exits out of the thread spawning loop. The code looks like this: dupf.h #ifndef __NM__DUPF__H__ #define __NM__DUPF__H__ #define NUM_THREADS 15 #define BLOCK_SIZE 8192 /* Thread argument structure */ struct thread_arg_s { const char *name_f1; /* First file name */ const char *name_f2; /* Second file name */ int cursor; /* Where to seek in the file */ }; typedef struct thread_arg_s thread_arg; /** * 'arg' is of type thread_arg. * Checks if the specified file blocks are * duplicates. */ void *check_block_dup(void *arg); /** * Checks if two files are duplicates */ int check_dup(const char *name_f1, const char *name_f2); /** * Returns a valid pointer to a file. * If the file (given by the path/name 'fname') cannot be opened * in 'mode', the program is interrupted an error message is shown. **/ FILE *safe_fopen(const char *name, const char *mode); #endif dupf.c #include <errno.h> #include <pthread.h> #include <stdio.h> #include <stdlib.h> #include <string.h> #include <sys/types.h> #include <sys/stat.h> #include <unistd.h> #include "dupf.h" FILE *safe_fopen(const char *fname, const char *mode) { FILE *f = NULL; f = fopen(fname, mode); if (f == NULL) { char emsg[255]; sprintf(emsg, "FOPEN() %s\t", fname); perror(emsg); exit(-1); } return (f); } void *check_block_dup(void *arg) { const char *name_f1 = NULL, *name_f2 = NULL; /* File names */ FILE *f1 = NULL, *f2 = NULL; /* Streams */ int cursor = 0; /* Reading cursor */ char buff_f1[BLOCK_SIZE], buff_f2[BLOCK_SIZE]; /* Character buffers */ int rchars_1, rchars_2; /* Readed characters */ /* Initializing variables from 'arg' */ name_f1 = ((thread_arg*)arg)->name_f1; name_f2 = ((thread_arg*)arg)->name_f2; cursor = ((thread_arg*)arg)->cursor; /* Opening files */ f1 = safe_fopen(name_f1, "r"); f2 = safe_fopen(name_f2, "r"); /* Setup cursor in files */ fseek(f1, cursor, SEEK_SET); fseek(f2, cursor, SEEK_SET); /* Initialize buffers */ rchars_1 = fread(buff_f1, 1, BLOCK_SIZE, f1); rchars_2 = fread(buff_f2, 1, BLOCK_SIZE, f2); if (rchars_1 != rchars_2) { /* fread failed to read the same portion. * program cannot continue */ perror("ERROR WHEN READING BLOCK"); exit(-1); } while (rchars_1-->0) { if (buff_f1[rchars_1] != buff_f2[rchars_1]) { /* Different characters */ fclose(f1); fclose(f2); pthread_exit("notdup"); } } /* Close streams */ fclose(f1); fclose(f2); pthread_exit("dup"); } int check_dup(const char *name_f1, const char *name_f2) { int num_blocks = 0; /* Number of 'blocks' to check */ int num_tsp = 0; /* Number of threads spawns */ int tsp_iter = 0; /* Iterator for threads spawns */ pthread_t *tsp_threads = NULL; thread_arg *tsp_threads_args = NULL; int tsp_threads_iter = 0; int thread_c_res = 0; /* Thread creation result */ int thread_j_res = 0; /* Thread join res */ int loop_res = 0; /* Function result */ int cursor; struct stat buf_f1; struct stat buf_f2; if (name_f1 == NULL || name_f2 == NULL) { /* Invalid input parameters */ perror("INVALID FNAMES\t"); return (-1); } if (stat(name_f1, &buf_f1) != 0 || stat(name_f2, &buf_f2) != 0) { /* Stat fails */ char emsg[255]; sprintf(emsg, "STAT() ERROR: %s %s\t", name_f1, name_f2); perror(emsg); return (-1); } if (buf_f1.st_size != buf_f2.st_size) { /* File have different sizes */ return (1); } /* Files have the same size, function exec. is continued */ num_blocks = (buf_f1.st_size / BLOCK_SIZE) + 1; num_tsp = (num_blocks / NUM_THREADS) + 1; cursor = 0; for (tsp_iter = 0; tsp_iter < num_tsp; tsp_iter++) { loop_res = 0; /* Create threads array for this spawn */ tsp_threads = malloc(NUM_THREADS * sizeof(*tsp_threads)); if (tsp_threads == NULL) { perror("TSP_THREADS ALLOC FAILURE\t"); return (-1); } /* Create arguments for every thread in the current spawn */ tsp_threads_args = malloc(NUM_THREADS * sizeof(*tsp_threads_args)); if (tsp_threads_args == NULL) { perror("TSP THREADS ARGS ALLOCA FAILURE\t"); return (-1); } /* Initialize arguments and create threads */ for (tsp_threads_iter = 0; tsp_threads_iter < NUM_THREADS; tsp_threads_iter++) { if (cursor >= buf_f1.st_size) { break; } tsp_threads_args[tsp_threads_iter].name_f1 = name_f1; tsp_threads_args[tsp_threads_iter].name_f2 = name_f2; tsp_threads_args[tsp_threads_iter].cursor = cursor; thread_c_res = pthread_create( &tsp_threads[tsp_threads_iter], NULL, check_block_dup, (void*)&tsp_threads_args[tsp_threads_iter]); if (thread_c_res != 0) { perror("THREAD CREATION FAILURE"); return (-1); } cursor+=BLOCK_SIZE; } /* Join last threads and get their status */ while (tsp_threads_iter-->0) { void *thread_res = NULL; thread_j_res = pthread_join(tsp_threads[tsp_threads_iter], &thread_res); if (thread_j_res != 0) { perror("THREAD JOIN FAILURE"); return (-1); } if (strcmp((char*)thread_res, "notdup")==0) { loop_res++; /* Closing other threads and exiting by condition * from loop. */ while (tsp_threads_iter-->0) { pthread_cancel(tsp_threads[tsp_threads_iter]); } } } free(tsp_threads); free(tsp_threads_args); if (loop_res > 0) { break; } } return (loop_res > 0) ? 1 : 0; } The function works fine (at least for what I've tested). Still, some guys from #C (freenode) suggested that the solution is overly complicated, and it may perform poorly because of parallel reading on hddisk. What I want to know: Is the threaded approach flawed by default ? Is fseek() so slow ? Is there a way to somehow map the files to memory and then compare them ?

