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  • SQL SERVER – Concurrancy Problems and their Relationship with Isolation Level

    - by pinaldave
    Concurrency is simply put capability of the machine to support two or more transactions working with the same data at the same time. This usually comes up with data is being modified, as during the retrieval of the data this is not the issue. Most of the concurrency problems can be avoided by SQL Locks. There are four types of concurrency problems visible in the normal programming. 1)      Lost Update – This problem occurs when there are two transactions involved and both are unaware of each other. The transaction which occurs later overwrites the transactions created by the earlier update. 2)      Dirty Reads – This problem occurs when a transactions selects data that isn’t committed by another transaction leading to read the data which may not exists when transactions are over. Example: Transaction 1 changes the row. Transaction 2 changes the row. Transaction 1 rolls back the changes. Transaction 2 has selected the row which does not exist. 3)      Nonrepeatable Reads – This problem occurs when two SELECT statements of the same data results in different values because another transactions has updated the data between the two SELECT statements. Example: Transaction 1 selects a row, which is later on updated by Transaction 2. When Transaction A later on selects the row it gets different value. 4)      Phantom Reads – This problem occurs when UPDATE/DELETE is happening on one set of data and INSERT/UPDATE is happening on the same set of data leading inconsistent data in earlier transaction when both the transactions are over. Example: Transaction 1 is deleting 10 rows which are marked as deleting rows, during the same time Transaction 2 inserts row marked as deleted. When Transaction 1 is done deleting rows, there will be still rows marked to be deleted. When two or more transactions are updating the data, concurrency is the biggest issue. I commonly see people toying around with isolation level or locking hints (e.g. NOLOCK) etc, which can very well compromise your data integrity leading to much larger issue in future. Here is the quick mapping of the isolation level with concurrency problems: Isolation Dirty Reads Lost Update Nonrepeatable Reads Phantom Reads Read Uncommitted Yes Yes Yes Yes Read Committed No Yes Yes Yes Repeatable Read No No No Yes Snapshot No No No No Serializable No No No No I hope this 400 word small article gives some quick understanding on concurrency issues and their relation to isolation level. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Are there concurrency problems when using -performSelector:withObject:afterDelay: ?

    - by mystify
    For example, I often use this: [self performSelector:@selector(doSomethingAfterDelay:) withObject:someObject afterDelay:someDelay]; Now, lets say I call this 10 times to perform at the exact same delay, like: [self performSelector:@selector(doSomethingAfterDelay:) withObject:someObject afterDelay:2.0]; [self performSelector:@selector(doSomethingAfterDelay:) withObject:someObject afterDelay:2.0]; [self performSelector:@selector(doSomethingAfterDelay:) withObject:someObject afterDelay:2.0]; [self performSelector:@selector(doSomethingAfterDelay:) withObject:someObject afterDelay:2.0]; [self performSelector:@selector(doSomethingAfterDelay:) withObject:someObject afterDelay:2.0]; [self performSelector:@selector(doSomethingAfterDelay:) withObject:someObject afterDelay:2.0]; [self performSelector:@selector(doSomethingAfterDelay:) withObject:someObject afterDelay:2.0]; [self performSelector:@selector(doSomethingAfterDelay:) withObject:someObject afterDelay:2.0]; [self performSelector:@selector(doSomethingAfterDelay:) withObject:someObject afterDelay:2.0]; - (void)doSomethingAfterDelay:(id)someObject { /* access an array, read stuff, write stuff, do different things that would suffer in multithreaded environments .... all operations are nonatomic! */ } I have observed pretty strange behavior when doing things like this. For my understanding, this method schedules a timer to fire on the current thread, so in this case the main thread. But since it doesn't create new threads, it actually should not be possible to run into concurrency problems, right?

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  • Are there concurrency problems when using -performSelector:withObject:afterDelay: ?

    - by mystify
    For example, I often use this: [self performSelector:@selector(doSomethingAfterDelay:) withObject:someObject afterDelay:someDelay]; Now, lets say I call this 10 times to perform at the exact same delay, like: [self performSelector:@selector(doSomethingAfterDelay:) withObject:someObject afterDelay:2.0]; [self performSelector:@selector(doSomethingAfterDelay:) withObject:someObject afterDelay:2.0]; [self performSelector:@selector(doSomethingAfterDelay:) withObject:someObject afterDelay:2.0]; [self performSelector:@selector(doSomethingAfterDelay:) withObject:someObject afterDelay:2.0]; [self performSelector:@selector(doSomethingAfterDelay:) withObject:someObject afterDelay:2.0]; [self performSelector:@selector(doSomethingAfterDelay:) withObject:someObject afterDelay:2.0]; [self performSelector:@selector(doSomethingAfterDelay:) withObject:someObject afterDelay:2.0]; [self performSelector:@selector(doSomethingAfterDelay:) withObject:someObject afterDelay:2.0]; [self performSelector:@selector(doSomethingAfterDelay:) withObject:someObject afterDelay:2.0]; - (void)doSomethingAfterDelay:(id)someObject { /* access an array, read stuff, write stuff, do different things that would suffer in multithreaded environments .... all operations are nonatomic! */ } I have observed pretty strange behavior when doing things like this. For my understanding, this method schedules a timer to fire on the current thread, so in this case the main thread. But since it doesn't create new threads, it actually should not be possible to run into concurrency problems, right?

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  • Matrix Multiplication with C++ AMP

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

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  • hosting simple python scripts in a container to handle concurrency, configuration, caching, etc.

    - by Justin Grant
    My first real-world Python project is to write a simple framework (or re-use/adapt an existing one) which can wrap small python scripts (which are used to gather custom data for a monitoring tool) with a "container" to handle boilerplate tasks like: fetching a script's configuration from a file (and keeping that info up to date if the file changes and handle decryption of sensitive config data) running multiple instances of the same script in different threads instead of spinning up a new process for each one expose an API for caching expensive data and storing persistent state from one script invocation to the next Today, script authors must handle the issues above, which usually means that most script authors don't handle them correctly, causing bugs and performance problems. In addition to avoiding bugs, we want a solution which lowers the bar to create and maintain scripts, especially given that many script authors may not be trained programmers. Below are examples of the API I've been thinking of, and which I'm looking to get your feedback about. A scripter would need to build a single method which takes (as input) the configuration that the script needs to do its job, and either returns a python object or calls a method to stream back data in chunks. Optionally, a scripter could supply methods to handle startup and/or shutdown tasks. HTTP-fetching script example (in pseudocode, omitting the actual data-fetching details to focus on the container's API): def run (config, context, cache) : results = http_library_call (config.url, config.http_method, config.username, config.password, ...) return { html : results.html, status_code : results.status, headers : results.response_headers } def init(config, context, cache) : config.max_threads = 20 # up to 20 URLs at one time (per process) config.max_processes = 3 # launch up to 3 concurrent processes config.keepalive = 1200 # keep process alive for 10 mins without another call config.process_recycle.requests = 1000 # restart the process every 1000 requests (to avoid leaks) config.kill_timeout = 600 # kill the process if any call lasts longer than 10 minutes Database-data fetching script example might look like this (in pseudocode): def run (config, context, cache) : expensive = context.cache["something_expensive"] for record in db_library_call (expensive, context.checkpoint, config.connection_string) : context.log (record, "logDate") # log all properties, optionally specify name of timestamp property last_date = record["logDate"] context.checkpoint = last_date # persistent checkpoint, used next time through def init(config, context, cache) : cache["something_expensive"] = get_expensive_thing() def shutdown(config, context, cache) : expensive = cache["something_expensive"] expensive.release_me() Is this API appropriately "pythonic", or are there things I should do to make this more natural to the Python scripter? (I'm more familiar with building C++/C#/Java APIs so I suspect I'm missing useful Python idioms.) Specific questions: is it natural to pass a "config" object into a method and ask the callee to set various configuration options? Or is there another preferred way to do this? when a callee needs to stream data back to its caller, is a method like context.log() (see above) appropriate, or should I be using yield instead? (yeild seems natural, but I worry it'd be over the head of most scripters) My approach requires scripts to define functions with predefined names (e.g. "run", "init", "shutdown"). Is this a good way to do it? If not, what other mechanism would be more natural? I'm passing the same config, context, cache parameters into every method. Would it be better to use a single "context" parameter instead? Would it be better to use global variables instead? Finally, are there existing libraries you'd recommend to make this kind of simple "script-running container" easier to write?

