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  • Following the Thread in OSB

    - by Antony Reynolds
    Threading in OSB The Scenario I recently led an OSB POC where we needed to get high throughput from an OSB pipeline that had the following logic: 1. Receive Request 2. Send Request to External System 3. If Response has a particular value   3.1 Modify Request   3.2 Resend Request to External System 4. Send Response back to Requestor All looks very straightforward and no nasty wrinkles along the way.  The flow was implemented in OSB as follows (see diagram for more details): Proxy Service to Receive Request and Send Response Request Pipeline   Copies Original Request for use in step 3 Route Node   Sends Request to External System exposed as a Business Service Response Pipeline   Checks Response to Check If Request Needs to Be Resubmitted Modify Request Callout to External System (same Business Service as Route Node) The Proxy and the Business Service were each assigned their own Work Manager, effectively giving each of them their own thread pool. The Surprise Imagine our surprise when, on stressing the system we saw it lock up, with large numbers of blocked threads.  The reason for the lock up is due to some subtleties in the OSB thread model which is the topic of this post.   Basic Thread Model OSB goes to great lengths to avoid holding on to threads.  Lets start by looking at how how OSB deals with a simple request/response routing to a business service in a route node. Most Business Services are implemented by OSB in two parts.  The first part uses the request thread to send the request to the target.  In the diagram this is represented by the thread T1.  After sending the request to the target (the Business Service in our diagram) the request thread is released back to whatever pool it came from.  A multiplexor (muxer) is used to wait for the response.  When the response is received the muxer hands off the response to a new thread that is used to execute the response pipeline, this is represented in the diagram by T2. OSB allows you to assign different Work Managers and hence different thread pools to each Proxy Service and Business Service.  In out example we have the “Proxy Service Work Manager” assigned to the Proxy Service and the “Business Service Work Manager” assigned to the Business Service.  Note that the Business Service Work Manager is only used to assign the thread to process the response, it is never used to process the request. This architecture means that while waiting for a response from a business service there are no threads in use, which makes for better scalability in terms of thread usage. First Wrinkle Note that if the Proxy and the Business Service both use the same Work Manager then there is potential for starvation.  For example: Request Pipeline makes a blocking callout, say to perform a database read. Business Service response tries to allocate a thread from thread pool but all threads are blocked in the database read. New requests arrive and contend with responses arriving for the available threads. Similar problems can occur if the response pipeline blocks for some reason, maybe a database update for example. Solution The solution to this is to make sure that the Proxy and Business Service use different Work Managers so that they do not contend with each other for threads. Do Nothing Route Thread Model So what happens if there is no route node?  In this case OSB just echoes the Request message as a Response message, but what happens to the threads?  OSB still uses a separate thread for the response, but in this case the Work Manager used is the Default Work Manager. So this is really a special case of the Basic Thread Model discussed above, except that the response pipeline will always execute on the Default Work Manager.   Proxy Chaining Thread Model So what happens when the route node is actually calling a Proxy Service rather than a Business Service, does the second Proxy Service use its own Thread or does it re-use the thread of the original Request Pipeline? Well as you can see from the diagram when a route node calls another proxy service then the original Work Manager is used for both request pipelines.  Similarly the response pipeline uses the Work Manager associated with the ultimate Business Service invoked via a Route Node.  This actually fits in with the earlier description I gave about Business Services and by extension Route Nodes they “… uses the request thread to send the request to the target”. Call Out Threading Model So what happens when you make a Service Callout to a Business Service from within a pipeline.  The documentation says that “The pipeline processor will block the thread until the response arrives asynchronously” when using a Service Callout.  What this means is that the target Business Service is called using the pipeline thread but the response is also handled by the pipeline thread.  This implies that the pipeline thread blocks waiting for a response.  It is the handling of this response that behaves in an unexpected way. When a Business Service is called via a Service Callout, the calling thread is suspended after sending the request, but unlike the Route Node case the thread is not released, it waits for the response.  The muxer uses the Business Service Work Manager to allocate a thread to process the response, but in this case processing the response means getting the response and notifying the blocked pipeline thread that the response is available.  The original pipeline thread can then continue to process the response. Second Wrinkle This leads to an unfortunate wrinkle.  If the Business Service is using the same Work Manager as the Pipeline then it is possible for starvation or a deadlock to occur.  The scenario is as follows: Pipeline makes a Callout and the thread is suspended but still allocated Multiple Pipeline instances using the same Work Manager are in this state (common for a system under load) Response comes back but all Work Manager threads are allocated to blocked pipelines. Response cannot be processed and so pipeline threads never unblock – deadlock! Solution The solution to this is to make sure that any Business Services used by a Callout in a pipeline use a different Work Manager to the pipeline itself. The Solution to My Problem Looking back at my original workflow we see that the same Business Service is called twice, once in a Routing Node and once in a Response Pipeline Callout.  This was what was causing my problem because the response pipeline was using the Business Service Work Manager, but the Service Callout wanted to use the same Work Manager to handle the responses and so eventually my Response Pipeline hogged all the available threads so no responses could be processed. The solution was to create a second Business Service pointing to the same location as the original Business Service, the only difference was to assign a different Work Manager to this Business Service.  This ensured that when the Service Callout completed there were always threads available to process the response because the response processing from the Service Callout had its own dedicated Work Manager. Summary Request Pipeline Executes on Proxy Work Manager (WM) Thread so limited by setting of that WM.  If no WM specified then uses WLS default WM. Route Node Request sent using Proxy WM Thread Proxy WM Thread is released before getting response Muxer is used to handle response Muxer hands off response to Business Service (BS) WM Response Pipeline Executes on Routed Business Service WM Thread so limited by setting of that WM.  If no WM specified then uses WLS default WM. No Route Node (Echo functionality) Proxy WM thread released New thread from the default WM used for response pipeline Service Callout Request sent using proxy pipeline thread Proxy thread is suspended (not released) until the response comes back Notification of response handled by BS WM thread so limited by setting of that WM.  If no WM specified then uses WLS default WM. Note this is a very short lived use of the thread After notification by callout BS WM thread that thread is released and execution continues on the original pipeline thread. Route/Callout to Proxy Service Request Pipeline of callee executes on requestor thread Response Pipeline of caller executes on response thread of requested proxy Throttling Request message may be queued if limit reached. Requesting thread is released (route node) or suspended (callout) So what this means is that you may get deadlocks caused by thread starvation if you use the same thread pool for the business service in a route node and the business service in a callout from the response pipeline because the callout will need a notification thread from the same thread pool as the response pipeline.  This was the problem we were having. You get a similar problem if you use the same work manager for the proxy request pipeline and a business service callout from that request pipeline. It also means you may want to have different work managers for the proxy and business service in the route node. Basically you need to think carefully about how threading impacts your proxy services. References Thanks to Jay Kasi, Gerald Nunn and Deb Ayers for helping to explain this to me.  Any errors are my own and not theirs.  Also thanks to my colleagues Milind Pandit and Prasad Bopardikar who travelled this road with me. OSB Thread Model Great Blog Post on Thread Usage in OSB

