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  • How to sort TBB concurrent_vector or concurrent_queue?

    - by Jackie
    Now I have a solver in that I need to keep a set of self-defined data type objects in a concurrent_vector or queue. It has to be concurrent because the objects come from different threads.With this concurrent container, I hope to sort these objects, eliminate duplicates and send them back when other threads need them. However, I know TBB offers concurrent_vector and concurrent_queue which can be read and written concurrently from different threads. But how to sort the objects inside a container? Does everyone know how to do that? Thanks.

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  • C#: System.Collections.Concurrent.ConcurrentQueue vs. Queue

    - by James Michael Hare
    I love new toys, so of course when .NET 4.0 came out I felt like the proverbial kid in the candy store!  Now, some people get all excited about the IDE and it’s new features or about changes to WPF and Silver Light and yes, those are all very fine and grand.  But me, I get all excited about things that tend to affect my life on the backside of development.  That’s why when I heard there were going to be concurrent container implementations in the latest version of .NET I was salivating like Pavlov’s dog at the dinner bell. They seem so simple, really, that one could easily overlook them.  Essentially they are implementations of containers (many that mirror the generic collections, others are new) that have either been optimized with very efficient, limited, or no locking but are still completely thread safe -- and I just had to see what kind of an improvement that would translate into. Since part of my job as a solutions architect here where I work is to help design, develop, and maintain the systems that process tons of requests each second, the thought of extremely efficient thread-safe containers was extremely appealing.  Of course, they also rolled out a whole parallel development framework which I won’t get into in this post but will cover bits and pieces of as time goes by. This time, I was mainly curious as to how well these new concurrent containers would perform compared to areas in our code where we manually synchronize them using lock or some other mechanism.  So I set about to run a processing test with a series of producers and consumers that would be either processing a traditional System.Collections.Generic.Queue or a System.Collection.Concurrent.ConcurrentQueue. Now, I wanted to keep the code as common as possible to make sure that the only variance was the container, so I created a test Producer and a test Consumer.  The test Producer takes an Action<string> delegate which is responsible for taking a string and placing it on whichever queue we’re testing in a thread-safe manner: 1: internal class Producer 2: { 3: public int Iterations { get; set; } 4: public Action<string> ProduceDelegate { get; set; } 5: 6: public void Produce() 7: { 8: for (int i = 0; i < Iterations; i++) 9: { 10: ProduceDelegate(“Hello”); 11: } 12: } 13: } Then likewise, I created a consumer that took a Func<string> that would read from whichever queue we’re testing and return either the string if data exists or null if not.  Then, if the item doesn’t exist, it will do a 10 ms wait before testing again.  Once all the producers are done and join the main thread, a flag will be set in each of the consumers to tell them once the queue is empty they can shut down since no other data is coming: 1: internal class Consumer 2: { 3: public Func<string> ConsumeDelegate { get; set; } 4: public bool HaltWhenEmpty { get; set; } 5: 6: public void Consume() 7: { 8: bool processing = true; 9: 10: while (processing) 11: { 12: string result = ConsumeDelegate(); 13: 14: if(result == null) 15: { 16: if (HaltWhenEmpty) 17: { 18: processing = false; 19: } 20: else 21: { 22: Thread.Sleep(TimeSpan.FromMilliseconds(10)); 23: } 24: } 25: else 26: { 27: DoWork(); // do something non-trivial so consumers lag behind a bit 28: } 29: } 30: } 31: } Okay, now that we’ve done that, we can launch threads of varying numbers using lambdas for each different method of production/consumption.  First let's look at the lambdas for a typical System.Collections.Generics.Queue with locking: 1: // lambda for putting to typical Queue with locking... 2: var productionDelegate = s => 3: { 4: lock (_mutex) 5: { 6: _mutexQueue.Enqueue(s); 7: } 8: }; 9:  10: // and lambda for typical getting from Queue with locking... 11: var consumptionDelegate = () => 12: { 13: lock (_mutex) 14: { 15: if (_mutexQueue.Count > 0) 16: { 17: return _mutexQueue.Dequeue(); 18: } 19: } 20: return null; 21: }; Nothing new or interesting here.  Just typical locks on an internal object instance.  Now let's look at using a ConcurrentQueue from the System.Collections.Concurrent library: 1: // lambda for putting to a ConcurrentQueue, notice it needs no locking! 2: var productionDelegate = s => 3: { 4: _concurrentQueue.Enqueue(s); 5: }; 6:  7: // lambda for getting from a ConcurrentQueue, once again, no locking required. 8: var consumptionDelegate = () => 9: { 10: string s; 11: return _concurrentQueue.TryDequeue(out s) ? s : null; 12: }; So I pass each of these lambdas and the number of producer and consumers threads to launch and take a look at the timing results.  Basically I’m timing from the time all threads start and begin producing/consuming to the time that all threads rejoin.  I won't bore you with the test code, basically it just launches code that creates the producers and consumers and launches them in their own threads, then waits for them all to rejoin.  The following are the timings from the start of all threads to the Join() on all threads completing.  The producers create 10,000,000 items evenly between themselves and then when all producers are done they trigger the consumers to stop once the queue is empty. These are the results in milliseconds from the ordinary Queue with locking: 1: Consumers Producers 1 2 3 Time (ms) 2: ---------- ---------- ------ ------ ------ --------- 3: 1 1 4284 5153 4226 4554.33 4: 10 10 4044 3831 5010 4295.00 5: 100 100 5497 5378 5612 5495.67 6: 1000 1000 24234 25409 27160 25601.00 And the following are the results in milliseconds from the ConcurrentQueue with no locking necessary: 1: Consumers Producers 1 2 3 Time (ms) 2: ---------- ---------- ------ ------ ------ --------- 3: 1 1 3647 3643 3718 3669.33 4: 10 10 2311 2136 2142 2196.33 5: 100 100 2480 2416 2190 2362.00 6: 1000 1000 7289 6897 7061 7082.33 Note that even though obviously 2000 threads is quite extreme, the concurrent queue actually scales really well, whereas the traditional queue with simple locking scales much more poorly. I love the new concurrent collections, they look so much simpler without littering your code with the locking logic, and they perform much better.  All in all, a great new toy to add to your arsenal of multi-threaded processing!