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• Emails going to Junk for Hotmail recipients

  - by David George

  We send daily mass emails to our customers (~30,000+ emails per day). We have problems with Hotmail users receiving our emails. Sometimes the email goes to the Junk folder, but often it will got to their inbox, but the content is blocked so the user sees a message saying "This email was blocked and may be dangerous". If an email is sent to GMAIL it is usually not blocked, but it does show up as from "Uknown" instead of the company. Please be advised I've done the following: 1. No RBLs Checked on - http://multirbl.valli.org/ 2. We do have SPF records published 3. We do have reverse DNS setup 4. Our company even signed up for the Junk Mail Reports Program at Hotmail Here is a sample header, I've noticed the X-SID-Result and the X-AUTH-Result both FAIL every time at Hotmail: X-Message-Delivery: Vj0xLjE7dXM9MDtsPTA7YT0wO0Q9MTtTQ0w9MQ== X-Message-Status: n:0 X-SID-Result: Fail X-AUTH-Result: FAIL X-Message-Info: JGTYoYF78jFqAaC29fBlDlD/ZI36+S6WoFmkQN10UxWFe1xLHhP+rDthGRZM87uHYM926hUBS+s0q46Yx9y6jdurhN6fx0bK Received: from privatecompany.com ([WanIPAddress]) by col0-mc3-f30.Col0.hotmail.com with Microsoft SMTPSVC(6.0.3790.4675); Wed, 5 May 2010 08:41:27 -0700 X-AuditID: ac10fe93-000013bc00000534-46-4be191a1618e Received: from INTERNAL-Email-SERVER([InternalIPAddress]) by privatecompany.com with Microsoft SMTPSVC(6.0.3790.4675); Wed, 5 May 2010 11:41:21 -0400 From: Private Company, Inc.<[email protected]> To: [email protected] Message-Id: <[email protected]> Subject: Date: Wed, 5 May 2010 11:42:46 -0400 MIME-Version: 1.0 Reply-To: [email protected] Content-Type: text/plain; charset="ISO-8859-1" Content-Transfer-Encoding: 8bit X-Brightmail-Tracker: AAAAAA== Return-Path: [email protected] X-OriginalArrivalTime: 05 May 2010 15:41:27.0837 (UTC) FILETIME=[6D06E4D0:01CAEC69]

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• Hotmail marking messages as junk

  - by Canadaka

  I was having problems with emails sent from my server being blocked completely by Hotmail, but I found out Hotmail had blocked my IP and by contacting Hotmail I had the block removed. See this question for more info: Email sent from server with rDNS & SPF being blocked by Hotmail But now all emails from my server are going directly to recipients "Junk" folder on hotmail and I can't figure out why. Hotmail says "Microsoft SmartScreen marked this message as junk and we'll delete it after ten days." I tried contacting the same people at Hotmail who had my IP block removed, but I haven't received any reply and its been almost a week. Here are some details: I have a valid SPF record for my domain "v=spf1 a include:_spf.google.com ~all" I have reverse DNS setup I have a Sender Score of 100 https://www.senderscore.org/lookup.php?lookup=66.199.162.177&ipLookup.x=55&ipLookup.y=14 I have signed up for Microsoft's SNDS and was approved. My ip says "All of the specified IPs have normal status." Microsoft added my IP to the JMRP Database My IP is not on any credible spam lists http://www.anti-abuse.org/multi-rbl-check-results/?host=66.199.162.177 my FROM header is being sent in proper format "From: CKA <[email protected]>" Here is a test email source:

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• linux thread synchronization

  - by johnnycrash

  I am new to linux and linux threads. I have spent some time googling to try to understand the differences between all the functions available for thread synchronization. I still have some questions. I have found all of these different types of synchronizations, each with a number of functions for locking, unlocking, testing the lock, etc. gcc atomic operations futexes mutexes spinlocks seqlocks rculocks conditions semaphores My current (but probably flawed) understanding is this: semaphores are process wide, involve the filesystem (virtually I assume), and are probably the slowest. Futexes might be the base locking mechanism used by mutexes, spinlocks, seqlocks, and rculocks. Futexes might be faster than the locking mechanisms that are based on them. Spinlocks dont block and thus avoid context swtiches. However they avoid the context switch at the expense of consuming all the cycles on a CPU until the lock is released (spinning). They should only should be used on multi processor systems for obvious reasons. Never sleep in a spinlock. The seq lock just tells you when you finished your work if a writer changed the data the work was based on. You have to go back and repeat the work in this case. Atomic operations are the fastest synch call, and probably are used in all the above locking mechanisms. You do not want to use atomic operations on all the fields in your shared data. You want to use a lock (mutex, futex, spin, seq, rcu) or a single atomic opertation on a lock flag when you are accessing multiple data fields. My questions go like this: Am I right so far with my assumptions? Does anyone know the cpu cycle cost of the various options? I am adding parallelism to the app so we can get better wall time response at the expense of running fewer app instances per box. Performances is the utmost consideration. I don't want to consume cpu with context switching, spinning, or lots of extra cpu cycles to read and write shared memory. I am absolutely concerned with number of cpu cycles consumed. Which (if any) of the locks prevent interruption of a thread by the scheduler or interrupt...or am I just an idiot and all synchonization mechanisms do this. What kinds of interruption are prevented? Can I block all threads or threads just on the locking thread's CPU? This question stems from my fear of interrupting a thread holding a lock for a very commonly used function. I expect that the scheduler might schedule any number of other workers who will likely run into this function and then block because it was locked. A lot of context switching would be wasted until the thread with the lock gets rescheduled and finishes. I can re-write this function to minimize lock time, but still it is so commonly called I would like to use a lock that prevents interruption...across all processors. I am writing user code...so I get software interrupts, not hardware ones...right? I should stay away from any functions (spin/seq locks) that have the word "irq" in them. Which locks are for writing kernel or driver code and which are meant for user mode? Does anyone think using an atomic operation to have multiple threads move through a linked list is nuts? I am thinking to atomicly change the current item pointer to the next item in the list. If the attempt works, then the thread can safely use the data the current item pointed to before it was moved. Other threads would now be moved along the list. futexes? Any reason to use them instead of mutexes? Is there a better way than using a condition to sleep a thread when there is no work? When using gcc atomic ops, specifically the test_and_set, can I get a performance increase by doing a non atomic test first and then using test_and_set to confirm? *I know this will be case specific, so here is the case. There is a large collection of work items, say thousands. Each work item has a flag that is initialized to 0. When a thread has exclusive access to the work item, the flag will be one. There will be lots of worker threads. Any time a thread is looking for work, they can non atomicly test for 1. If they read a 1, we know for certain that the work is unavailable. If they read a zero, they need to perform the atomic test_and_set to confirm. So if the atomic test_and_set is 500 cpu cycles because it is disabling pipelining, causes cpu's to communicate and L2 caches to flush/fill .... and a simple test is 1 cycle .... then as long as I had a better ratio of 500 to 1 when it came to stumbling upon already completed work items....this would be a win.* I hope to use mutexes or spinlocks to sparilngly protect sections of code that I want only one thread on the SYSTEM (not jsut the CPU) to access at a time. I hope to sparingly use gcc atomic ops to select work and minimize use of mutexes and spinlocks. For instance: a flag in a work item can be checked to see if a thread has worked it (0=no, 1=yes or in progress). A simple test_and_set tells the thread if it has work or needs to move on. I hope to use conditions to wake up threads when there is work. Thanks!

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• ASP.NET request queue priority

  - by dan

  I'm on IIS 7 and .NET 4.0. My understanding is that IIS takes requests and passes them off to ASP.NET worker threads. If all the threads are in use, the request goes into a queue and is processed once a thread becomes available. If the queue goes over a certain size, all new requests get a 503 until there is room in the queue again. Is there a way to prioritize the order in which queued requests are served? For example, I have consumer traffic and infrastructure traffic coming to the same server. If there are no available threads, I'd like for the consumer requests to be served first, even if they have arrived after infrastructure requests. Basically I want to replace the request queue with a priority queue. Is this possible with IIS?

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• How to use supervisord to run a PHP script as a daemon?