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  • How to synchronize cuda threads when they are in the same loop and we need to synchronize them to ex

    - by Vickey
    Hi all, I have written a code and Now I want to implement this on cuda GPU but I'm new to synchronization so please help me with this, It's little urgent to me. Below I'm presenting the code and I want to that LOOP1 to be executed by all threads (heance I want to this portion to take advantage of cuda and the remaining portion (the portion other from the LOOP1) is to be executed by only a single thread. do{ point_set = master_Q[(*num_mas) - 1].q; List* temp = point_set; List* pa = point_set; if(master_Q[num_mas[0] - 1].max) max_level = (int) (ceilf(il2 * log(master_Q[num_mas[0] - 1].max))); *num_mas = (*num_mas) - 1; while(point_set){ List* insert_ele = temp; while(temp){ insert_ele = temp; if((insert_ele->dist[insert_ele->dist_index-1] <= pow(2, max_level-1)) || (top_level == max_level)){ if(point_set == temp){ point_set = temp->next; pa = temp->next; } else{ pa->next = temp->next; } temp = NULL; List* new_point_set = point_set; float maximum_dist = 0; if(parent->p_index != insert_ele->point_index){ List* tmp = new_point_set; float *b = &(data[(insert_ele->point_index)*point_len]); **LOOP 1:** while(tmp){ float *c = &(data[(tmp->point_index)*point_len]); float sum = 0.; for(int j = 0; j < point_len; j+=2){ float d1 = b[j] - c[j]; float d2 = b[j+1] - c[j+1]; d1 *= d1; d2 *= d2; sum = sum + d1 + d2; } tmp->dist[tmp->dist_index] = sqrt(sum); if(maximum_dist < tmp->dist[tmp->dist_index]) maximum_dist = tmp->dist[tmp->dist_index]; tmp->dist_index = tmp->dist_index+1; tmp = tmp->next; } max_distance = maximum_dist; } while(new_point_set || insert_ele){ List* far, *par, *tmp, *tmp_new; far = NULL; tmp = new_point_set; tmp_new = NULL; float level_dist = pow(2, max_level-1); float maxdist = 0, maxp = 0; while(tmp){ if(tmp->dist[(tmp->dist_index)-1] > level_dist){ if(maxdist < tmp->dist[tmp->dist_index-1]) maxdist = tmp->dist[tmp->dist_index-1]; if(tmp == new_point_set){ new_point_set = tmp->next; par = tmp->next; } else{ par->next = tmp->next; } if(far == NULL){ far = tmp; tmp_new = far; } else{ tmp_new->next = tmp; tmp_new = tmp; } if(parent->p_index != insert_ele->point_index) tmp->dist_index = tmp->dist_index - 1; tmp = tmp->next; tmp_new->next = NULL; } else{ par = tmp; if(maxp < tmp->dist[(tmp->dist_index)-1]) maxp = tmp->dist[(tmp->dist_index)-1]; tmp = tmp->next; } } if(0 == maxp){ tmp = new_point_set; aloc_mem[*tree_index].p_index = insert_ele->point_index; aloc_mem[*tree_index].no_child = 0; aloc_mem[*tree_index].level = max_level--; parent->children_index[parent->no_child++] = *tree_index; parent = &(aloc_mem[*tree_index]); tree_index[0] = tree_index[0]+1; while(tmp){ aloc_mem[*tree_index].p_index = tmp->point_index; aloc_mem[(*tree_index)].no_child = 0; aloc_mem[(*tree_index)].level = master_Q[(*cur_count_Q)-1].level; parent->children_index[parent->no_child] = *tree_index; parent->no_child = parent->no_child + 1; (*tree_index)++; tmp = tmp->next; } cur_count_Q[0] = cur_count_Q[0]-1; new_point_set = NULL; } master_Q[*num_mas].q = far; master_Q[*num_mas].parent = parent; master_Q[*num_mas].valid = true; master_Q[*num_mas].max = maxdist; master_Q[*num_mas].level = max_level; num_mas[0] = num_mas[0]+1; if(0 != maxp){ aloc_mem[*tree_index].p_index = insert_ele->point_index; aloc_mem[*tree_index].no_child = 0; aloc_mem[*tree_index].level = max_level; parent->children_index[parent->no_child++] = *tree_index; parent = &(aloc_mem[*tree_index]); tree_index[0] = tree_index[0]+1; if(maxp){ int new_level = ((int) (ceilf(il2 * log(maxp)))) +1; if (new_level < (max_level-1)) max_level = new_level; else max_level--; } else max_level--; } if( 0 == maxp ) insert_ele = NULL; } } else{ if(NULL == temp->next){ master_Q[*num_mas].q = point_set; master_Q[*num_mas].parent = parent; master_Q[*num_mas].valid = true; master_Q[*num_mas].level = max_level; num_mas[0] = num_mas[0]+1; } pa = temp; temp = temp->next; } } if((*num_mas) > 1){ List *temp2 = master_Q[(*num_mas)-1].q; while(temp2){ List* temp3 = master_Q[(*num_mas)-2].q; master_Q[(*num_mas)-2].q = temp2; if((master_Q[(*num_mas)-1].parent)->p_index != (master_Q[(*num_mas)-2].parent)->p_index){ temp2->dist_index = temp2->dist_index - 1; } temp2 = temp2->next; master_Q[(*num_mas)-2].q->next = temp3; } num_mas[0] = num_mas[0]-1; } point_set = master_Q[(*num_mas)-1].q; temp = point_set; pa = point_set; parent = master_Q[(*num_mas)-1].parent; max_level = master_Q[(*num_mas)-1].level; if(master_Q[(*num_mas)-1].max) if( max_level > ((int) (ceilf(il2 * log(master_Q[(*num_mas)-1].max)))) +1) max_level = ((int) (ceilf(il2 * log(master_Q[(*num_mas)-1].max)))) +1; num_mas[0] = num_mas[0]-1; } }while(*num_mas > 0);