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  • Concurrent Generation of Sequential Keys

    - by GenTiradentes
    I'm working on a project which generates a very large number of sequential text strings, in a very tight loop. My application makes heavy use of SIMD instruction set extensions like SSE and MMX, in other parts of the program, but the key generator is plain C++. The way my key generator works is I have a keyGenerator class, which holds a single char array that stores the current key. To get the next key, there is a function called "incrementKey," which treats the string as a number, adding one to the string, carrying where necessary. Now, the problem is, the keygen is somewhat of a bottleneck. It's fast, but it would be nice if it were faster. One of the biggest problems is that when I'm generating a set of sequential keys to be processed using my SSE2 code, I have to have the entire set stored in an array, which means I have to sequentially generate and copy 12 strings into an array, one by one, like so: char* keys[12]; for(int i = 0; i < 12; i++) { keys[i] = new char[16]; strcmp(keys[i], keygen++); } So how would you efficiently generate these plaintext strings in order? I need some ideas to help move this along. Concurrency would be nice; as my code is right now, each successive key depends on the previous one, which means that the processor can't start work on the next key until the current one has been completely generated. Here is the code relevant to the key generator: KeyGenerator.h class keyGenerator { public: keyGenerator(unsigned long long location, characterSet* charset) : location(location), charset(charset) { for(int i = 0; i < 16; i++) key[i] = 0; charsetStr = charset->getCharsetStr(); integerToKey(); } ~keyGenerator() { } inline void incrementKey() { register size_t keyLength = strlen(key); for(register char* place = key; place; place++) { if(*place == charset->maxChar) { // Overflow, reset char at place *place = charset->minChar; if(!*(place+1)) { // Carry, no space, insert char *(place+1) = charset->minChar; ++keyLength; break; } else { continue; } } else { // Space available, increment char at place if(*place == charset->charSecEnd[0]) *place = charset->charSecBegin[0]; else if(*place == charset->charSecEnd[1]) *place = charset->charSecBegin[1]; (*place)++; break; } } } inline char* operator++() // Pre-increment { incrementKey(); return key; } inline char* operator++(int) // Post-increment { memcpy(postIncrementRetval, key, 16); incrementKey(); return postIncrementRetval; } void integerToKey() { register unsigned long long num = location; if(!num) { key[0] = charsetStr[0]; } else { num++; while(num) { num--; unsigned int remainder = num % charset->length; num /= charset->length; key[strlen(key)] = charsetStr[remainder]; } } } inline unsigned long long keyToInteger() { // TODO return 0; } inline char* getKey() { return key; } private: unsigned long long location; characterSet* charset; std::string charsetStr; char key[16]; // We need a place to store the key for the post increment operation. char postIncrementRetval[16]; }; CharacterSet.h struct characterSet { characterSet() { } characterSet(unsigned int len, int min, int max, int charsec0, int charsec1, int charsec2, int charsec3) { init(length, min, max, charsec0, charsec1, charsec2, charsec3); } void init(unsigned int len, int min, int max, int charsec0, int charsec1, int charsec2, int charsec3) { length = len; minChar = min; maxChar = max; charSecEnd[0] = charsec0; charSecBegin[0] = charsec1; charSecEnd[1] = charsec2; charSecBegin[1] = charsec3; } std::string getCharsetStr() { std::string retval; for(int chr = minChar; chr != maxChar; chr++) { for(int i = 0; i < 2; i++) if(chr == charSecEnd[i]) chr = charSecBegin[i]; retval += chr; } return retval; } int minChar, maxChar; // charSec = character set section int charSecEnd[2], charSecBegin[2]; unsigned int length; };