  - by Alasdair

  I need to have 8 threads of the same PHP script running in the background on a server continuously (as a daemon), and each script need to be automatically restarted if it exits for any reason. I've been advised to use supervisord to do this, but I don't at all understand their documentation, which seems very complicated to me. I also want each of the 8 threads of the script to be initially started at 2 minute intervals (2 minutes in between each launch) but then after this all 8 threads of the same PHP script should continue running on the server forever (and restarting if any exit for any reason). Could someone please explain how to do this with supervisord, or any other easy way of doing it? I'm on CentOS 6. Thank you!

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• Windows forms application blocks after station lock

  - by Silviu

  We're having a serious issue at work. We've discovered that after the station where the client was running is locked/unlocked the client is blocked. No repaint. So the UI thread is blocked with something. Looking at the callstack of the UI thread (thread 0) using windbg we see that a UserPreferenceChanged event gets raised. It is marshalled through a WindowsFormsSynchronizationContext using it's controlToSend field to the UI. It gets blocked by a call to the marshalling control. The method called is MarshaledInvoke it builds a ThreadMethodEntry entry = new ThreadMethodEntry(caller, method, args, synchronous, executionContext); This entry is supposed to do the magic. The call is a synchronous call and because of that (still in the MarshaledInvoke of the Control class) the wait call is reached: if (!entry.IsCompleted) { this.WaitForWaitHandle(entry.AsyncWaitHandle); } The last thing that i can see on the stack is the WaitOne called on the previously mentioned AsyncWaitHandle. This is very annoying because having just the callstack of the runtime and not one of our methods being invoked we cannot really point to a bug in our code. I might be wrong, but I'm guessing that the marshaling control is not "marshaling" to the ui thread. But another one...i don't really know which one because the other threads are being used by us and are blocked...maybe this is the issue. But none of the other threads are running a message loop. This is very annoying. We had some issues in the past with marshaling controls to the right ui thread. That is because the first form that is constructed is a splash form. Which is not the main form. We used to use the main form to marshal call to the ui thread. But from time to time some calls would go to a non ui thread and some grids would broke with a big red X on them. I fixed this by creating a specific class: public class WindowsFormsSynchronizer { private static readonly WindowsFormsSynchronizationContext = new WindowsFormsSynchronizationContext(); //Methods are following that would build the same interface of the synchronization context. } This class gets build as one of the first objects in the first form being constructed. We've noticed some other strange thing. Looking at the heap there are 7 WindowsFormsSynchronizationContext objects. 6 of these have the same instance of controlToSend, and the other one has some different instance of controlToSend. This last one is the one that should marshal the calls to the UI. I don't have any other idea...maybe some of you guys had this same issue?

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• How many VPS' can I create on my server?

  - by user197692

  I need to create as many VPS's on my dedicated server (KVM or OpenVZ) in order to sell them but I really don't know the answer. RAM calculation is simple, it's more about CPU resources, how many VPS's can hold. I'am talking about Intel i7-2600 (4 cores, 8 Threads). I need to deploy as many VPS's. It's all about the nr threads? i.e. 8 threads = maximum 8 x 1vCPU or maximum 4 x 2vCPU? I'am planning to use 1Gb and 2Gb memory on each VPS, the server has 16Gb (but I can raise RAM if need it. So, can I create 8 KVM VPS's with 4 vCPU and 2Gb ram each ? How about 20 VPS's with 1Gb ram and 4vCPU each? How is this decision affected by the hypervisor (KVM, OpenVZ, VMware)?

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• Python and .exe files, another way

  - by Sorush Rabiee

  How to build exe files (compatible with win32)? please don't refer to py2exe. that is blocked service in IRI. for Iranians only: do you know how to download something (like py2exe) from blocked sites? especially from sourceforge ande fontforge?

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• How could I get to know the resources occupied by a .NET thread?

  - by smwikipedia

  Hi guys! I am developing a multi-thread application and one of my threads is somehow blocked by something and thus it will occupy some resource such as a file forever. Is there a way to find out which thread is being blocked and more important what resouce is being held by that thread? At last and as usual, thanks for your patience and reply. :)

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• Android: Stopping method to be called twice if already running.