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  • SQL IO and SAN troubles

    - by James
    We are running two servers with identical software setup but different hardware. The first one is a VM on VMWare on a normal tower server with dual core xeons, 16 GB RAM and a 7200 RPM drive. The second one is a VM on XenServer on a powerful brand new rack server, with 4 core xeons and shared storage. We are running Dynamics AX 2012 and SQL Server 2008 R2. When I insert 15 000 records into a table on the slow tower server (as a test), it does so in 13 seconds. On the fast server it takes 33 seconds. I re-ran these tests several times with the same results. I have a feeling it is some sort of IO bottleneck, so I ran SQLIO on both. Here are the results for the slow tower server: C:\Program Files (x86)\SQLIO>test.bat C:\Program Files (x86)\SQLIO>sqlio -kW -t8 -s120 -o8 -frandom -b8 -BH -LS C:\Tes tFile.dat sqlio v1.5.SG using system counter for latency timings, 14318180 counts per second 8 threads writing for 120 secs to file C:\TestFile.dat using 8KB random IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) using current size: 5120 MB for file: C:\TestFile.dat initialization done CUMULATIVE DATA: throughput metrics: IOs/sec: 226.97 MBs/sec: 1.77 latency metrics: Min_Latency(ms): 0 Avg_Latency(ms): 281 Max_Latency(ms): 467 histogram: ms: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24+ %: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 99 C:\Program Files (x86)\SQLIO>sqlio -kR -t8 -s120 -o8 -frandom -b8 -BH -LS C:\Tes tFile.dat sqlio v1.5.SG using system counter for latency timings, 14318180 counts per second 8 threads reading for 120 secs from file C:\TestFile.dat using 8KB random IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) using current size: 5120 MB for file: C:\TestFile.dat initialization done CUMULATIVE DATA: throughput metrics: IOs/sec: 91.34 MBs/sec: 0.71 latency metrics: Min_Latency(ms): 14 Avg_Latency(ms): 699 Max_Latency(ms): 1124 histogram: ms: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24+ %: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 C:\Program Files (x86)\SQLIO>sqlio -kW -t8 -s120 -o8 -fsequential -b64 -BH -LS C :\TestFile.dat sqlio v1.5.SG using system counter for latency timings, 14318180 counts per second 8 threads writing for 120 secs to file C:\TestFile.dat using 64KB sequential IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) using current size: 5120 MB for file: C:\TestFile.dat initialization done CUMULATIVE DATA: throughput metrics: IOs/sec: 1094.50 MBs/sec: 68.40 latency metrics: Min_Latency(ms): 0 Avg_Latency(ms): 58 Max_Latency(ms): 467 histogram: ms: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24+ %: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 C:\Program Files (x86)\SQLIO>sqlio -kR -t8 -s120 -o8 -fsequential -b64 -BH -LS C :\TestFile.dat sqlio v1.5.SG using system counter for latency timings, 14318180 counts per second 8 threads reading for 120 secs from file C:\TestFile.dat using 64KB sequential IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) using current size: 5120 MB for file: C:\TestFile.dat initialization done CUMULATIVE DATA: throughput metrics: IOs/sec: 1155.31 MBs/sec: 72.20 latency metrics: Min_Latency(ms): 17 Avg_Latency(ms): 55 Max_Latency(ms): 205 histogram: ms: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24+ %: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 Here are the results of the fast rack server: C:\Program Files (x86)\SQLIO>test.bat C:\Program Files (x86)\SQLIO>sqlio -kW -t8 -s120 -o8 -frandom -b8 -BH -LS E:\Tes tFile.dat sqlio v1.5.SG using system counter for latency timings, 62500000 counts per second 8 threads writing for 120 secs to file E:\TestFile.dat using 8KB random IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) open_file: CreateFile (E:\TestFile.dat for write): The system cannot find the pa th specified. exiting C:\Program Files (x86)\SQLIO>sqlio -kR -t8 -s120 -o8 -frandom -b8 -BH -LS E:\Tes tFile.dat sqlio v1.5.SG using system counter for latency timings, 62500000 counts per second 8 threads reading for 120 secs from file E:\TestFile.dat using 8KB random IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) open_file: CreateFile (E:\TestFile.dat for read): The system cannot find the pat h specified. exiting C:\Program Files (x86)\SQLIO>sqlio -kW -t8 -s120 -o8 -fsequential -b64 -BH -LS E :\TestFile.dat sqlio v1.5.SG using system counter for latency timings, 62500000 counts per second 8 threads writing for 120 secs to file E:\TestFile.dat using 64KB sequential IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) open_file: CreateFile (E:\TestFile.dat for write): The system cannot find the pa th specified. exiting C:\Program Files (x86)\SQLIO>sqlio -kR -t8 -s120 -o8 -fsequential -b64 -BH -LS E :\TestFile.dat sqlio v1.5.SG using system counter for latency timings, 62500000 counts per second 8 threads reading for 120 secs from file E:\TestFile.dat using 64KB sequential IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) open_file: CreateFile (E:\TestFile.dat for read): The system cannot find the pat h specified. exiting C:\Program Files (x86)\SQLIO>test.bat C:\Program Files (x86)\SQLIO>sqlio -kW -t8 -s120 -o8 -frandom -b8 -BH -LS c:\Tes tFile.dat sqlio v1.5.SG using system counter for latency timings, 62500000 counts per second 8 threads writing for 120 secs to file c:\TestFile.dat using 8KB random IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) using current size: 5120 MB for file: c:\TestFile.dat initialization done CUMULATIVE DATA: throughput metrics: IOs/sec: 2575.77 MBs/sec: 20.12 latency metrics: Min_Latency(ms): 1 Avg_Latency(ms): 24 Max_Latency(ms): 655 histogram: ms: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24+ %: 0 0 0 5 8 9 9 9 8 5 3 1 1 1 1 0 0 0 0 0 0 0 0 0 37 C:\Program Files (x86)\SQLIO>sqlio -kR -t8 -s120 -o8 -frandom -b8 -BH -LS c:\Tes tFile.dat sqlio v1.5.SG using system counter for latency timings, 62500000 counts per second 8 threads reading for 120 secs from file c:\TestFile.dat using 8KB random IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) using current size: 5120 MB for file: c:\TestFile.dat initialization done CUMULATIVE DATA: throughput metrics: IOs/sec: 1141.39 MBs/sec: 8.91 latency metrics: Min_Latency(ms): 1 Avg_Latency(ms): 55 Max_Latency(ms): 652 histogram: ms: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24+ %: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 91 C:\Program Files (x86)\SQLIO>sqlio -kW -t8 -s120 -o8 -fsequential -b64 -BH -LS c :\TestFile.dat sqlio v1.5.SG using system counter for latency timings, 62500000 counts per second 8 threads writing for 120 secs to file c:\TestFile.dat using 64KB sequential IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) using current size: 5120 MB for file: c:\TestFile.dat initialization done CUMULATIVE DATA: throughput metrics: IOs/sec: 341.37 MBs/sec: 21.33 latency metrics: Min_Latency(ms): 5 Avg_Latency(ms): 186 Max_Latency(ms): 120037 histogram: ms: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24+ %: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 C:\Program Files (x86)\SQLIO>sqlio -kR -t8 -s120 -o8 -fsequential -b64 -BH -LS c :\TestFile.dat sqlio v1.5.SG using system counter for latency timings, 62500000 counts per second 8 threads reading for 120 secs from file c:\TestFile.dat using 64KB sequential IOs enabling multiple I/Os per thread with 8 outstanding buffering set to use hardware disk cache (but not file cache) using current size: 5120 MB for file: c:\TestFile.dat initialization done CUMULATIVE DATA: throughput metrics: IOs/sec: 1024.07 MBs/sec: 64.00 latency metrics: Min_Latency(ms): 5 Avg_Latency(ms): 61 Max_Latency(ms): 81632 histogram: ms: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24+ %: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 Three of the four tests are, to my mind, within reasonable parameters for the rack server. However, the 64 write test is incredibly slow on the rack server. (68 mb/sec on the slow tower vs 21 mb/s on the rack). The read speed for 64k also seems slow. Is this enough to say there is some sort of bottleneck with the shared storage? I need to know if I can take this evidence and say we need to launch an investigation into this. Any help is appreciated.