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  • How to reduce celeryd memory consumption?

    - by Gringo Suave
    I'm using celery 2.5.1 with django on a micro ec2 instance with 613mb memory and as such have to keep memory consumption down. Currently I'm using it only for the scheduler "celery beat" as a web interface to cron, though I hope to use it for more in the future. I've noticed it is the biggest consumer of memory on my micro machine even though I have configured the number of workers to one. I don't have many other options set in settings.py: import djcelery djcelery.setup_loader() BROKER_BACKEND = 'djkombu.transport.DatabaseTransport' CELERYBEAT_SCHEDULER = 'djcelery.schedulers.DatabaseScheduler' CELERY_RESULT_BACKEND = 'database' BROKER_POOL_LIMIT = 2 CELERYD_CONCURRENCY = 1 CELERY_DISABLE_RATE_LIMITS = True CELERYD_MAX_TASKS_PER_CHILD = 20 CELERYD_SOFT_TASK_TIME_LIMIT = 5 * 60 CELERYD_TASK_TIME_LIMIT = 6 * 60 Here's the details via top: PID USER NI CPU% VIRT SHR RES MEM% Command 1065 wuser 10 0.0 283M 4548 85m 14.3 python manage_prod.py celeryd --beat 1025 wuser 10 1.0 577M 6368 67m 11.2 python manage_prod.py celeryd --beat 1071 wuser 10 0.0 578M 2384 62m 10.6 python manage_prod.py celeryd --beat That's about 214mb of memory (and not much shared) to run a cron job occasionally. Have I done anything wrong, or can this be reduced about ten-fold somehow? ;) Update: here's my upstart config: description "Celery Daemon" start on (net-device-up and local-filesystems) stop on runlevel [016] nice 10 respawn respawn limit 5 10 chdir /home/wuser/wuser/ env CELERYD_OPTS=--concurrency=1 exec sudo -u wuser -H /usr/bin/python manage_prod.py celeryd --beat --concurrency=1 --loglevel info --logfile /var/tmp/celeryd.log Update 2: I notice there is one root process, one user child process, and two grandchildren from that. So I think it isn't a matter of duplicate startup. root 34580 1556 sudo -u wuser -H /usr/bin/python manage_prod.py celeryd wuser 577M 67548 +- python manage_prod.py celeryd --beat --concurrency=1 wuser 578M 63784 +- python manage_prod.py celeryd --beat --concurrency=1 wuser 271M 76260 +- python manage_prod.py celeryd --beat --concurrency=1

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  • Convert OpenGL code to DirectX

    - by Fredrik Boston Westman
    First of all, this is kind of a follow up question on @byte56 excellent anwser on this question concerning picking algorithms. I'm trying to convert one of his code examples to directX 11 however I have run into some problems ( I can pick but the picking is way off), and I wanted to make sure I had done it right before moving on and checking the rest of my code. I am not that familiar with openGl but I can imagine openGl has different coordinations systems, and functions that alters how you must implement to code a bit. The getPickRay function on the answer linked is what I'm trying to convert. This is the part of my code that I think is giving me trouble when converting from openGl to directX Because I'm unsure on how their different coordination systems differs from one another. PRVecX = ((( 2.0f * mouseX) / ClientWidth ) - 1 ) * tan((viewAngle)/2); PRVecY = (1-(( 2.0f * mouseY) / ClientHeight)) * tan((viewAngle)/2); Another thing that I am unsure about is this part: XMVECTOR worldSpaceNear = XMVector3TransformCoord(cameraSpaceNear, invMat); XMVECTOR worldSpaceFar = XMVector3TransformCoord(cameraSpaceFar, invMat); A couple of notes: The mouse coordinates are already converted so that the top left corner of the client window would be (0,0) and the bottom right (800,600) ( or whatever resolution you would have) The viewAngle is the same angle that I used when setting the camera view with XMMatrixPerspectiveFovLH. I removed the variables aspectRatio and zoomFactor because I assumed that they were related to some specific function of his game. To summarize it up to questions : Does the openGL coordination system differ in such a way that this equation in the first of my code examples wouldn't be valid when used in DirectX 11 ( with its respective screen coordination system)? Is the openGL method Matrix4f.transform(a, b, c) equal to the directX method c = XMVector3TransformCoord(b,a)? (where a is a matrix and b,c are vectors). Because I know when it comes to matrices order is important.

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  • Apache Bench length failures

    - by Laurens
    I am running Apache Bench against a Ruby on Rails XML-RPC web service that is running on Passenger via mod_passenger. All is fine when I run 1000 requests without concurrency. Bench indicates that all requests successfully complete with no failures. When I run Bench again with a concurrency level of 2, however, requests start to fail due to content length. I am seeing failures rates of 70-80% when using concurrency. This should not happen. The requests I am sending to the web service should always results in the same response. I have used cURL to verify that this is in fact the case. My Rails log is not showing any errors as well so I am curious to see what content Bench actually received and interpreted as a failure. Is there any way to print these failures?

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  • Lenovo V570 CPU fan running constantly, CPU core 1 running over 90%!