  - by user285831

  I'm trying to prevent my application to call the same method twice in the event of a double-click, or if the user presses different buttons quickly, almost at the same time. I have clickable Views, acting as buttons, that call the same method but passing different parameters. This is the call: startTheSearch(context, getState(), what, where); Inside this method I'm creating a new Thread, because it queries a web server for the result: new Thread(new Runnable() { public void run() { progDiag = ProgressDialog.show(ctx, null, "Searching", true); getServerXML(context, what, where, searchIsCustom, mOffset); handler.sendEmptyMessage(0); } }).start(); The problem is that upon two quick clicks, the method is fired twice, two threads are created, and consequently two new activities are created. That makes my app crash. When the methods are done, and we have the result from the server, we call the handler: private Handler handler = new Handler() { @Override public void handleMessage(Message msg) { super.handleMessage(msg); try { Intent i = new Intent(Golf.this, Result.class); Bundle b = new Bundle(); b.putString("what", mWhat); b.putString("where", mWhere); b.putInt("offset", mOffset); b.putBoolean("searchIsCustom", searchIsCustom); i.putExtras(b); startActivityForResult(i, Activity.RESULT_OK); progDiag.dismiss(); } catch (Exception e) { Alerts.generalDialogAlert("Error", "settings", ctx); } } }; I tried to have a global boolean variable called "blocked" initially set to false, creating a condition like: if(!blocked){ blocked = true; new Thread(new Runnable() { public void run() { But this only seems to work on slower phones like the G1, I tried on Nexus and before it set blocked = true, the second request has was granted. So is there any way I can block the method being called if it's already running, or if the thread has started so it wont create a new one? Please, I really need to fix this. I've been developing on Android for almost 2 months now, but I'm yet to tackle that bug. Thanks in advance.

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• Restrict whole system on certain cores except a few process?

  - by icando

  Hi I am running some latency sensitive program on a Linux machine (more specifically, CentOS 6), and I don't want the threads of the process being preempted. So in my plan, the first step is to set cpu affinity of the threads so that threads are running on separate cores, so they don't preempt each other. Then the second step is to make sure other processes in the system not running on these cores. So my question is: is it possible to restrict the whole system running on certain cores, except this process? This should apply to any newly created processes in the future.

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• How do I make time?

  - by SystemNetworks

  I wanted to output a text for a certain amount of time. One way is to use threads. Are there any other ways? I can't use threads for slick2d. This is my code when I use threads for slick: package javagame; import org.newdawn.slick.GameContainer; import org.newdawn.slick.Graphics; import org.newdawn.slick.Image; import java.util.Random; import org.newdawn.slick.Input; import org.newdawn.slick.*; import org.newdawn.slick.state.*; import org.lwjgl.input.Mouse; public class thread1 implements Runnable { String showUp; int timeLeft; public thread1(String s) { s = showUp; } public void run(Graphics g) { try { g.drawString("%s is sleeping %d", 500, 500); Thread.sleep(timeLeft); g.drawString("%s is awake", 600,600); } catch(Exception e) { } } @Override public void run() { // TODO Auto-generated method stub run(); } } It auto generates a new run() And also when I call it to my main class it has stack overflow!

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• Matrix Multiplication with C++ AMP