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  • Using Parallel Extensions with ThreadStatic attribute. Could it leak memory?

    - by the-locster
    I'm using Parallel Extensions fairly heavily and I've just now encountered a case where using thread locla storrage might be sensible to allow re-use of objects by worker threads. As such I was lookign at the ThreadStatic attribute which marks a static field/variable as having a unique value per thread. It seems to me that it would be unwise to use PE with the ThreadStatic attribute without any guarantee of thread re-use by PE. That is, if threads are created and destroyed to some degree would the variables (and thus objects they point to) remain in thread local storage for some indeterminate amount of time, thus causing a memory leak? Or perhaps the thread storage is tied to the threads and disposed of when the threads are disposed? But then you still potentially have threads in a pool that are longed lived and that accumulate thread local storage from various pieces of code the threads are used for. Is there a better approach to obtaining thread local storage with PE? Thankyou.

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  • Can Ruby Fibers be Concurrent?

    - by Jesse J
    I'm trying to get some speed up in my program and I've been told that Ruby Fibers are faster than threads and can take advantage of multiple cores. I've looked around, but I just can't find how to actually run different fibers concurrently. With threads you can dO this: threads = [] threads << Thread.new {Do something} threads << Thread.new {Do something} threads.each {|thread| thread.join} I can't see how to do something like this with fibers. All I can find is yield and resume which seems like just a bunch of starting and stopping between the fibers. Is there a way to do true concurrency with fibers?

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  • Is DataRow thread safe? How to update a single datarow in a datatable using multiple threads? - .net

    - by NLV
    Hello all I want to update a single datarow in a datatable using multiple threads. Is this actually possible? I've written the following code implementing a simple multi-threading to update a single datarow. I get different results each time. Why is it so? public partial class Form1 : Form { private static DataTable dtMain; private static string threadMsg = string.Empty; public Form1() { InitializeComponent(); } private void Form1_Load(object sender, EventArgs e) { Thread[] thArr = new Thread[5]; dtMain = new DataTable(); dtMain.Columns.Add("SNo"); DataRow dRow; dRow = dtMain.NewRow(); dRow["SNo"] = 5; dtMain.Rows.Add(dRow); dtMain.AcceptChanges(); ThreadStart ts = new ThreadStart(delegate { dtUpdate(); }); thArr[0] = new Thread(ts); thArr[1] = new Thread(ts); thArr[2] = new Thread(ts); thArr[3] = new Thread(ts); thArr[4] = new Thread(ts); thArr[0].Start(); thArr[1].Start(); thArr[2].Start(); thArr[3].Start(); thArr[4].Start(); while (!WaitTillAllThreadsStopped(thArr)) { Thread.Sleep(500); } foreach (Thread thread in thArr) { if (thread != null && thread.IsAlive) { thread.Abort(); } } dgvMain.DataSource = dtMain; } private void dtUpdate() { for (int i = 0; i < 1000; i++) { try { dtMain.Rows[0][0] = Convert.ToInt32(dtMain.Rows[0][0]) + 1; dtMain.AcceptChanges(); } catch { continue; } } } private bool WaitTillAllThreadsStopped(Thread[] threads) { foreach (Thread thread in threads) { if (thread != null && thread.ThreadState == ThreadState.Running) { return false; } } return true; } } Any thoughts on this? Thank you NLV

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  • SQL SERVER – Find Max Worker Count using DMV – 32 Bit and 64 Bit

    - by pinaldave
    During several recent training courses, I found it very interesting that Worker Thread is not quite known to everyone despite the fact that it is a very important feature. At some point in the discussion, one of the attendees mentioned that we can double the Worker Thread if we double the CPU (add the same number of CPU that we have on current system). The same discussion has triggered this quick article. Here is the DMV which can be used to find out Max Worker Count SELECT max_workers_count FROM sys.dm_os_sys_info Let us run the above query on my system and find the results. As my system is 32 bit and I have two CPU, the Max Worker Count is displayed as 512. To address the previous discussion, adding more CPU does not necessarily double the Worker Count. In fact, the logic behind this simple principle is as follows: For x86 (32-bit) upto 4 logical processors  max worker threads = 256 For x86 (32-bit) more than 4 logical processors  max worker threads = 256 + ((# Procs – 4) * 8) For x64 (64-bit) upto 4 logical processors  max worker threads = 512 For x64 (64-bit) more than 4 logical processors  max worker threads = 512+ ((# Procs – 4) * 8) In addition to this, you can configure the Max Worker Thread by using SSMS. Go to Server Node >> Right Click and Select Property >> Select Process and modify setting under Worker Threads. According to Book On Line, the default Worker Thread settings are appropriate for most of the systems. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Server, SQL System Table, SQL Tips and Tricks, T SQL, Technology Tagged: SQL DMV

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  • Why the “Toilet” Analogy for SQL might be bad

    - by Jonathan Kehayias
    Robert Davis(blog/twitter) recently blogged The Toilet Analogy … or Why I Never Recommend Increasing Worker Threads , in which he uses an analogy for why increasing the value for the ‘max worker threads’ sp_configure option can be bad inside of SQL Server.  While I can’t make an argument against Robert’s assertion that increasing worker threads may not improve performance, I can make an argument against his suggestion that, simply increasing the number of logical processors, for example from...(read more)

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  • Is there a way to ‘join’ (block) in POSIX threads, without exiting the joinee?