    - by Rabbit2190
    I have seen that a lot of people are having this same issue. I am running a Lenovo V570 i5 4 core, 6 gigs of ram, and am running 11.10 Onieric Ocelot. On my system monitor graph it shows CPU at 20%, when I open the monitor it shows core #1 at around 90%, the other cores fluctuate at or below 5-12% if even. Now this seems like a really terrible balance of power between the cores, especially with so much stress on one core only, when these things are designed to work with 4 cores and not at such high temps. My current readings say 64 degrees Celsius, this does not seem normal for any cpu, and I am seriously considering, working on my windows7 partition until I see a real solution to this issue or upgrading to 12.04 right away when it comes out... I have seen countless things saying it has something to do with the Kernel, the kernel on mine is the same as when I upgraded, I really do not like messing with it, as when I had 11.04, I did tinker with it due to the freeze issues I was having, and that just made worse issues. I like this version 11.10 and would like to keep it for a while, but without the fear that my core is going to fry! So any help would be much appreciated! I did try changing a couple things in ACPI, and restarting this did not help, and here I am. I tried one thing prior to that that was listed under a different computer brand, but it would not do a make on the file. I really need help with this, I rely on this computer for a lot of things, and love this OS! Please help so I do not need to resort to my Microsoft partition! PLEASE! Here is the fwts cpufrequ- output: rabbit@rabbit-Lenovo-V570:~$ sudo fwts cpufreq - 00001 fwts Results generated by fwts: Version V0.23.25 (Thu Oct 6 15 00002 fwts :12:31 BST 2011). 00003 fwts 00004 fwts Some of this work - Copyright (c) 1999 - 2010, Intel Corp. 00005 fwts All rights reserved. 00006 fwts Some of this work - Copyright (c) 2010 - 2011, Canonical. 00007 fwts 00008 fwts This test run on 02/04/12 at 17:23:22 on host Linux 00009 fwts rabbit-Lenovo-V570 3.0.0-17-generic-pae #30-Ubuntu SMP Thu 00010 fwts Mar 8 17:53:35 UTC 2012 i686. 00011 fwts 00012 fwts Running tests: cpufreq. 00014 cpufreq CPU frequency scaling tests (takes ~1-2 mins). 00015 cpufreq --------------------------------------------------------- 00016 cpufreq Test 1 of 1: CPU P-State Checks. 00017 cpufreq For each processor in the system, this test steps through 00018 cpufreq the various frequency states (P-states) that the BIOS 00019 cpufreq advertises for the processor. For each processor/frequency 00020 cpufreq combination, a quick performance value is measured. The 00021 cpufreq test then validates that: 00022 cpufreq 1) Each processor has the same number of frequency states 00023 cpufreq 2) Higher advertised frequencies have a higher performance 00024 cpufreq 3) No duplicate frequency values are reported by the BIOS 00025 cpufreq 4) Is BIOS wrongly doing Sw_All P-state coordination across cores 00026 cpufreq 5) Is BIOS wrongly doing Sw_Any P-state coordination across cores 00027 cpufreq Frequency | Speed 00028 cpufreq -----------+--------- 00029 cpufreq 2.45 Ghz | 100.0 % 00030 cpufreq 2.45 Ghz | 83.7 % 00031 cpufreq 2.05 Ghz | 69.2 % 00032 cpufreq 1.85 Ghz | 62.5 % 00033 cpufreq 1.65 Ghz | 55.2 % 00034 cpufreq 1400 Mhz | 48.6 % 00035 cpufreq 1200 Mhz | 41.8 % 00036 cpufreq 1000 Mhz | 34.5 % 00037 cpufreq 800 Mhz | 27.6 % 00038 cpufreq 9 CPU frequency steps supported 00039 cpufreq Frequency | Speed 00040 cpufreq -----------+--------- 00041 cpufreq 2.45 Ghz | 97.7 % 00042 cpufreq 2.45 Ghz | 83.7 % 00043 cpufreq 2.05 Ghz | 69.6 % 00044 cpufreq 1.85 Ghz | 63.3 % 00045 cpufreq 1.65 Ghz | 55.7 % 00046 cpufreq 1400 Mhz | 48.7 % 00047 cpufreq 1200 Mhz | 41.7 % 00048 cpufreq 1000 Mhz | 34.5 % 00049 cpufreq 800 Mhz | 27.5 % 00050 cpufreq Frequency | Speed 00051 cpufreq -----------+--------- 00052 cpufreq 2.45 Ghz | 97.7 % 00053 cpufreq 2.45 Ghz | 84.4 % 00054 cpufreq 2.05 Ghz | 69.6 % 00055 cpufreq 1.85 Ghz | 62.6 % 00056 cpufreq 1.65 Ghz | 55.9 % 00057 cpufreq 1400 Mhz | 48.7 % 00058 cpufreq 1200 Mhz | 41.7 % 00059 cpufreq 1000 Mhz | 34.7 % 00060 cpufreq 800 Mhz | 27.8 % 00061 cpufreq Frequency | Speed 00062 cpufreq -----------+--------- 00063 cpufreq 2.45 Ghz | 100.0 % 00064 cpufreq 2.45 Ghz | 82.6 % 00065 cpufreq 2.05 Ghz | 67.8 % 00066 cpufreq 1.85 Ghz | 61.4 % 00067 cpufreq 1.65 Ghz | 54.9 % 00068 cpufreq 1400 Mhz | 48.3 % 00069 cpufreq 1200 Mhz | 41.1 % 00070 cpufreq 1000 Mhz | 34.3 % 00071 cpufreq 800 Mhz | 27.4 % 00072 cpufreq Frequency | Speed 00073 cpufreq -----------+--------- 00074 cpufreq 2.45 Ghz | 96.2 % 00075 cpufreq 2.45 Ghz | 82.5 % 00076 cpufreq 2.05 Ghz | 69.3 % 00077 cpufreq 1.85 Ghz | 62.7 % 00078 cpufreq 1.65 Ghz | 55.0 % 00079 cpufreq 1400 Mhz | 47.4 % 00080 cpufreq 1200 Mhz | 41.1 % 00081 cpufreq 1000 Mhz | 34.0 % 00082 cpufreq 800 Mhz | 27.2 % 00083 cpufreq Frequency | Speed 00084 cpufreq -----------+--------- 00085 cpufreq 2.45 Ghz | 96.5 % 00086 cpufreq 2.45 Ghz | 83.6 % 00087 cpufreq 2.05 Ghz | 68.1 % 00088 cpufreq 1.85 Ghz | 61.7 % 00089 cpufreq 1.65 Ghz | 54.9 % 00090 cpufreq 1400 Mhz | 48.0 % 00091 cpufreq 1200 Mhz | 41.1 % 00092 cpufreq 1000 Mhz | 34.2 % 00093 cpufreq 800 Mhz | 27.8 % 00094 cpufreq Frequency | Speed 00095 cpufreq -----------+--------- 00096 cpufreq 2.45 Ghz | 96.4 % 00097 cpufreq 2.45 Ghz | 82.6 % 00098 cpufreq 2.05 Ghz | 68.8 % 00099 cpufreq 1.85 Ghz | 60.5 % 00100 cpufreq 1.65 Ghz | 52.4 % 00101 cpufreq 1400 Mhz | 48.8 % 00102 cpufreq 1200 Mhz | 41.1 % 00103 cpufreq 1000 Mhz | 34.2 % 00104 cpufreq 800 Mhz | 26.4 % 00105 cpufreq Frequency | Speed 00106 cpufreq -----------+--------- 00107 cpufreq 2.45 Ghz | 95.3 % 00108 cpufreq 2.45 Ghz | 82.5 % 00109 cpufreq 2.05 Ghz | 65.5 % 00110 cpufreq 1.85 Ghz | 62.8 % 00111 cpufreq 1.65 Ghz | 54.8 % 00112 cpufreq 1400 Mhz | 48.0 % 00113 cpufreq 1200 Mhz | 41.2 % 00114 cpufreq 1000 Mhz | 34.2 % 00115 cpufreq 800 Mhz | 27.3 % 00116 cpufreq Frequency | Speed 00117 cpufreq -----------+--------- 00118 cpufreq 2.45 Ghz | 96.3 % 00119 cpufreq 2.45 Ghz | 83.4 % 00120 cpufreq 2.05 Ghz | 68.3 % 00121 cpufreq 1.85 Ghz | 61.9 % 00122 cpufreq 1.65 Ghz | 54.9 % 00123 cpufreq 1400 Mhz | 48.0 % 00124 cpufreq 1200 Mhz | 41.1 % 00125 cpufreq 1000 Mhz | 34.2 % 00126 cpufreq 800 Mhz | 27.3 % 00127 cpufreq Frequency | Speed 00128 cpufreq -----------+--------- 00129 cpufreq 2.45 Ghz | 100.0 % 00130 cpufreq 2.45 Ghz | 77.9 % 00131 cpufreq 2.05 Ghz | 64.6 % 00132 cpufreq 1.85 Ghz | 54.0 % 00133 cpufreq 1.65 Ghz | 51.7 % 00134 cpufreq 1400 Mhz | 45.2 % 00135 cpufreq 1200 Mhz | 39.0 % 00136 cpufreq 1000 Mhz | 33.1 % 00137 cpufreq 800 Mhz | 25.5 % 00138 cpufreq Frequency | Speed 00139 cpufreq -----------+--------- 00140 cpufreq 2.45 Ghz | 93.4 % 00141 cpufreq 2.45 Ghz | 75.7 % 00142 cpufreq 2.05 Ghz | 64.5 % 00143 cpufreq 1.85 Ghz | 59.1 % 00144 cpufreq 1.65 Ghz | 51.4 % 00145 cpufreq 1400 Mhz | 45.9 % 00146 cpufreq 1200 Mhz | 39.3 % 00147 cpufreq 1000 Mhz | 32.7 % 00148 cpufreq 800 Mhz | 25.8 % 00149 cpufreq Frequency | Speed 00150 cpufreq -----------+--------- 00151 cpufreq 2.45 Ghz | 92.1 % 00152 cpufreq 2.45 Ghz | 78.1 % 00153 cpufreq 2.05 Ghz | 65.7 % 00154 cpufreq 1.85 Ghz | 58.6 % 00155 cpufreq 1.65 Ghz | 52.5 % 00156 cpufreq 1400 Mhz | 45.7 % 00157 cpufreq 1200 Mhz | 39.3 % 00158 cpufreq 1000 Mhz | 32.7 % 00159 cpufreq 800 Mhz | 24.3 % 00160 cpufreq Frequency | Speed 00161 cpufreq -----------+--------- 00162 cpufreq 2.45 Ghz | 88.9 % 00163 cpufreq 2.45 Ghz | 79.8 % 00164 cpufreq 2.05 Ghz | 58.4 % 00165 cpufreq 1.85 Ghz | 52.6 % 00166 cpufreq 1.65 Ghz | 46.9 % 00167 cpufreq 1400 Mhz | 41.0 % 00168 cpufreq 1200 Mhz | 35.1 % 00169 cpufreq 1000 Mhz | 29.1 % 00170 cpufreq 800 Mhz | 22.9 % 00171 cpufreq Frequency | Speed 00172 cpufreq -----------+--------- 00173 cpufreq 2.45 Ghz | 92.8 % 00174 cpufreq 2.45 Ghz | 80.1 % 00175 cpufreq 2.05 Ghz | 66.2 % 00176 cpufreq 1.85 Ghz | 59.5 % 00177 cpufreq 1.65 Ghz | 52.9 % 00178 cpufreq 1400 Mhz | 46.2 % 00179 cpufreq 1200 Mhz | 39.5 % 00180 cpufreq 1000 Mhz | 32.9 % 00181 cpufreq 800 Mhz | 26.3 % 00182 cpufreq Frequency | Speed 00183 cpufreq -----------+--------- 00184 cpufreq 2.45 Ghz | 92.9 % 00185 cpufreq 2.45 Ghz | 79.5 % 00186 cpufreq 2.05 Ghz | 66.2 % 00187 cpufreq 1.85 Ghz | 59.6 % 00188 cpufreq 1.65 Ghz | 52.9 % 00189 cpufreq 1400 Mhz | 46.7 % 00190 cpufreq 1200 Mhz | 39.6 % 00191 cpufreq 1000 Mhz | 32.9 % 00192 cpufreq 800 Mhz | 26.3 % 00193 cpufreq FAILED [MEDIUM] CPUFreqCPUsSetToSW_ANY: Test 1, Processors 00194 cpufreq are set to SW_ANY. 00195 cpufreq FAILED [MEDIUM] CPUFreqSW_ANY: Test 1, Firmware not 00196 cpufreq implementing hardware coordination cleanly. Firmware using 00197 cpufreq SW_ANY instead?. 00198 cpufreq 00199 cpufreq ========================================================= 00200 cpufreq 0 passed, 2 failed, 0 warnings, 0 aborted, 0 skipped, 0 00201 cpufreq info only. 00202 cpufreq ========================================================= 00204 summary 00205 summary 0 passed, 2 failed, 0 warnings, 0 aborted, 0 skipped, 0 00206 summary info only. 00207 summary 00208 summary Test Failure Summary 00209 summary ==================== 00210 summary 00211 summary Critical failures: NONE 00212 summary 00213 summary High failures: NONE 00214 summary 00215 summary Medium failures: 2 00216 summary cpufreq test, at 1 log line: 193 00217 summary "Processors are set to SW_ANY." 00218 summary cpufreq test, at 1 log line: 195 00219 summary "Firmware not implementing hardware coordination cleanly. Firmware using SW_ANY instead?." 00220 summary 00221 summary Low failures: NONE 00222 summary 00223 summary Other failures: NONE 00224 summary 00225 summary Test |Pass |Fail |Abort|Warn |Skip |Info | 00226 summary ---------------+-----+-----+-----+-----+-----+-----+ 00227 summary cpufreq | | 2| | | | | 00228 summary ---------------+-----+-----+-----+-----+-----+-----+ 00229 summary Total: | 0| 2| 0| 0| 0| 0| 00230 summary ---------------+-----+-----+-----+-----+-----+-----+ rabbit@rabbit-Lenovo-V570:~$