  - by Daniel Moth

  As part of our API tour of C++ AMP, we looked recently at parallel_for_each. I ended that post by saying we would revisit parallel_for_each after introducing array and array_view. Now is the time, so this is part 2 of parallel_for_each, and also a post that brings together everything we've seen until now. The code for serial and accelerated Consider a naïve (or brute force) serial implementation of matrix multiplication  0: void MatrixMultiplySerial(std::vector<float>& vC, const std::vector<float>& vA, const std::vector<float>& vB, int M, int N, int W) 1: { 2: for (int row = 0; row < M; row++) 3: { 4: for (int col = 0; col < N; col++) 5: { 6: float sum = 0.0f; 7: for(int i = 0; i < W; i++) 8: sum += vA[row * W + i] * vB[i * N + col]; 9: vC[row * N + col] = sum; 10: } 11: } 12: } We notice that each loop iteration is independent from each other and so can be parallelized. If in addition we have really large amounts of data, then this is a good candidate to offload to an accelerator. First, I'll just show you an example of what that code may look like with C++ AMP, and then we'll analyze it. It is assumed that you included at the top of your file #include <amp.h> 13: void MatrixMultiplySimple(std::vector<float>& vC, const std::vector<float>& vA, const std::vector<float>& vB, int M, int N, int W) 14: { 15: concurrency::array_view<const float,2> a(M, W, vA); 16: concurrency::array_view<const float,2> b(W, N, vB); 17: concurrency::array_view<concurrency::writeonly<float>,2> c(M, N, vC); 18: concurrency::parallel_for_each(c.grid, 19: [=](concurrency::index<2> idx) restrict(direct3d) { 20: int row = idx[0]; int col = idx[1]; 21: float sum = 0.0f; 22: for(int i = 0; i < W; i++) 23: sum += a(row, i) * b(i, col); 24: c[idx] = sum; 25: }); 26: } First a visual comparison, just for fun: The beginning and end is the same, i.e. lines 0,1,12 are identical to lines 13,14,26. The double nested loop (lines 2,3,4,5 and 10,11) has been transformed into a parallel_for_each call (18,19,20 and 25). The core algorithm (lines 6,7,8,9) is essentially the same (lines 21,22,23,24). We have extra lines in the C++ AMP version (15,16,17). Now let's dig in deeper. Using array_view and extent When we decided to convert this function to run on an accelerator, we knew we couldn't use the std::vector objects in the restrict(direct3d) function. So we had a choice of copying the data to the the concurrency::array<T,N> object, or wrapping the vector container (and hence its data) with a concurrency::array_view<T,N> object from amp.h – here we used the latter (lines 15,16,17). Now we can access the same data through the array_view objects (a and b) instead of the vector objects (vA and vB), and the added benefit is that we can capture the array_view objects in the lambda (lines 19-25) that we pass to the parallel_for_each call (line 18) and the data will get copied on demand for us to the accelerator. Note that line 15 (and ditto for 16 and 17) could have been written as two lines instead of one: extent<2> e(M, W); array_view<const float, 2> a(e, vA); In other words, we could have explicitly created the extent object instead of letting the array_view create it for us under the covers through the constructor overload we chose. The benefit of the extent object in this instance is that we can express that the data is indeed two dimensional, i.e a matrix. When we were using a vector object we could not do that, and instead we had to track via additional unrelated variables the dimensions of the matrix (i.e. with the integers M and W) – aren't you loving C++ AMP already? Note that the const before the float when creating a and b, will result in the underling data only being copied to the accelerator and not be copied back – a nice optimization. A similar thing is happening on line 17 when creating array_view c, where we have indicated that we do not need to copy the data to the accelerator, only copy it back. The kernel dispatch On line 18 we make the call to the C++ AMP entry point (parallel_for_each) to invoke our parallel loop or, as some may say, dispatch our kernel. The first argument we need to pass describes how many threads we want for this computation. For this algorithm we decided that we want exactly the same number of threads as the number of elements in the output matrix, i.e. in array_view c which will eventually update the vector vC. So each thread will compute exactly one result. Since the elements in c are organized in a 2-dimensional manner we can organize our threads in a two-dimensional manner too. We don't have to think too much about how to create the first argument (a grid) since the array_view object helpfully exposes that as a property. Note that instead of c.grid we could have written grid<2>(c.extent) or grid<2>(extent<2>(M, N)) – the result is the same in that we have specified M*N threads to execute our lambda. The second argument is a restrict(direct3d) lambda that accepts an index object. Since we elected to use a two-dimensional extent as the first argument of parallel_for_each, the index will also be two-dimensional and as covered in the previous posts it represents the thread ID, which in our case maps perfectly to the index of each element in the resulting array_view. The kernel itself The lambda body (lines 20-24), or as some may say, the kernel, is the code that will actually execute on the accelerator. It will be called by M*N threads and we can use those threads to index into the two input array_views (a,b) and write results into the output array_view ( c ). The four lines (21-24) are essentially identical to the four lines of the serial algorithm (6-9). The only difference is how we index into a,b,c versus how we index into vA,vB,vC. The code we wrote with C++ AMP is much nicer in its indexing, because the dimensionality is a first class concept, so you don't have to do funny arithmetic calculating the index of where the next row starts, which you have to do when working with vectors directly (since they store all the data in a flat manner). I skipped over describing line 20. Note that we didn't really need to read the two components of the index into temporary local variables. This mostly reflects my personal choice, in some algorithms to break down the index into local variables with names that make sense for the algorithm, i.e. in this case row and col. In other cases it may i,j,k or x,y,z, or M,N or whatever. Also note that we could have written line 24 as: c(idx[0], idx[1])=sum  or  c(row, col)=sum instead of the simpler c[idx]=sum Targeting a specific accelerator Imagine that we had more than one hardware accelerator on a system and we wanted to pick a specific one to execute this parallel loop on. So there would be some code like this anywhere before line 18: vector<accelerator> accs = MyFunctionThatChoosesSuitableAccelerators(); accelerator acc = accs[0]; …and then we would modify line 18 so we would be calling another overload of parallel_for_each that accepts an accelerator_view as the first argument, so it would become: concurrency::parallel_for_each(acc.default_view, c.grid, ...and the rest of your code remains the same… how simple is that? Comments about this post by Daniel Moth welcome at the original blog.

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• Creating sitemap for Googlebot - how to mark dynamic content / dynamic subpages?

  - by ojek

  I have a website that is an Internet forum. This forum has many categories, and a single category page that contains a lot of subpages with listed threads. This Internet forum is brand new, and about a week ago I filled it with a few hundred thousand threads. I then looked at my Google Webmasters Tools page to see any changes in indexing, but the index went up from 300 to about 1200, so that means it did not index my added threads (although it added something). The following is what my sitemap.xml contains, which I uploaded to their website. Of course there is a lot more code, this is just a snippet for a single category. In my real sitemap file I have all the categories listed as below: <url> <loc>http://mysite.com/Forums/Physics</loc> <changefreq>hourly</changefreq> </url> Now, I would expect Googlebot to go into mysite.com/Forums/Physics, and crawl through all the subpages with thread links, and then crawl inside of each thread and index its content. How can I achieve this? Also if this is unclear, I will add a real link to my website.