    - by elliottcable
    I’m buried in multithreading / parallelism documents, trying to figure out how to implement a threading implementation in a programming language I’ve been designing. I’m trying to map a mental model to the pthreads.h library, but I’m having trouble with one thing: I need my interpreter instances to continue to exist after they complete interpretation of a routine (the language’s closure/function data type), because I want to later assign other routines to them for interpretation, thus saving me the thread and interpreter setup/teardown time. This would be fine, except that pthread_join(3) requires that I call pthread_exit(3) to ‘unblock’ the original thread. How can I block the original thread (when it needs the result of executing the routine), and then unblock it when interpretation of the child routine is complete?

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  • Inside the Concurrent Collections: ConcurrentDictionary

    - by Simon Cooper
    Using locks to implement a thread-safe collection is rather like using a sledgehammer - unsubtle, easy to understand, and tends to make any other tool redundant. Unlike the previous two collections I looked at, ConcurrentStack and ConcurrentQueue, ConcurrentDictionary uses locks quite heavily. However, it is careful to wield locks only where necessary to ensure that concurrency is maximised. This will, by necessity, be a higher-level look than my other posts in this series, as there is quite a lot of code and logic in ConcurrentDictionary. Therefore, I do recommend that you have ConcurrentDictionary open in a decompiler to have a look at all the details that I skip over. The problem with locks There's several things to bear in mind when using locks, as encapsulated by the lock keyword in C# and the System.Threading.Monitor class in .NET (if you're unsure as to what lock does in C#, I briefly covered it in my first post in the series): Locks block threads The most obvious problem is that threads waiting on a lock can't do any work at all. No preparatory work, no 'optimistic' work like in ConcurrentQueue and ConcurrentStack, nothing. It sits there, waiting to be unblocked. This is bad if you're trying to maximise concurrency. Locks are slow Whereas most of the methods on the Interlocked class can be compiled down to a single CPU instruction, ensuring atomicity at the hardware level, taking out a lock requires some heavy lifting by the CLR and the operating system. There's quite a bit of work required to take out a lock, block other threads, and wake them up again. If locks are used heavily, this impacts performance. Deadlocks When using locks there's always the possibility of a deadlock - two threads, each holding a lock, each trying to aquire the other's lock. Fortunately, this can be avoided with careful programming and structured lock-taking, as we'll see. So, it's important to minimise where locks are used to maximise the concurrency and performance of the collection. Implementation As you might expect, ConcurrentDictionary is similar in basic implementation to the non-concurrent Dictionary, which I studied in a previous post. I'll be using some concepts introduced there, so I recommend you have a quick read of it. So, if you were implementing a thread-safe dictionary, what would you do? The naive implementation is to simply have a single lock around all methods accessing the dictionary. This would work, but doesn't allow much concurrency. Fortunately, the bucketing used by Dictionary allows a simple but effective improvement to this - one lock per bucket. This allows different threads modifying different buckets to do so in parallel. Any thread making changes to the contents of a bucket takes the lock for that bucket, ensuring those changes are thread-safe. The method that maps each bucket to a lock is the GetBucketAndLockNo method: private void GetBucketAndLockNo( int hashcode, out int bucketNo, out int lockNo, int bucketCount) { // the bucket number is the hashcode (without the initial sign bit) // modulo the number of buckets bucketNo = (hashcode & 0x7fffffff) % bucketCount; // and the lock number is the bucket number modulo the number of locks lockNo = bucketNo % m_locks.Length; } However, this does require some changes to how the buckets are implemented. The 'implicit' linked list within a single backing array used by the non-concurrent Dictionary adds a dependency between separate buckets, as every bucket uses the same backing array. Instead, ConcurrentDictionary uses a strict linked list on each bucket: This ensures that each bucket is entirely separate from all other buckets; adding or removing an item from a bucket is independent to any changes to other buckets. Modifying the dictionary All the operations on the dictionary follow the same basic pattern: void AlterBucket(TKey key, ...) { int bucketNo, lockNo; 1: GetBucketAndLockNo( key.GetHashCode(), out bucketNo, out lockNo, m_buckets.Length); 2: lock (m_locks[lockNo]) { 3: Node headNode = m_buckets[bucketNo]; 4: Mutate the node linked list as appropriate } } For example, when adding another entry to the dictionary, you would iterate through the linked list to check whether the key exists already, and add the new entry as the head node. When removing items, you would find the entry to remove (if it exists), and remove the node from the linked list. Adding, updating, and removing items all follow this pattern. Performance issues There is a problem we have to address at this point. If the number of buckets in the dictionary is fixed in the constructor, then the performance will degrade from O(1) to O(n) when a large number of items are added to the dictionary. As more and more items get added to the linked lists in each bucket, the lookup operations will spend most of their time traversing a linear linked list. To fix this, the buckets array has to be resized once the number of items in each bucket has gone over a certain limit. (In ConcurrentDictionary this limit is when the size of the largest bucket is greater than the number of buckets for each lock. This check is done at the end of the TryAddInternal method.) Resizing the bucket array and re-hashing everything affects every bucket in the collection. Therefore, this operation needs to take out every lock in the collection. Taking out mutiple locks at once inevitably summons the spectre of the deadlock; two threads each hold a lock, and each trying to acquire the other lock. How can we eliminate this? Simple - ensure that threads never try to 'swap' locks in this fashion. When taking out multiple locks, always take them out in the same order, and always take out all the locks you need before starting to release them. In ConcurrentDictionary, this is controlled by the AcquireLocks, AcquireAllLocks and ReleaseLocks methods. Locks are always taken out and released in the order they are in the m_locks array, and locks are all released right at the end of the method in a finally block. At this point, it's worth pointing out that the locks array is never re-assigned, even when the buckets array is increased in size. The number of locks is fixed in the constructor by the concurrencyLevel parameter. This simplifies programming the locks; you don't have to check if the locks array has changed or been re-assigned before taking out a lock object. And you can be sure that when a thread takes out a lock, another thread isn't going to re-assign the lock array. This would create a new series of lock objects, thus allowing another thread to ignore the existing locks (and any threads controlling them), breaking thread-safety. Consequences of growing the array Just because we're using locks doesn't mean that race conditions aren't a problem. We can see this by looking at the GrowTable method. The operation of this method can be boiled down to: private void GrowTable(Node[] buckets) { try { 1: Acquire first lock in the locks array // this causes any other thread trying to take out // all the locks to block because the first lock in the array // is always the one taken out first // check if another thread has already resized the buckets array // while we were waiting to acquire the first lock 2: if (buckets != m_buckets) return; 3: Calculate the new size of the backing array 4: Node[] array = new array[size]; 5: Acquire all the remaining locks 6: Re-hash the contents of the existing buckets into array 7: m_buckets = array; } finally { 8: Release all locks } } As you can see, there's already a check for a race condition at step 2, for the case when the GrowTable method is called twice in quick succession on two separate threads. One will successfully resize the buckets array (blocking the second in the meantime), when the second thread is unblocked it'll see that the array has already been resized & exit without doing anything. There is another case we need to consider; looking back at the AlterBucket method above, consider the following situation: Thread 1 calls AlterBucket; step 1 is executed to get the bucket and lock numbers. Thread 2 calls GrowTable and executes steps 1-5; thread 1 is blocked when it tries to take out the lock in step 2. Thread 2 re-hashes everything, re-assigns the buckets array, and releases all the locks (steps 6-8). Thread 1 is unblocked and continues executing, but the calculated bucket and lock numbers are no longer valid. Between calculating the correct bucket and lock number and taking out the lock, another thread has changed where everything is. Not exactly thread-safe. Well, a similar problem was solved in ConcurrentStack and ConcurrentQueue by storing a local copy of the state, doing the necessary calculations, then checking if that state is still valid. We can use a similar idea here: void AlterBucket(TKey key, ...) { while (true) { Node[] buckets = m_buckets; int bucketNo, lockNo; GetBucketAndLockNo( key.GetHashCode(), out bucketNo, out lockNo, buckets.Length); lock (m_locks[lockNo]) { // if the state has changed, go back to the start if (buckets != m_buckets) continue; Node headNode = m_buckets[bucketNo]; Mutate the node linked list as appropriate } break; } } TryGetValue and GetEnumerator And so, finally, we get onto TryGetValue and GetEnumerator. I've left these to the end because, well, they don't actually use any locks. How can this be? Whenever you change a bucket, you need to take out the corresponding lock, yes? Indeed you do. However, it is important to note that TryGetValue and GetEnumerator don't actually change anything. Just as immutable objects are, by definition, thread-safe, read-only operations don't need to take out a lock because they don't change anything. All lockless methods can happily iterate through the buckets and linked lists without worrying about locking anything. However, this does put restrictions on how the other methods operate. Because there could be another thread in the middle of reading the dictionary at any time (even if a lock is taken out), the dictionary has to be in a valid state at all times. Every change to state has to be made visible to other threads in a single atomic operation (all relevant variables are marked volatile to help with this). This restriction ensures that whatever the reading threads are doing, they never read the dictionary in an invalid state (eg items that should be in the collection temporarily removed from the linked list, or reading a node that has had it's key & value removed before the node itself has been removed from the linked list). Fortunately, all the operations needed to change the dictionary can be done in that way. Bucket resizes are made visible when the new array is assigned back to the m_buckets variable. Any additions or modifications to a node are done by creating a new node, then splicing it into the existing list using a single variable assignment. Node removals are simply done by re-assigning the node's m_next pointer. Because the dictionary can be changed by another thread during execution of the lockless methods, the GetEnumerator method is liable to return dirty reads - changes made to the dictionary after GetEnumerator was called, but before the enumeration got to that point in the dictionary. It's worth listing at this point which methods are lockless, and which take out all the locks in the dictionary to ensure they get a consistent view of the dictionary: Lockless: TryGetValue GetEnumerator The indexer getter ContainsKey Takes out every lock (lockfull?): Count IsEmpty Keys Values CopyTo ToArray Concurrent principles That covers the overall implementation of ConcurrentDictionary. I haven't even begun to scratch the surface of this sophisticated collection. That I leave to you. However, we've looked at enough to be able to extract some useful principles for concurrent programming: Partitioning When using locks, the work is partitioned into independant chunks, each with its own lock. Each partition can then be modified concurrently to other partitions. Ordered lock-taking When a method does need to control the entire collection, locks are taken and released in a fixed order to prevent deadlocks. Lockless reads Read operations that don't care about dirty reads don't take out any lock; the rest of the collection is implemented so that any reading thread always has a consistent view of the collection. That leads us to the final collection in this little series - ConcurrentBag. Lacking a non-concurrent analogy, it is quite different to any other collection in the class libraries. Prepare your thinking hats!