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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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  • SYN receives RST,ACK very frequently

    - by user1289508
    Hi Socket Programming experts, I am writing a proxy server on Linux for SQL Database server running on Windows. The proxy is coded using bsd sockets and in C, and it is working just fine. When I use a database client (written in JAVA, and running on a Linux box) to fire queries (with a concurrency of 100 or more) directly to the Database server, not experiencing connection resets. But through my proxy I am experiencing many connection resets. Digging deeper I came to know that connection from 'DB client' to 'Proxy' always succeeds but when the 'Proxy' tries to connect to the DB server the connection fails, due to the SYN packet getting RST,ACK. That was to give some background. The question is : Why does sometimes SYN receives RST,ACK? 'DB client(linux)' to 'Server(windows)' ---- Works fine 'DB client(linux) to 'Proxy(Linux)' to 'Server(windows)' ----- problematic I am aware that this can happen in "connection refused" case but this definitely is not that one. SYN flooding might be another scenario, but that does not explain fine behavior while firing to Server directly. I am suspecting some socket option setting may be required, that the client does before connecting and my proxy does not. Please put some light on this. Any help (links or pointers) is most appreciated. Additional info: Wrote a C client that does concurrent connections, which takes concurrency as an argument. Here are my observations: - At 5000 concurrency and above, some connects failed with 'connection refused'. - Below 2000, it works fine. But the actual problem is observed even at a concurrency of 100 or more. Note: The problem is time dependent sometimes it never comes at all and sometimes it is very frequent and DB client (directly to server) works fine at all times .

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  • Apache2 benchmarks - very poor performance