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• Uses of persistent data structures in non-functional languages

  - by Ray Toal

  Languages that are purely functional or near-purely functional benefit from persistent data structures because they are immutable and fit well with the stateless style of functional programming. But from time to time we see libraries of persistent data structures for (state-based, OOP) languages like Java. A claim often heard in favor of persistent data structures is that because they are immutable, they are thread-safe. However, the reason that persistent data structures are thread-safe is that if one thread were to "add" an element to a persistent collection, the operation returns a new collection like the original but with the element added. Other threads therefore see the original collection. The two collections share a lot of internal state, of course -- that's why these persistent structures are efficient. But since different threads see different states of data, it would seem that persistent data structures are not in themselves sufficient to handle scenarios where one thread makes a change that is visible to other threads. For this, it seems we must use devices such as atoms, references, software transactional memory, or even classic locks and synchronization mechanisms. Why then, is the immutability of PDSs touted as something beneficial for "thread safety"? Are there any real examples where PDSs help in synchronization, or solving concurrency problems? Or are PDSs simply a way to provide a stateless interface to an object in support of a functional programming style?

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• The Importance of Fully Specifying a Problem

  - by Alan

  I had a customer call this week where we were provided a forced crashdump and asked to determine why the system was hung. Normally when you are looking at a hung system, you will find a lot of threads blocked on various locks, and most likely very little actually running on the system (unless it's threads spinning on busy wait type locks). This vmcore showed none of that. In fact we were seeing hundreds of threads actively on cpu in the second before the dump was forced. This prompted the question back to the customer: What exactly were you seeing that made you believe that the system was hung? It took a few days to get a response, but the response that I got back was that they were not able to ssh into the system and when they tried to login to the console, they got the login prompt, but after typing "root" and hitting return, the console was no longer responsive. This description puts a whole new light on the "hang". You immediately start thinking "name services". Looking at the crashdump, yes the sshds are all in door calls to nscd, and nscd is idle waiting on responses from the network. Looking at the connections I see a lot of connections to the secure ldap port in CLOSE_WAIT, but more interestingly I am seeing a few connections over the non-secure ldap port to a different LDAP server just sitting open. My feeling at this point is that we have an either non-responding LDAP server, or one that is responding slowly, the resolution being to investigate that server. Moral When you log a service ticket for a "system hang", it's great to get the forced crashdump first up, but it's even better to get a description of what you observed to make to believe that the system was hung.

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• Concurrency pattern of logger in multithreaded application

  - by Dipan Mehta

  The context: We are working on a multi-threaded (Linux-C) application that follows a pipeline model. Each module has a private thread and encapsulated objects which do processing of data; and each stage has a standard form of exchanging data with next unit. The application is free from memory leak and is threadsafe using locks at the point where they exchange data. Total number of threads is about 15- and each thread can have from 1 to 4 objects. Making about 25 - 30 odd objects which all have some critical logging to do. Most discussion I have seen about different levels as in Log4J and it's other translations. The real big questions is about how the overall logging should really happen? One approach is all local logging does fprintf to stderr. The stderr is redirected to some file. This approach is very bad when logs become too big. If all object instantiate their individual loggers - (about 30-40 of them) there will be too many files. And unlike above, one won't have the idea of true order of events. Timestamping is one possibility - but it is still a mess to collate. If there is a single global logger (singleton) pattern - it indirectly blocks so many threads while one is busy putting up logs. This is unacceptable when processing of the threads are heavy. So what should be the ideal way to structure the logging objects? What are some of the best practices in actual large scale applications? I would also love to learn from some of the real designs of large scale applications to get inspirations from!

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• Creating sitemap for google bot - how to mark dynamic content / dynamic subpages?

  - by ojek

  I have a website that is internet forum. This forum has many categories, and single category page contains alot of subpages with listed threads. This internet forum is brand new, and about a week ago I filled it with few hundred thousands threads. I then looked at google webmasters page to see any changes in indexing, but the index went up from 300 to about 1200, so that means it did not index my added threads (although it added something). This is what my sitemap.xml contains which I uploaded on their website (of course there is a lot more of the code, this is just a snipped for a single category, in my real sitemap file I have all the categories listed as below): <url> <loc>http://mysite.com/Forums/Physics</loc> <changefreq>hourly</changefreq> </url> Now, I would expect google bot to go into http://mysite.com/Forums/Physics, and move through all the subpages with thread links, and then get inside of each thread and index it's content. How can I do this? Also if this will be unclear, I will add a real link to my website.