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  • Improve your Application Performance with .NET Framework 4.0

    Nice Article on CodeGuru. This processors we use today are quite different from those of just a few years ago, as most processors today provide multiple cores and/or multiple threads. With multiple cores and/or threads we need to change how we tackle problems in code. Yes we can still continue to write code to perform an action in a top down fashion to complete a task. This apprach will continue to work; however, you are not taking advantage of the extra processing power available. The best way to take advantage of the extra cores prior to .NET Framework 4.0 was to create threads and/or utilize the ThreadPool. For many developers utilizing Threads or the ThreadPool can be a little daunting. The .NET 4.0 Framework drastically simplified the process of utilizing the extra processing power through the Task Parallel Library (TPL). This article talks following topics “Data Parallelism”, “Parallel LINQ (PLINQ)” and “Task Parallelism”. span.fullpost {display:none;}

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  • Improve your Application Performance with .NET Framework 4.0

    Nice Article on CodeGuru. This processors we use today are quite different from those of just a few years ago, as most processors today provide multiple cores and/or multiple threads. With multiple cores and/or threads we need to change how we tackle problems in code. Yes we can still continue to write code to perform an action in a top down fashion to complete a task. This apprach will continue to work; however, you are not taking advantage of the extra processing power available. The best way to take advantage of the extra cores prior to .NET Framework 4.0 was to create threads and/or utilize the ThreadPool. For many developers utilizing Threads or the ThreadPool can be a little daunting. The .NET 4.0 Framework drastically simplified the process of utilizing the extra processing power through the Task Parallel Library (TPL). This article talks following topics “Data Parallelism”, “Parallel LINQ (PLINQ)” and “Task Parallelism”. span.fullpost {display:none;}

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  • Getting Current Native Thread

    - by Ricardo Peres
    The native OS threads running in the current process are exposed through the Threads property of the Process class. Please note that this is not the same as a managed thread, these are the actual native threads running on the operating system. In order to get a pointer to the current executing thread, we must use P/Invoke. Here's how we do it: [DllImport("kernel32.dll")] public static extern UInt32 GetCurrentThreadId(); UInt32 id = GetCurrentThreadId(); ProcessThread thread = Process.GetCurrentProcess().Threads.Cast().Where(t = t.Id == id).Single(); SyntaxHighlighter.config.clipboardSwf = 'http://alexgorbatchev.com/pub/sh/2.0.320/scripts/clipboard.swf'; SyntaxHighlighter.brushes.CSharp.aliases = ['c#', 'c-sharp', 'csharp']; SyntaxHighlighter.all();

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  • Can I get the stack traces of all threads in my c# app?