    - by andrzejp
    I have two servers on which I test the configuration of apache2. The first server: 4GB of RAM, AMD Athlon (tm) 64 X2 Dual Core Processor 5600 + Apache 2.2.3, mod_php, mpm prefork: Settings: Timeout 100 KeepAlive On MaxKeepAliveRequests 150 KeepAliveTimeout 4 <IfModule Mpm_prefork_module> StartServers 7 MinSpareServers 15 MaxSpareServers 30 MaxClients 250 MaxRequestsPerChild 2000 </ IfModule> Compiled in modules: core.c mod_log_config.c mod_logio.c prefork.c http_core.c mod_so.c Second server: 8GB of RAM, Intel (R) Core (TM) i7 CPU [email protected] Apache 2.2.9, **fcgid, mpm worker, suexec** PHP scripts are running via fcgi-wrapper Settings: Timeout 100 KeepAlive On MaxKeepAliveRequests 100 KeepAliveTimeout 4 <IfModule Mpm_worker_module> StartServers 10 MaxClients 200 MinSpareThreads 25 MaxSpareThreads 75 ThreadsPerChild 25 MaxRequestsPerChild 1000 </ IfModule> Compiled in modules: core.c mod_log_config.c mod_logio.c worker.c http_core.c mod_so.c The following test results, which are very strange! New server (dynamic content - php via fcgid+suexec): Server Software: Apache/2.2.9 Server Hostname: XXXXXXXX Server Port: 80 Document Path: XXXXXXX Document Length: 179512 bytes Concurrency Level: 10 Time taken for tests: 0.26276 seconds Complete requests: 1000 Failed requests: 0 Total transferred: 179935000 bytes HTML transferred: 179512000 bytes Requests per second: 38.06 Transfer rate: 6847.88 kb/s received Connnection Times (ms) min avg max Connect: 2 4 54 Processing: 161 257 449 Total: 163 261 503 Old server (dynamic content - mod_php): Server Software: Apache/2.2.3 Server Hostname: XXXXXX Server Port: 80 Document Path: XXXXXX Document Length: 187537 bytes Concurrency Level: 10 Time taken for tests: 173.073 seconds Complete requests: 1000 Failed requests: 22 (Connect: 0, Length: 22, Exceptions: 0) Total transferred: 188003372 bytes HTML transferred: 187546372 bytes Requests per second: 5777.91 Transfer rate: 1086267.40 kb/s received Connnection Times (ms) min avg max Connect: 3 3 28 Processing: 298 1724 26615 Total: 301 1727 26643 Old server: Static content (jpg file) Server Software: Apache/2.2.3 Server Hostname: xxxxxxxxx Server Port: 80 Document Path: /images/top2.gif Document Length: 40486 bytes Concurrency Level: 100 Time taken for tests: 3.558 seconds Complete requests: 1000 Failed requests: 0 Write errors: 0 Total transferred: 40864400 bytes HTML transferred: 40557482 bytes Requests per second: 281.09 [#/sec] (mean) Time per request: 355.753 [ms] (mean) Time per request: 3.558 [ms] (mean, across all concurrent requests) Transfer rate: 11217.51 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 3 11 4.5 12 23 Processing: 40 329 61.4 339 1009 Waiting: 6 282 55.2 293 737 Total: 43 340 63.0 351 1020 New server - static content (jpg file) Server Software: Apache/2.2.9 Server Hostname: XXXXX Server Port: 80 Document Path: /images/top2.gif Document Length: 40486 bytes Concurrency Level: 100 Time taken for tests: 3.571531 seconds Complete requests: 1000 Failed requests: 0 Write errors: 0 Total transferred: 41282792 bytes HTML transferred: 41030080 bytes Requests per second: 279.99 [#/sec] (mean) Time per request: 357.153 [ms] (mean) Time per request: 3.572 [ms] (mean, across all concurrent requests) Transfer rate: 11287.88 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 2 63 24.8 66 119 Processing: 124 278 31.8 282 391 Waiting: 3 70 28.5 66 164 Total: 126 341 35.9 350 443 I noticed that in the apache error.log is a lot of entries: [notice] mod_fcgid: call /www/XXXXX/public_html/forum/index.php with wrapper /www/php-fcgi-scripts/XXXXXX/php-fcgi-starter What I have omitted, or do not understand? Such a difference in requests per second? Is it possible? What could be the cause?

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  • Is there a quasi-standard set of attributes to annotate thread safety, immutability etc.?

    - by Eugene Beresovksy
    Except for a blog post here and there, describing the custom attributes someone created, but that do not seem to get any traction - like one describing how to enforce immutability, another one on Documenting Thread Safety, modeling the attributes after JCIP annotations - is there any standard emerging? Anything MS might be planning for the future? This is something that should be standard, if there's to be any chance of interoperability between libraries concurrency-wise. Both for documentation purposes, and also to feed static / dynamic test tools. If MS isn't doing anything in that direction, it could be done on CodePlex - but I couldn't find anything there, either. <opinion>Concurrency and thread safety are really hard in imperative and object-languages like C# and Java, we should try to tame it, until we hopefully switch to more appropriate languages.</opinion>

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  • Utility Objects–Waitfor Delay Coordinator (SQL Server 2008+)

    - by drsql
    Finally… took longer than I had expected when I wrote this a while back, but I had to move my website and get DNS moved before I could post code… When I write code, I do my best to test that code in as many ways as necessary. One of the last types of tests that is necessary is concurrency testing. Concurrency testing is one of the most difficult types of testing because it takes running multiple processes simultaneously and making sure that you get the correct answers multiple times. This is really...(read more)

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  • How can I designed multi-threaded application for larger user base

    - by rokonoid
    Here's my scenario, I need to develop a scalable application. My user base may be over 100K, every user has 10 or more small tasks. Tasks check every minute with a third party application through web services, retrieving data which is written in the database, then the data is forwarded to it's original destination. So here's the processing of a small task: while(true){ Boolean isNewInformationAvailable = checkWhetherInformationIsAvailableOrNot(); If(isNewInformationAvailable ==true){ fetchTheData(); writeToDatabase(); findTheDestination(); deliverTheData(); isAvailable =false; } } Here is the question: as the application is large, how should I approach designing this. I'm going to use Java to write it. Should I use concurrency, and how would you manage the concurrency?

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  • How can I design multi-threaded application for larger user base

    - by rokonoid
    Here's my scenario, I need to develop a scalable application. My user base may be over 100K, every user has 10 or more small tasks. Tasks check every minute with a third party application through web services, retrieving data which is written in the database, then the data is forwarded to it's original destination. So here's the processing of a small task: while(true){ Boolean isNewInformationAvailable = checkWhetherInformationIsAvailableOrNot(); If(isNewInformationAvailable ==true){ fetchTheData(); writeToDatabase(); findTheDestination(); deliverTheData(); isAvailable =false; } } As the application is large, how should I approach designing this? I'm going to use Java to write it. Should I use concurrency, and how would you manage the concurrency?

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  • Performance issues when using SSD for a developer notebook (WAMP/LAMP stack)?

    - by András Szepesházi
    I'm a web application developer using my notebook as a standalone development environment (WAMP stack). I just switched from a Core2-duo Vista 32 bit notebook with 2Gb RAM and SATA HDD, to an i5-2520M Win7 64 bit with 4Gb RAM and 128 GB SDD (Corsair P3 128). My initial experience was what I expected, fast boot, quick load of all the applications (Eclipse takes now 5 seconds as opposed to 30s on my old notebook), overall great experience. Then I started to build up my development stack, both as LAMP (using VirtualBox with a debian guest) and WAMP (windows native apache + mysql + php). I wanted to compare those two. This still all worked great out, then I started to pull in my projects to these stacks. And here came the nasty surprise, one of those projects produced a lot worse response times than on my old notebook (that was true for both the VirtualBox and WAMP stack). Apache, php and mysql configurations were practically identical in all environments. I started to do a lot of benchmarking and profiling, and here is what I've found: All general benchmarks (Performance Test 7.0, HDTune Pro, wPrime2 and some more) gave a big advantage to the new notebook. Nothing surprising here. Disc specific tests showed that read/write operations peaked around 380M/160M for the SSD, and all the different sized block operations also performed very well. Started apache performance benchmarking with Apache Benchmark for a small static html file (10 concurrent threads, 500 iterations). Old notebook: min 47ms, median 111ms, max 156ms New WAMP stack: min 71ms, median 135ms, max 296ms New LAMP stack (in VirtualBox): min 6ms, median 46ms, max 175ms Right here I don't get why the native WAMP stack performed so bad, but at least the LAMP environment brought the expected speed. Apache performance measurement for non-cached php content. The php runs a loop of 1000 and generates sha1(uniqid()) inisde. Again, 10 concurrent threads, 500 iterations were used for the benchmark. Old notebook: min 0ms, median 39ms, max 218ms New WAMP stack: min 20ms, median 61ms, max 186ms New LAMP stack (in VirtualBox): min 124ms, median 704ms, max 2463ms What the hell? The new LAMP performed miserably, and even the new native WAMP was outperformed by the old notebook. php + mysql test. The test consists of connecting to a database and reading a single record form a table using INNER JOIN on 3 more (indexed) tables, repeated 100 times within a loop. Databases were identical. 10 concurrent threads, 100 iterations were used for the benchmark. Old notebook: min 1201ms, median 1734ms, max 3728ms New WAMP stack: min 367ms, median 675ms, max 1893ms New LAMP stack (in VirtualBox): min 1410ms, median 3659ms, max 5045ms And the same test with concurrency set to 1 (instead of 10): Old notebook: min 1201ms, median 1261ms, max 1357ms New WAMP stack: min 399ms, median 483ms, max 539ms New LAMP stack (in VirtualBox): min 285ms, median 348ms, max 444ms Strictly for my purposes, as I'm using a self contained development environment (= low concurrency) I could be satisfied with the second test's result. Though I have no idea why the VirtualBox environment performed so bad with higher concurrency. Finally I performed a test of including many php files. The application that I mentioned at the beginning, the one that was performing so bad, has a heavy bootstrap, loads hundreds of small library and configuration files while initializing. So this test does nothing else just includes about 100 files. Concurrency set to 1, 100 iterations: Old notebook: min 140ms, median 168ms, max 406ms New WAMP stack: min 434ms, median 488ms, max 604ms New LAMP stack (in VirtualBox): min 413ms, median 1040ms, max 1921ms Even if I consider that VirtualBox reached those files via shared folders, and that slows things down a bit, I still don't see how could the old notebook outperform so heavily both new configurations. And I think this is the real root of the slow performance, as the application uses even more includes, and the whole bootstrap will occur several times within a page request (for each ajax call, for example). To sum it up, here I am with a brand new high-performance notebook that loads the same page in 20 seconds, that my old notebook can do in 5-7 seconds. Needless to say, I'm not a very happy person right now. Why do you think I experience these poor performance values? What are my options to remedy this situation?