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  • How to handle concurrent web requests

    - by Rajan
    I am having a simple webservice running in my local system, hosted in IIS 6.0. This webservice is accessible from any other system (within LAN and Internet) and everything works fine as long as the number of systems accessing my webservice is very much limited. Whereas, if more number of users (say 500) are concurrently trying to access my webservice, an exception is thrown saying 'Server is busy'. When i browsed in net, i found that IIS can handle only 100-150 web requests concurrently. What should i do to make my webservice accessible from more than 500 users, concurrently? Thanks in advance. Rajan.

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  • C++/g++: Concurrent programm

    - by phimuemue
    Hi, I got a C++ program (source) that is said to work in parallel. However, if I compile it (I am using Ubuntu 10.04 and g++ 4.4.3) with g++ and run it, one of my two CPU cores gets full load while the other is doing "nothing". So I spoke to the one who gave me the program. I was told that I had to set specific flags for g++ in order to get the program compiled for 2 CPU cores. However, if I look at the code I'm not able to find any lines that point to parallelism. So I have two questions: Are there any C++-intrinsics for multithreaded applications, i.e. is it possible to write parallel code without any extra libraries (because I did not find any non-standard libraries included)? Is it true that there are indeed flags for g++ that tell the compiler to compile the program for 2 CPU cores and to compile it so it runs in parallel (and if: what are they)?

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  • Concurrent Threads in C# using BackgroundWorker

    - by Jim Fell
    My C# application is such that a background worker is being used to wait for the acknowledgement of some transmitted data. Here is some psuedo code demonstrating what I'm trying to do: UI_thread { TransmitData() { // load data for tx // fire off TX background worker } RxSerialData() { // if received data is ack, set ack received flag } } TX_thread { // transmit data // set ack wait timeout // fire off ACK background worker // wait for ACK background worker to complete // evaluate status of ACK background worker as completed, failed, etc. } ACK_thread { // wait for ack received flag to be set } What happens is that the ACK BackgroundWorker times out, and the acknowledgement is never received. I'm fairly certain that it is being transmitted by the remote device because that device has not changed at all, and the C# application is transmitting. I have changed the ack thread from this (when it was working)... for( i = 0; (i < waitTimeoutVar) && (!bAckRxd); i++ ) { System.Threading.Thread.Sleep(1); } ...to this... DateTime dtThen = DateTime.Now(); DateTime dtNow; TimeSpan stTime; do { dtNow = DateTime.Now(); stTime = dtNow - dtThen; } while ( (stTime.TotalMilliseconds < waitTimeoutVar) && (!bAckRxd) ); The latter generates a very acurate wait time, as compared to the former. However, I am wondering if removal of the Sleep function is interferring with the ability to receive serial data. Does C# only allow one thread to run at a time, that is, do I have to put threads to sleep at some time to allow other threads to run? Any thoughts or suggestions you may have would be appreciated. I am using Microsoft Visual C# 2008 Express Edition. Thanks.

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  • Ideas for pet concurrent applications [closed]

    - by DJoyce
    As part of research I am doing into functional concurrent programming, I am looking to develop a prototype business application (or otherwise), that requires and supports parallelism. So I will first develop this application in java 7, followed by scala and then clojure while demonstrating the concurrent support in each language. However, I am a little short on suitable ideas and therefore i'm hoping I can get some good, interesting ideas on this thread. Thanks!