    - by Drew Shafer
    I'm debugging an apparent concurrency issue in a largish app that I hack on at work. The bug in question only manifests on certain lower-performance machines after running for many (12+) hours, and I have never reproduced it in the debugger. Because of this, my debugging tools are basically limited to analyzing log files. C# makes it easy to get the stack trace of the thread throwing the exception, but I'd like to additionally get the stack traces of every other thread currently executing in my AppDomain at the time the exception was thrown. Is this possible?

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  • Is true multithreading really necessary?

    - by Jonathan Graef
    So yeah, I'm creating a programming language. And the language allows multiple threads. But, all threads are synchronized with a global interpreter lock, which means only one thread is allowed to execute at a time. The only way to get the threads to switch off is to explicitly tell the current thread to wait, which allows another thread to execute. Parallel processing is of course possible by spawning multiple processes, but the variables and objects in one process cannot be accessed from another. However the language does have a fairly efficient IPC interface for communicating between processes. My question is: Would there ever be a reason to have multiple, unsynchronized threads within a single process (thus circumventing the GIL)? Why not just put thread.wait() statements in key positions in the program logic (presuming thread.wait() isn't a CPU hog, of course)? I understand that certain other languages that use a GIL have processor scheduling issues (cough Python), but they have all been resolved.

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  • Can threads safely read variables set by VCL events?

    - by Tom1952
    Is it safe for a thread to READ a variable set by a Delphi VCL event? When a user clicks on a VCL TCheckbox, the main thread sets a boolean to the checkbox's Checked state. CheckboxState := CheckBox1.Checked; At any time, a thread reads that variable if CheckBoxState then ... It doesn't matter if the thread "misses" a change to the boolean, because the thread checks the variable in a loop as it does other things. So it will see the state change eventually... Is this safe? Or do I need special code? Is surrounding the read and write of the variable (in the thread and main thread respectively) with critical code calls necessary and sufficient? As I said, it doesn't matter if the thread gets the "wrong" value, but I keep thinking that there might be a low-level problem if one thread tries to read a variable while the main thread is in the middle of writing it, or vice versa. My question is similar to this one: http://stackoverflow.com/questions/1353096/cross-thread-reading-of-a-variable. (Also related to my previous question: http://stackoverflow.com/questions/2449183/using-entercriticalsection-in-thread-to-update-vcl-label)

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  • Performance triage

    - by Dave
    Folks often ask me how to approach a suspected performance issue. My personal strategy is informed by the fact that I work on concurrency issues. (When you have a hammer everything looks like a nail, but I'll try to keep this general). A good starting point is to ask yourself if the observed performance matches your expectations. Expectations might be derived from known system performance limits, prototypes, and other software or environments that are comparable to your particular system-under-test. Some simple comparisons and microbenchmarks can be useful at this stage. It's also useful to write some very simple programs to validate some of the reported or expected system limits. Can that disk controller really tolerate and sustain 500 reads per second? To reduce the number of confounding factors it's better to try to answer that question with a very simple targeted program. And finally, nothing beats having familiarity with the technologies that underlying your particular layer. On the topic of confounding factors, as our technology stacks become deeper and less transparent, we often find our own technology working against us in some unexpected way to choke performance rather than simply running into some fundamental system limit. A good example is the warm-up time needed by just-in-time compilers in Java Virtual Machines. I won't delve too far into that particular hole except to say that it's rare to find good benchmarks and methodology for java code. Another example is power management on x86. Power management is great, but it can take a while for the CPUs to throttle up from low(er) frequencies to full throttle. And while I love "turbo" mode, it makes benchmarking applications with multiple threads a chore as you have to remember to turn it off and then back on otherwise short single-threaded runs may look abnormally fast compared to runs with higher thread counts. In general for performance characterization I disable turbo mode and fix the power governor at "performance" state. Another source of complexity is the scheduler, which I've discussed in prior blog entries. Lets say I have a running application and I want to better understand its behavior and performance. We'll presume it's warmed up, is under load, and is an execution mode representative of what we think the norm would be. It should be in steady-state, if a steady-state mode even exists. On Solaris the very first thing I'll do is take a set of "pstack" samples. Pstack briefly stops the process and walks each of the stacks, reporting symbolic information (if available) for each frame. For Java, pstack has been augmented to understand java frames, and even report inlining. A few pstack samples can provide powerful insight into what's actually going on inside the program. You'll be able to see calling patterns, which threads are blocked on what system calls or synchronization constructs, memory allocation, etc. If your code is CPU-bound then you'll get a good sense where the cycles are being spent. (I should caution that normal C/C++ inlining can diffuse an otherwise "hot" method into other methods. This is a rare instance where pstack sampling might not immediately point to the key problem). At this point you'll need to reconcile what you're seeing with pstack and your mental model of what you think the program should be doing. They're often rather different. And generally if there's a key performance issue, you'll spot it with a moderate number of samples. I'll also use OS-level observability tools to lock for the existence of bottlenecks where threads contend for locks; other situations where threads are blocked; and the distribution of threads over the system. On Solaris some good tools are mpstat and too a lesser degree, vmstat. Try running "mpstat -a 5" in one window while the application program runs concurrently. One key measure is the voluntary context switch rate "vctx" or "csw" which reflects threads descheduling themselves. It's also good to look at the user; system; and idle CPU percentages. This can give a broad but useful understanding if your threads are mostly parked or mostly running. For instance if your program makes heavy use of malloc/free, then it might be the case you're contending on the central malloc lock in the default allocator. In that case you'd see malloc calling lock in the stack traces, observe a high csw/vctx rate as threads block for the malloc lock, and your "usr" time would be less than expected. Solaris dtrace is a wonderful and invaluable performance tool as well, but in a sense you have to frame and articulate a meaningful and specific question to get a useful answer, so I tend not to use it for first-order screening of problems. It's also most effective for OS and software-level performance issues as opposed to HW-level issues. For that reason I recommend mpstat & pstack as my the 1st step in performance triage. If some other OS-level issue is evident then it's good to switch to dtrace to drill more deeply into the problem. Only after I've ruled out OS-level issues do I switch to using hardware performance counters to look for architectural impediments.