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  • OSError : [Errno 38] Function not implemented - Django Celery implementation

    - by Jordan Messina
    I installed django-celery and I tried to start up the worker server but I get an OSError that a function isn't implemented. I'm running CentOS release 5.4 (Final) on a VPS: . broker -> amqp://guest@localhost:5672/ . queues -> . celery -> exchange:celery (direct) binding:celery . concurrency -> 4 . loader -> djcelery.loaders.DjangoLoader . logfile -> [stderr]@WARNING . events -> OFF . beat -> OFF [2010-07-22 17:10:01,364: WARNING/MainProcess] Traceback (most recent call last): [2010-07-22 17:10:01,364: WARNING/MainProcess] File "manage.py", line 11, in <module> [2010-07-22 17:10:01,364: WARNING/MainProcess] execute_manager(settings) [2010-07-22 17:10:01,364: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/django/core/management/__init__.py", line 438, in execute_manager [2010-07-22 17:10:01,364: WARNING/MainProcess] utility.execute() [2010-07-22 17:10:01,364: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/django/core/management/__init__.py", line 379, in execute [2010-07-22 17:10:01,365: WARNING/MainProcess] self.fetch_command(subcommand).run_from_argv(self.argv) [2010-07-22 17:10:01,365: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/django/core/management/base.py", line 191, in run_from_argv [2010-07-22 17:10:01,365: WARNING/MainProcess] self.execute(*args, **options.__dict__) [2010-07-22 17:10:01,365: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/django/core/management/base.py", line 218, in execute [2010-07-22 17:10:01,365: WARNING/MainProcess] output = self.handle(*args, **options) [2010-07-22 17:10:01,365: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/django_celery-2.0.0-py2.6.egg/djcelery/management/commands/celeryd.py", line 22, in handle [2010-07-22 17:10:01,366: WARNING/MainProcess] run_worker(**options) [2010-07-22 17:10:01,366: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/bin/celeryd.py", line 385, in run_worker [2010-07-22 17:10:01,366: WARNING/MainProcess] return Worker(**options).run() [2010-07-22 17:10:01,366: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/bin/celeryd.py", line 218, in run [2010-07-22 17:10:01,366: WARNING/MainProcess] self.run_worker() [2010-07-22 17:10:01,366: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/bin/celeryd.py", line 312, in run_worker [2010-07-22 17:10:01,367: WARNING/MainProcess] worker.start() [2010-07-22 17:10:01,367: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/worker/__init__.py", line 206, in start [2010-07-22 17:10:01,367: WARNING/MainProcess] component.start() [2010-07-22 17:10:01,367: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/concurrency/processes/__init__.py", line 54, in start [2010-07-22 17:10:01,367: WARNING/MainProcess] maxtasksperchild=self.maxtasksperchild) [2010-07-22 17:10:01,367: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/concurrency/processes/pool.py", line 448, in __init__ [2010-07-22 17:10:01,368: WARNING/MainProcess] self._setup_queues() [2010-07-22 17:10:01,368: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/concurrency/processes/pool.py", line 564, in _setup_queues [2010-07-22 17:10:01,368: WARNING/MainProcess] self._inqueue = SimpleQueue() [2010-07-22 17:10:01,368: WARNING/MainProcess] File "/usr/local/lib/python2.6/multiprocessing/queues.py", line 315, in __init__ [2010-07-22 17:10:01,368: WARNING/MainProcess] self._rlock = Lock() [2010-07-22 17:10:01,368: WARNING/MainProcess] File "/usr/local/lib/python2.6/multiprocessing/synchronize.py", line 117, in __init__ [2010-07-22 17:10:01,369: WARNING/MainProcess] SemLock.__init__(self, SEMAPHORE, 1, 1) [2010-07-22 17:10:01,369: WARNING/MainProcess] File "/usr/local/lib/python2.6/multiprocessing/synchronize.py", line 49, in __init__ [2010-07-22 17:10:01,369: WARNING/MainProcess] sl = self._semlock = _multiprocessing.SemLock(kind, value, maxvalue) [2010-07-22 17:10:01,369: WARNING/MainProcess] OSError [2010-07-22 17:10:01,369: WARNING/MainProcess] : [2010-07-22 17:10:01,369: WARNING/MainProcess] [Errno 38] Function not implemented Am I just totally screwed and should use a new kernel that has this implemented or is there an easy way to resolve this?

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  • Yet Another ASP.NET MVC CRUD Tutorial