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  • Webcast Replay Available: Performance Tuning E-Business Suite Concurrent Manager (Performance Series Part 2 of 3)

    - by BillSawyer
    I am pleased to release the replay and presentation for the latest ATG Live Webcast: Performance Tuning E-Business Suite Concurrent Manager (Performance Series Part 2 of 3) (Presentation)Andy Tremayne, Senior Architect, Applications Performance, and co-author of Oracle Applications Performance Tuning Handbook from Oracle Press, and Uday Moogala, Senior Principal Engineer, Applications Performance discussed two major components of E-Business Suite performance tuning:  concurrent management and tracing. They dispel some myths surrounding these topics, and shared with you the recommended best practices that you can use on your own E-Business Suite instance.Finding other recorded ATG webcastsThe catalog of ATG Live Webcast replays, presentations, and all ATG training materials is available in this blog's Webcasts and Training section.

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  • Concurrent modification during backup: rsync vs dump vs tar vs ?

    - by pehrs
    I have a Linux log server where multiple applications write data. Data is written in bursts, and in a lot of different files. I need to make a backup of this mess, preferably preserving as much coherence between the file versions as possible and avoiding getting truncated files. Total amount of data on the server is about 100Gb. What I really would want (but can't) is to shut-down, backup the system cold and then start it up again. What kind of guarantees against concurrent modification does the various backup tools give? When do they "freeze" the file versions? I am looking at rsync, dump and tar at the moment, but I am open for other (open source) alternatives. Changing the application or blocking writing for backups is sadly not an option. System is not running LVM (yet), but I have considered that for rebuilding the system and then snapshots.

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  • NUMA-aware constructs for java.util.concurrent

    - by Dave
    The constructs in the java.util.concurrent JSR-166 "JUC" concurrency library are currently NUMA-oblivious. That's because we currently don't have the topology discovery infrastructure and underpinnings in place that would allow and enable NUMA-awareness. But some quick throw-away prototypes show that it's possible to write NUMA-aware library code. I happened to use JUC Exchanger as a research vehicle. Another interesting idea is to adapt fork-join work-stealing to favor stealing from queues associated with 'nearby' threads.

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  • proper concurrent users estimation case studies

    - by golemwashere
    I've been asked to size a web architecture for an excessive number of concurrent users ( hundreds of thousands ). I'm having a hard time convincing these people that unless you are in the top 5 of your country websites it's quite hard to hit those numbers. Can anyone provide some real world case studies providing stats for total / concurrent users explaining what is the usual ratio between total vs concurrent?

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  • Concurrent Affairs

    - by Tony Davis
    I once wrote an editorial, multi-core mania, on the conundrum of ever-increasing numbers of processor cores, but without the concurrent programming techniques to get anywhere near exploiting their performance potential. I came to the.controversial.conclusion that, while the problem loomed for all procedural languages, it was not a big issue for the vast majority of programmers. Two years later, I still think most programmers don't concern themselves overly with this issue, but I do think that's a bigger problem than I originally implied. Firstly, is the performance boost from writing code that can fully exploit all available cores worth the cost of the additional programming complexity? Right now, with quad-core processors that, at best, can make our programs four times faster, the answer is still no for many applications. But what happens in a few years, as the number of cores grows to 100 or even 1000? At this point, it becomes very hard to ignore the potential gains from exploiting concurrency. Possibly, I was optimistic to assume that, by the time we have 100-core processors, and most applications really needed to exploit them, some technology would be around to allow us to do so with relative ease. The ideal solution would be one that allows programmers to forget about the problem, in much the same way that garbage collection removed the need to worry too much about memory allocation. From all I can find on the topic, though, there is only a remote likelihood that we'll ever have a compiler that takes a program written in a single-threaded style and "auto-magically" converts it into an efficient, correct, multi-threaded program. At the same time, it seems clear that what is currently the most common solution, multi-threaded programming with shared memory, is unsustainable. As soon as a piece of state can be changed by a different thread of execution, the potential number of execution paths through your program grows exponentially with the number of threads. If you have two threads, each executing n instructions, then there are 2^n possible "interleavings" of those instructions. Of course, many of those interleavings will have identical behavior, but several won't. Not only does this make understanding how a program works an order of magnitude harder, but it will also result in irreproducible, non-deterministic, bugs. And of course, the problem will be many times worse when you have a hundred or a thousand threads. So what is the answer? All of the possible alternatives require a change in the way we write programs and, currently, seem to be plagued by performance issues. Software transactional memory (STM) applies the ideas of database transactions, and optimistic concurrency control, to memory. However, working out how to break down your program into sufficiently small transactions, so as to avoid contention issues, isn't easy. Another approach is concurrency with actors, where instead of having threads share memory, each thread runs in complete isolation, and communicates with others by passing messages. It simplifies concurrent programs but still has performance issues, if the threads need to operate on the same large piece of data. There are doubtless other possible solutions that I haven't mentioned, and I would love to know to what extent you, as a developer, are considering the problem of multi-core concurrency, what solution you currently favor, and why. Cheers, Tony.