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• Marking Discussions as Answered

  As a contributor to a number of projects on CodePlex I really like the fact that the discussions feature exists but also I need ways to help me sort the discussions threads so I can make sure no-one is getting forgotten about. Seems like a lot of you agreed as the feature request Provide feature to allow Coordinators to mark Discussions threads as 'Answered' is our number 2 voted feature right now with 178 votes.  Today we rolled out the first iteration of “answer” support to discussions. In this first iteration we wanted to keep it simple and lightweight. The original poster of the thread along with project owners, developers or editors can mark any post to the thread as an answer. You can have any number of answers marked in a thread and it’s very quick to mark or unmark a post as an answer.  We deliberately keep the answers in the originally posted order so that you can see them in context with the discussion thread. When viewing discussions the default view is still to see everything, but you can easily filter by “Unanswered”.  You can even save that as a bookmark so as someone interested in the project can quickly jump to the unanswered discussion threads to go help out on. As I mention, we kept this first pass of the answering feature as simple and as lightweight as possible so that we can get some feedback on it. Head on over to the issue tracking this feature if you have any thoughts once you have used it for a bit or feel free to respond in the comments. I already have a couple of things I think we want to do such as a refresh of the look and feel of discussions in general along, make it easier to navigate to posts that are marked an answered and surface posts that you do that were marked as answered in your profile page - but if you have ideas then please let us know.

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• How can I tell if I am overusing multi-threading?

  - by exhuma

  NOTE: This is a complete re-write of the question. The text before was way too lengthy and did not get to the point! If you're interested in the original question, you can look it up in the edit history. I currently feel like I am over-using multi-threading. I have 3 types of data, A, B and C. Each A can be converted to multiple Bs and each B can be converted to multiple Cs. I am only interested in treating Cs. I could write this fairly easily with a couple of conversion functions. But I caught myself implementing it with threads, three queues (queue_a, queue_b and queue_c). There are two threads doing the different conversions, and one worker: ConverterA reads from queue_a and writes to queue_b ConverterB reads from queue_b and writes to queue_c Worker handles each element from queue_c The conversions are fairly mundane, and I don't know if this model is too convoluted. But it seems extremely robust to me. Each "converter" can start working even before data has arrived on the queues, and at any time in the code I can just "submit" new As or Bs and it will trigger the conversion pipeline which in turn will trigger a job by the worker thread. Even the resulting code looks simpler. But I still am unsure if I am abusing threads for something simple.

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• multi-thread in mmorpg server

  - by jean

  For MMORPG, there is a tick function to update every object's state in a map. The function was triggered by a timer in fixed interval. So each map's update can be dispatch to different thread. At other side, server handle player incoming package have its own threads also: I/O threads. Generally, the handler of the corresponding incoming package run in I/O threads. So there is a problem: thread synchronization. I have consider two methods: Synchronize with mutex. I/O thread lock a mutex before execute handler function and map thread lock same mutex before it execute map's update. Execute all handler functions in map's thread, I/O thread only queue the incoming handler and let map thread to pop the queue then call handler function. These two have a disadvantage: delay. For method 1, if the map's tick function is running, then all clients' request need to waiting the lock release. For method 2, if map's tick function is running, all clients' request need to waiting for next tick to be handle. Of course, there is another method: add lock to functions that use data which will be accessed both in I/O thread & map thread. But this is hard to maintain and easy to goes incorrect. It needs carefully check all variables whether or not accessed by both two kinds thread. My problem is: is there better way to do this? Notice that I said map is logic concept means no interactions can happen between two map except transport. I/O thread means thread in 3rd part network lib which used to handle client request.

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• Context switches much slower in new linux kernels

  - by Michael Goldshteyn

  We are looking to upgrade the OS on our servers from Ubuntu 10.04 LTS to Ubuntu 12.04 LTS. Unfortunately, it seems that the latency to run a thread that has become runnable has significantly increased from the 2.6 kernel to the 3.2 kernel. In fact the latency numbers we are getting are hard to believe. Let me be more specific about the test. We have a program that has two threads. The first thread gets the current time (in ticks using RDTSC) and then signals a condition variable once a second. The second thread waits on the condition variable and wakes up when it is signaled. It then gets the current time (in ticks using RDTSC). The difference between the time in the second thread and the time in the first thread is computed and displayed on the console. After this the second thread waits on the condition variable once more. So, we get a thread to thread signaling latency measurement once a second as a result. In linux 2.6.32, this latency is somewhere on the order of 2.8-3.5 us, which is reasonable. In linux 3.2.0, this latency is somewhere on the order of 40-100 us. I have excluded any differences in hardware between the two host hosts. They run on identical hardware (dual socket X5687 {Westmere-EP} processors running at 3.6 GHz with hyperthreading, speedstep and all C states turned off). We are changing the affinity to run both threads on physical cores of the same socket (i.e., the first thread is run on Core 0 and the second thread is run on Core 1), so there is no bouncing of threads on cores or bouncing/communication between sockets. The only difference between the two hosts is that one is running Ubuntu 10.04 LTS with kernel 2.6.32-28 (the fast context switch box) and the other is running the latest Ubuntu 12.04 LTS with kernel 3.2.0-23 (the slow context switch box). Have there been any changes in the kernel that could account for this ridiculous slow down in how long it takes for a thread to be scheduled to run?

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