    - by Ricardo Peres
    I know that I have not posted much on MVC, mostly because I don’t use it on my daily life, but since I find it so interesting, and since it is gaining such popularity, I will be talking about it much more. This time, it’s about the most basic of scenarios: CRUD. Although there are several ASP.NET MVC tutorials out there that cover ordinary CRUD operations, I couldn’t find any that would explain how we can have also AJAX, optimistic concurrency control and validation, using Entity Framework Code First, so I set out to write one! I won’t go into explaining what is MVC, Code First or optimistic concurrency control, or AJAX, I assume you are all familiar with these concepts by now. Let’s consider an hypothetical use case, products. For simplicity, we only want to be able to either view a single product or edit this product. First, we need our model: 1: public class Product 2: { 3: public Product() 4: { 5: this.Details = new HashSet<OrderDetail>(); 6: } 7:  8: [Required] 9: [StringLength(50)] 10: public String Name 11: { 12: get; 13: set; 14: } 15:  16: [Key] 17: [ScaffoldColumn(false)] 18: [DatabaseGenerated(DatabaseGeneratedOption.Identity)] 19: public Int32 ProductId 20: { 21: get; 22: set; 23: } 24:  25: [Required] 26: [Range(1, 100)] 27: public Decimal Price 28: { 29: get; 30: set; 31: } 32:  33: public virtual ISet<OrderDetail> Details 34: { 35: get; 36: protected set; 37: } 38:  39: [Timestamp] 40: [ScaffoldColumn(false)] 41: public Byte[] RowVersion 42: { 43: get; 44: set; 45: } 46: } Keep in mind that this is a simple scenario. Let’s see what we have: A class Product, that maps to a product record on the database; A product has a required (RequiredAttribute) Name property which can contain up to 50 characters (StringLengthAttribute); The product’s Price must be a decimal value between 1 and 100 (RangeAttribute); It contains a set of order details, for each time that it has been ordered, which we will not talk about (Details); The record’s primary key (mapped to property ProductId) comes from a SQL Server IDENTITY column generated by the database (KeyAttribute, DatabaseGeneratedAttribute); The table uses a SQL Server ROWVERSION (previously known as TIMESTAMP) column for optimistic concurrency control mapped to property RowVersion (TimestampAttribute). Then we will need a controller for viewing product details, which will located on folder ~/Controllers under the name ProductController: 1: public class ProductController : Controller 2: { 3: [HttpGet] 4: public ViewResult Get(Int32 id = 0) 5: { 6: if (id != 0) 7: { 8: using (ProductContext ctx = new ProductContext()) 9: { 10: return (this.View("Single", ctx.Products.Find(id) ?? new Product())); 11: } 12: } 13: else 14: { 15: return (this.View("Single", new Product())); 16: } 17: } 18: } If the requested product does not exist, or one was not requested at all, one with default values will be returned. I am using a view named Single to display the product’s details, more on that later. As you can see, it delegates the loading of products to an Entity Framework context, which is defined as: 1: public class ProductContext: DbContext 2: { 3: public DbSet<Product> Products 4: { 5: get; 6: set; 7: } 8: } Like I said before, I’ll keep it simple for now, only aggregate root Product is available. The controller will use the standard routes defined by the Visual Studio ASP.NET MVC 3 template: 1: routes.MapRoute( 2: "Default", // Route name 3: "{controller}/{action}/{id}", // URL with parameters 4: new { controller = "Home", action = "Index", id = UrlParameter.Optional } // Parameter defaults 5: ); Next, we need a view for displaying the product details, let’s call it Single, and have it located under ~/Views/Product: 1: <%@ Page Language="C#" Inherits="System.Web.Mvc.ViewPage<Product>" %> 2: <!DOCTYPE html> 3:  4: <html> 5: <head runat="server"> 6: <title>Product</title> 7: <script src="/Scripts/jquery-1.7.2.js" type="text/javascript"></script> 1:  2: <script src="/Scripts/jquery-ui-1.8.19.js" type="text/javascript"> 1: </script> 2: <script src="/Scripts/jquery.unobtrusive-ajax.js" type="text/javascript"> 1: </script> 2: <script src="/Scripts/jquery.validate.js" type="text/javascript"> 1: </script> 2: <script src="/Scripts/jquery.validate.unobtrusive.js" type="text/javascript"> 1: </script> 2: <script type="text/javascript"> 3: function onFailure(error) 4: { 5: } 6:  7: function onComplete(ctx) 8: { 9: } 10:  11: </script> 8: </head> 9: <body> 10: <div> 11: <% 1: : this.Html.ValidationSummary(false) %> 12: <% 1: using (this.Ajax.BeginForm("Edit", "Product", new AjaxOptions{ HttpMethod = FormMethod.Post.ToString(), OnSuccess = "onSuccess", OnFailure = "onFailure" })) { %> 13: <% 1: : this.Html.EditorForModel() %> 14: <input type="submit" name="submit" value="Submit" /> 15: <% 1: } %> 16: </div> 17: </body> 18: </html> Yes… I am using ASPX syntax… sorry about that!   I implemented an editor template for the Product class, which must be located on the ~/Views/Shared/EditorTemplates folder as file Product.ascx: 1: <%@ Control Language="C#" Inherits="System.Web.Mvc.ViewUserControl<Product>" %> 2: <div> 3: <%: this.Html.HiddenFor(model => model.ProductId) %> 4: <%: this.Html.HiddenFor(model => model.RowVersion) %> 5: <fieldset> 6: <legend>Product</legend> 7: <div class="editor-label"> 8: <%: this.Html.LabelFor(model => model.Name) %> 9: </div> 10: <div class="editor-field"> 11: <%: this.Html.TextBoxFor(model => model.Name) %> 12: <%: this.Html.ValidationMessageFor(model => model.Name) %> 13: </div> 14: <div class="editor-label"> 15: <%= this.Html.LabelFor(model => model.Price) %> 16: </div> 17: <div class="editor-field"> 18: <%= this.Html.TextBoxFor(model => model.Price) %> 19: <%: this.Html.ValidationMessageFor(model => model.Price) %> 20: </div> 21: </fieldset> 22: </div> One thing you’ll notice is, I am including both the ProductId and the RowVersion properties as hidden fields; they will come handy later or, so that we know what product and version we are editing. The other thing is the included JavaScript files: jQuery, jQuery UI and unobtrusive validations. Also, I am not using the Content extension method for translating relative URLs, because that way I would lose JavaScript intellisense for jQuery functions. OK, so, at this moment, I want to add support for AJAX and optimistic concurrency control. So I write a controller method like this: 1: [HttpPost] 2: [AjaxOnly] 3: [Authorize] 4: public JsonResult Edit(Product product) 5: { 6: if (this.TryValidateModel(product) == true) 7: { 8: using (BlogContext ctx = new BlogContext()) 9: { 10: Boolean success = false; 11:  12: ctx.Entry(product).State = (product.ProductId == 0) ? EntityState.Added : EntityState.Modified; 13:  14: try 15: { 16: success = (ctx.SaveChanges() == 1); 17: } 18: catch (DbUpdateConcurrencyException) 19: { 20: ctx.Entry(product).Reload(); 21: } 22:  23: return (this.Json(new { Success = success, ProductId = product.ProductId, RowVersion = Convert.ToBase64String(product.RowVersion) })); 24: } 25: } 26: else 27: { 28: return (this.Json(new { Success = false, ProductId = 0, RowVersion = String.Empty })); 29: } 30: } So, this method is only valid for HTTP POST requests (HttpPost), coming from AJAX (AjaxOnly, from MVC Futures), and from authenticated users (Authorize). It returns a JSON object, which is what you would normally use for AJAX requests, containing three properties: Success: a boolean flag; RowVersion: the current version of the ROWVERSION column as a Base-64 string; ProductId: the inserted product id, as coming from the database. If the product is new, it will be inserted into the database, and its primary key will be returned into the ProductId property. Success will be set to true; If a DbUpdateConcurrencyException occurs, it means that the value in the RowVersion property does not match the current ROWVERSION column value on the database, so the record must have been modified between the time that the page was loaded and the time we attempted to save the product. In this case, the controller just gets the new value from the database and returns it in the JSON object; Success will be false. Otherwise, it will be updated, and Success, ProductId and RowVersion will all have their values set accordingly. So let’s see how we can react to these situations on the client side. Specifically, we want to deal with these situations: The user is not logged in when the update/create request is made, perhaps the cookie expired; The optimistic concurrency check failed; All went well. So, let’s change our view: 1: <%@ Page Language="C#" Inherits="System.Web.Mvc.ViewPage<Product>" %> 2: <%@ Import Namespace="System.Web.Security" %> 3:  4: <!DOCTYPE html> 5:  6: <html> 7: <head runat="server"> 8: <title>Product</title> 9: <script src="/Scripts/jquery-1.7.2.js" type="text/javascript"></script> 1:  2: <script src="/Scripts/jquery-ui-1.8.19.js" type="text/javascript"> 1: </script> 2: <script src="/Scripts/jquery.unobtrusive-ajax.js" type="text/javascript"> 1: </script> 2: <script src="/Scripts/jquery.validate.js" type="text/javascript"> 1: </script> 2: <script src="/Scripts/jquery.validate.unobtrusive.js" type="text/javascript"> 1: </script> 2: <script type="text/javascript"> 3: function onFailure(error) 4: { 5: window.alert('An error occurred: ' + error); 6: } 7:  8: function onSuccess(ctx) 9: { 10: if (typeof (ctx.Success) != 'undefined') 11: { 12: $('input#ProductId').val(ctx.ProductId); 13: $('input#RowVersion').val(ctx.RowVersion); 14:  15: if (ctx.Success == false) 16: { 17: window.alert('An error occurred while updating the entity: it may have been modified by third parties. Please try again.'); 18: } 19: else 20: { 21: window.alert('Saved successfully'); 22: } 23: } 24: else 25: { 26: if (window.confirm('Not logged in. Login now?') == true) 27: { 28: document.location.href = '<%: FormsAuthentication.LoginUrl %>?ReturnURL=' + document.location.pathname; 29: } 30: } 31: } 32:  33: </script> 10: </head> 11: <body> 12: <div> 13: <% 1: : this.Html.ValidationSummary(false) %> 14: <% 1: using (this.Ajax.BeginForm("Edit", "Product", new AjaxOptions{ HttpMethod = FormMethod.Post.ToString(), OnSuccess = "onSuccess", OnFailure = "onFailure" })) { %> 15: <% 1: : this.Html.EditorForModel() %> 16: <input type="submit" name="submit" value="Submit" /> 17: <% 1: } %> 18: </div> 19: </body> 20: </html> The implementation of the onSuccess function first checks if the response contains a Success property, if not, the most likely cause is the request was redirected to the login page (using Forms Authentication), because it wasn’t authenticated, so we navigate there as well, keeping the reference to the current page. It then saves the current values of the ProductId and RowVersion properties to their respective hidden fields. They will be sent on each successive post and will be used in determining if the request is for adding a new product or to updating an existing one. The only thing missing is the ability to insert a new product, after inserting/editing an existing one, which can be easily achieved using this snippet: 1: <input type="button" value="New" onclick="$('input#ProductId').val('');$('input#RowVersion').val('');"/> And that’s it.