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  • "Microsoft ne défend pas assez les Droits de l'Homme", Google n'apprécie pas que son concurrent rest

    "Microsoft ne défend pas assez les Droits de l'Homme", Google n'apprécie pas que son concurrent reste en Chine La morale businesso-américaine commence à s'intéresser au cas de la Chine. Suite à l'altercation musclée entre Google et le régime en place à Pékin, d'autres entreprises se mettent à considérer l'idée de quitter le pays du riz. La politique chinoise est montrée du doigt dans les discussions mondaines entre les dirigeants des plus grands groupes américains. Fort de son nouveau statut de justicier, Sergey Brin (l'un des co-fondateurs de Google), s'en est pris à Microsoft, accusant la firme de ne pas assez défendre les droits de l'Homme et la liberté d'expression. « J'es...

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  • How do Tortoise svn handle concurrent file updation

    - by sabithpocker
    I don't know much about Tortoise SVN, but have been using it for a while :) I understand that it is capable of managing concurrent users on same file, but how does it do that? I am working on a file that another user is using concurrently and he committed couple of times before me, Now my files doesnt have the changes he made. Every time i end up in such a situation I gets a conflict and I am stuck. So is there something else I should do instead of just committing and causing conflict. Is it safe to do a update first and then commit ? Will I lose my local changes in this case? Please dont beat me up if this is somthing basic, A link to some tutorial will be useful in that case :)

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  • NoSQL : JSON, indexation distribuée et géoréplication débarquent dans Couchbase, le concurrent de MongoDB

    Base de données NoSQL : documents JSON, indexation distribuée et géoréplication débarquent dans Couchbase Le concurrent de MongoDB Couchbase Server, le système de gestion de bases de données NoSQL, vient de subir une mise à jour assez importante. La version 2.0 de Couchbase introduit un modèle de stockage de documents et un magasin clé-valeur (key-value), permettant à l'outil de faire un grand pas dans le support du Big Data (gros volumes de données). Pour rappel, CouchBase est un projet initialement basé sur le système noSQL Apache CouchDB, à la différence que le code Erlang de CouchDB a été entièrement réécrit en C++, avec des ajustements et ajouts en tirant profit du système de ...

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  • Les Google Apps veulent se positionner en concurrent de Sharepoint, Gmail passe les 425 millions d'utilisateurs

    Les Google Apps veulent se positionner en concurrent de Sharepoint Gmail passe les 425 millions d'utilisateurs Parmi les nombreux chiffres donnés sur ses différents produits, Google en a mis deux particulièrement en avant : ceux de ses Web Apps et de sa messagerie. Lancée en 2004, la messagerie Gmail possède aujourd'hui 425 millions d'utilisateurs. Un bon résultat. Surtout si on lui ajoute 5 millions de client "business" qui utilisent les Google Apps (en résumé Gmail + Google Docs) comme par exemple la compagnie aérienne européenne KLM. Mais cette annonce a surtout été l'occasion pour Sundar Pichai, vice-président de Google, de s'attaquer à son conc...

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  • Concurrent NFS access

    - by Kristian
    Similar to Concurrent FTP access. How is concurrent file access handled for NFS? Say that one client is updating/overwriting a file on a NFS server, and a process on the server is reading that same file directly from the file system at the same time. Is there some sort of atomic handling of file read/write in NFS/Linux or do I have to work with tmp files to ensure data consistency? I'm worried that the process reading the file will get corrupt data.

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  • Free or very Cheap VPN server software with high concurrent users count

    - by Martin KS
    I'm hoping to implement a VPN whereby about 200 concurrent users can log in to briefly access my network. I had a look at OpenVPN and this seemed excellent, but was hoping that there would be a less costly option on a per-concurrent-user basis. I've no need for particularly strong security, and my only other requirement is that I would need to be able to add users in bulk via csv or similar. Does anyone have any suggestions?