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  • Problem in suspending 2 threads at the same time in MFC!

    - by kiddo
    I am learning about threading and multithreading..so i just created a small application in which i will update the progressbar and a static text using threading.I vl get two inputs from the user, start and end values for how long the loop should rotate.I have 2threads in my application. Thread1- to update the progressbar(according to the loop) the static text which will show the count(loop count). Thread2 - to update the another static text which will just diplay a name Basically if the user clicks start, the progressbar steps up and at the same time filecount and the name are displayed parallely. There's is another operation where if the user clicks pause it(thread) has to suspend until the user clicks resume. The problem is,the above will not work(will not suspend and resume) for both thread..but works for a singlw thread. Please check the code to get an idea and reply me what can done! on button click start void CThreadingEx3Dlg::OnBnClickedStart() { m_ProgressBar.SetRange(start,end); myThread1 = AfxBeginThread((AFX_THREADPROC)MyThreadFunction1,this); myThread2 = AfxBeginThread((AFX_THREADPROC)MyThreadFunction2,this); } thread1 UINT MyThreadFunction1(LPARAM lparam) { CThreadingEx3Dlg* pthis = (CThreadingEx3Dlg*)lparam; for(int intvalue =pthis->start;intvalue<=pthis->end; ++intvalue) { pthis->SendMessage(WM_MY_THREAD_MESSAGE1,intvalue); } return 0; } thread1 function LRESULT CThreadingEx3Dlg::OnThreadMessage1(WPARAM wparam,LPARAM lparam) { int nProgress= (int)wparam; m_ProgressBar.SetPos(nProgress); CString strStatus; strStatus.Format(L"Thread1:Processing item: %d", nProgress); m_Static.SetWindowText(strStatus); Sleep(100); return 0; } thread2 UINT MyThreadFunction2(LPARAM lparam) { CThreadingEx3Dlg* pthis = (CThreadingEx3Dlg*)lparam; for(int i =pthis->start;i<=pthis->end;i++) { pthis->SendMessage(WM_MY_THREAD_MESSAGE2,i); } return 0; } thread2 function LRESULT CThreadingEx3Dlg::OnThreadMessage2(WPARAM wparam,LPARAM lparam) { m_Static1.GetDlgItem(IDC_STATIC6); m_Static1.SetWindowTextW(L"Thread2 Running"); Sleep(100); m_Static1.SetWindowTextW(L""); Sleep(100); return TRUE; } void CThreadingEx3Dlg::OnBnClickedPause() { // TODO: Add your control notification handler code here if(!m_Track) { m_Track = TRUE; GetDlgItem(IDCANCEL)->SetWindowTextW(L"Resume"); myThread1->SuspendThread(); WaitForSingleObject(myThread1->m_hThread,INFINITE); myThread2->SuspendThread(); m_Static.SetWindowTextW(L"Paused.."); } else { m_Track = FALSE; GetDlgItem(IDCANCEL)->SetWindowTextW(L"Pause"); myThread1->ResumeThread(); myThread2->ResumeThread(); /*myEventHandler.SetEvent(); WaitForSingleObject(myThread1->m_hThread,INFINITE);*/ } }

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  • How to hold a queue of messages and have a group of working threads without polling?

    - by Mark
    I have a workflow that I want to looks something like this: / Worker 1 \ =Request Channel= - [Holding Queue|||] - Worker 2 - =Response Channel= \ Worker 3 / That is: Requests come in and they enter a FIFO queue Identical workers then pick up tasks from the queue At any given time any worker may work only one task When a worker is free and the holding queue is non-empty the worker should immediately pick up another task When tasks are complete, a worker places the result on the Response Channel I know there are QueueChannels in Spring Integration, but these channels require polling (which seems suboptimal). In particular, if a worker can be busy, I'd like the worker to be busy. Also, I've considered avoiding the queue altogether and simply letting tasks round-robin to all workers, but it's preferable to have a single waiting line as some tasks may be accomplished faster than others. Furthermore, I'd like insight into how many jobs are remaining (which I can get from the queue) and the ability to cancel all or particular jobs. How can I implement this message queuing/work distribution pattern while avoiding a polling? Edit: It appears I'm looking for the Message Dispatcher pattern -- how can I implement this using Spring/Spring Integration?

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  • How do I call a method when all threads (backgroundWorkers) are done with their jobs in .NET

    - by claws
    Hello, I'm using C# .NET. Its a GUI application. When user clicks a button work is assigned to 4 different background workers. Each take different amount of time and the end result is they populate some variables. I need to call a method which operates on these end results of each bgwoeker and gives the final output. How to accomplish this? Problem here is to be sure that all bgworkers are done with their job.

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  • Can foreground threads be aborted on application shutdown in C#.Net?

    - by Cartman
    I inherited a c# winforms app (.NET 2.0) which creates a thread upon startup in a singleton class. This thread handles messages from a messaging topic (Tibco EMS). When the app is shutdown, the thread doesn't remain the the background? How is this possible? Is there something else happening that can abort this thread? Code snippet: public class Startup { public static void main(string [] args) { MySingletonClass.Instance.Init(); // do other things below Application.Run(new MainForm()); } public class MySingletonClass { // singleton code //.. //.. private Thread t; public void Init() { t = new Thread(new ThreadStart(poll)); } private void poll() { while(true} { // listen for messages and process but there is no break clause // exceptions are also handled within to ensure control doesnt go out } } Pls. don't write to tell me this is bad code. I know it and i was going to refactor it when i realised that the app actually shutdowns correctly inspite of this aberration. So i want to understand how this is possible. Thanks for your help

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  • How to handle feedback from background threads if the user uses the back button?

    - by Janusz
    I have the following problem: I have an Activity where a user can start a web search showing a new activity to show a progress bar until the results are shown. Now the user can either wait for the results or maybe think about the search parameters, hit the back button and triggering a new search. The search is running in an Async Task and therefor still running if the user hits back. At the moment the thread finishes it calls some methods on the old activity causing the activity to show a dialog. This causes the system to crash because the dialog tries to show itself with a reference to an activity that is not longer present on the screen. How can I achieve a dialog that is only shown if the activity is still active?

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