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  • Running & Managing Concurrent Queries in SQL Developer

    - by thatjeffsmith
    We’ve all been there – you’ve managed to write a query that takes longer than a few seconds to execute. Tuning aside, sometimes it takes longer than you want for a query to run. So what’s a SQL Developer user to do? I say, keep going! While you’re waiting for your query to finish, there’s no reason why you can’t continue on with your work. If you need to execute something else in a worksheet, there’s no reason to launch a 2nd or 3rd copy of SQL Developer. Just open an un-shared worksheet. Now while you’ve got 1 or more queries running, you can easily get yourself into a situation where you’re not sure what’s running where. Or maybe you want to cancel a query or just check how long something’s been running. Just open the Task Progress Panel If a query or task in SQL Developer takes more than 3-5 seconds, it will appear in the Task Progress panel. You can then watch the throbbers go back and forth while you sip your coffee/soda/Red Bull. Run a query, spawn a new worksheet, run another query, watch them in the Task Progress panel. Kudos and thanks to @leight0nn for helping me get the title of this post right If you’re looking for help in managing and monitoring sessions in general, check out this post.

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  • A better way to do concurrent programming

    - by Alex.Davies
    Programming to take advantage of multicore processors is hard. If you let multiple threads access the same memory, bad things happen. To avoid this, you use the lock keyword, but if you use that in the wrong way, your code deadlocks. It's all a nightmare. Luckily, there's a better way - Actors. They're really easy to think about. They're really safe (if you follow a couple of simple rules). And high-performance, type-safe actors are now available for .NET by using this open-source library: http://code.google.com/p/n-act/ Have a look at the site for details. I'll blog with more reasons to use actors and tips and tricks to get the best parallelism from them soon.

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  • Efficiently separating Read/Compute/Write steps for concurrent processing of entities in Entity/Component systems

    - by TravisG
    Setup I have an entity-component architecture where Entities can have a set of attributes (which are pure data with no behavior) and there exist systems that run the entity logic which act on that data. Essentially, in somewhat pseudo-code: Entity { id; map<id_type, Attribute> attributes; } System { update(); vector<Entity> entities; } A system that just moves along all entities at a constant rate might be MovementSystem extends System { update() { for each entity in entities position = entity.attributes["position"]; position += vec3(1,1,1); } } Essentially, I'm trying to parallelise update() as efficiently as possible. This can be done by running entire systems in parallel, or by giving each update() of one system a couple of components so different threads can execute the update of the same system, but for a different subset of entities registered with that system. Problem In reality, these systems sometimes require that entities interact(/read/write data from/to) each other, sometimes within the same system (e.g. an AI system that reads state from other entities surrounding the current processed entity), but sometimes between different systems that depend on each other (i.e. a movement system that requires data from a system that processes user input). Now, when trying to parallelize the update phases of entity/component systems, the phases in which data (components/attributes) from Entities are read and used to compute something, and the phase where the modified data is written back to entities need to be separated in order to avoid data races. Otherwise the only way (not taking into account just "critical section"ing everything) to avoid them is to serialize parts of the update process that depend on other parts. This seems ugly. To me it would seem more elegant to be able to (ideally) have all processing running in parallel, where a system may read data from all entities as it wishes, but doesn't write modifications to that data back until some later point. The fact that this is even possible is based on the assumption that modification write-backs are usually very small in complexity, and don't require much performance, whereas computations are very expensive (relatively). So the overhead added by a delayed-write phase might be evened out by more efficient updating of entities (by having threads work more % of the time instead of waiting). A concrete example of this might be a system that updates physics. The system needs to both read and write a lot of data to and from entities. Optimally, there would be a system in place where all available threads update a subset of all entities registered with the physics system. In the case of the physics system this isn't trivially possible because of race conditions. So without a workaround, we would have to find other systems to run in parallel (which don't modify the same data as the physics system), other wise the remaining threads are waiting and wasting time. However, that has disadvantages Practically, the L3 cache is pretty much always better utilized when updating a large system with multiple threads, as opposed to multiple systems at once, which all act on different sets of data. Finding and assembling other systems to run in parallel can be extremely time consuming to design well enough to optimize performance. Sometimes, it might even not be possible at all because a system just depends on data that is touched by all other systems. Solution? In my thinking, a possible solution would be a system where reading/updating and writing of data is separated, so that in one expensive phase, systems only read data and compute what they need to compute, and then in a separate, performance-wise cheap, write phase, attributes of entities that needed to be modified are finally written back to the entities. The Question How might such a system be implemented to achieve optimal performance, as well as making programmer life easier? What are the implementation details of such a system and what might have to be changed in the existing EC-architecture to accommodate this solution?

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