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  • vJUG: Worldwide Virtual JUG Created

    - by Tori Wieldt
    London Java Community leader and technical evangelist Simon Maple has created a Meetup called vJUG, with aim toward connecting Java Developers in the virtual world. The aim for vJUG is: Get technical leaders from around the world to present to the vJUG members (without travel cost concerns!). Work with local JUGs to provide worldwide content to their members and help JUGs present to a worldwide audience. Provide content to devs without access to a local JUG. Be a hub that will stream content from other JUG sessions live.  The vJUG is not intended to replace local JUG efforts. "The vJUG can never be, and will never be, as vibrant and valuable to its members as a proper local JUG can. Why? Because the true value in JUG meetings are the face to face interactions and personal networking," said Maple. "However, many people do not have access to a really active JUG with great speakers and awesome content. Or, like me, the closest JUG is about 90 mins away." WebEx and Google Hangouts are great, Maple explained, he hopes vJUG will provide more coordination of online events.  Maple hopes that in the future, vJUG will provide An Events calendar with reminders and links to up coming meetings. A Newsletter with what's coming up and links to previous sessions. Coordination of links to IRC channels which are active during presentations (to create a feeling of virtual community). Comments and forums around sessions and presentations A place where physical JUGs could advertise their sessions (i.e. a NY JUG event) to a worldwide audience, when streamed, via an event that people can sign up to. A common Webex or Hangout. Maple encourages both people who need a JUG and existing JUG members to join vJUG. "I'm looking forward to talking with many of you one to get members, speakers, and JUG support!" Join vJUG now! (I sense a need for a logo...) 

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  • PTLQueue : a scalable bounded-capacity MPMC queue

    - by Dave
    Title: Fast concurrent MPMC queue -- I've used the following concurrent queue algorithm enough that it warrants a blog entry. I'll sketch out the design of a fast and scalable multiple-producer multiple-consumer (MPSC) concurrent queue called PTLQueue. The queue has bounded capacity and is implemented via a circular array. Bounded capacity can be a useful property if there's a mismatch between producer rates and consumer rates where an unbounded queue might otherwise result in excessive memory consumption by virtue of the container nodes that -- in some queue implementations -- are used to hold values. A bounded-capacity queue can provide flow control between components. Beware, however, that bounded collections can also result in resource deadlock if abused. The put() and take() operators are partial and wait for the collection to become non-full or non-empty, respectively. Put() and take() do not allocate memory, and are not vulnerable to the ABA pathologies. The PTLQueue algorithm can be implemented equally well in C/C++ and Java. Partial operators are often more convenient than total methods. In many use cases if the preconditions aren't met, there's nothing else useful the thread can do, so it may as well wait via a partial method. An exception is in the case of work-stealing queues where a thief might scan a set of queues from which it could potentially steal. Total methods return ASAP with a success-failure indication. (It's tempting to describe a queue or API as blocking or non-blocking instead of partial or total, but non-blocking is already an overloaded concurrency term. Perhaps waiting/non-waiting or patient/impatient might be better terms). It's also trivial to construct partial operators by busy-waiting via total operators, but such constructs may be less efficient than an operator explicitly and intentionally designed to wait. A PTLQueue instance contains an array of slots, where each slot has volatile Turn and MailBox fields. The array has power-of-two length allowing mod/div operations to be replaced by masking. We assume sensible padding and alignment to reduce the impact of false sharing. (On x86 I recommend 128-byte alignment and padding because of the adjacent-sector prefetch facility). Each queue also has PutCursor and TakeCursor cursor variables, each of which should be sequestered as the sole occupant of a cache line or sector. You can opt to use 64-bit integers if concerned about wrap-around aliasing in the cursor variables. Put(null) is considered illegal, but the caller or implementation can easily check for and convert null to a distinguished non-null proxy value if null happens to be a value you'd like to pass. Take() will accordingly convert the proxy value back to null. An advantage of PTLQueue is that you can use atomic fetch-and-increment for the partial methods. We initialize each slot at index I with (Turn=I, MailBox=null). Both cursors are initially 0. All shared variables are considered "volatile" and atomics such as CAS and AtomicFetchAndIncrement are presumed to have bidirectional fence semantics. Finally T is the templated type. I've sketched out a total tryTake() method below that allows the caller to poll the queue. tryPut() has an analogous construction. Zebra stripping : alternating row colors for nice-looking code listings. See also google code "prettify" : https://code.google.com/p/google-code-prettify/ Prettify is a javascript module that yields the HTML/CSS/JS equivalent of pretty-print. -- pre:nth-child(odd) { background-color:#ff0000; } pre:nth-child(even) { background-color:#0000ff; } border-left: 11px solid #ccc; margin: 1.7em 0 1.7em 0.3em; background-color:#BFB; font-size:12px; line-height:65%; " // PTLQueue : Put(v) : // producer : partial method - waits as necessary assert v != null assert Mask = 1 && (Mask & (Mask+1)) == 0 // Document invariants // doorway step // Obtain a sequence number -- ticket // As a practical concern the ticket value is temporally unique // The ticket also identifies and selects a slot auto tkt = AtomicFetchIncrement (&PutCursor, 1) slot * s = &Slots[tkt & Mask] // waiting phase : // wait for slot's generation to match the tkt value assigned to this put() invocation. // The "generation" is implicitly encoded as the upper bits in the cursor // above those used to specify the index : tkt div (Mask+1) // The generation serves as an epoch number to identify a cohort of threads // accessing disjoint slots while s-Turn != tkt : Pause assert s-MailBox == null s-MailBox = v // deposit and pass message Take() : // consumer : partial method - waits as necessary auto tkt = AtomicFetchIncrement (&TakeCursor,1) slot * s = &Slots[tkt & Mask] // 2-stage waiting : // First wait for turn for our generation // Acquire exclusive "take" access to slot's MailBox field // Then wait for the slot to become occupied while s-Turn != tkt : Pause // Concurrency in this section of code is now reduced to just 1 producer thread // vs 1 consumer thread. // For a given queue and slot, there will be most one Take() operation running // in this section. // Consumer waits for producer to arrive and make slot non-empty // Extract message; clear mailbox; advance Turn indicator // We have an obvious happens-before relation : // Put(m) happens-before corresponding Take() that returns that same "m" for T v = s-MailBox if v != null : s-MailBox = null ST-ST barrier s-Turn = tkt + Mask + 1 // unlock slot to admit next producer and consumer return v Pause tryTake() : // total method - returns ASAP with failure indication for auto tkt = TakeCursor slot * s = &Slots[tkt & Mask] if s-Turn != tkt : return null T v = s-MailBox // presumptive return value if v == null : return null // ratify tkt and v values and commit by advancing cursor if CAS (&TakeCursor, tkt, tkt+1) != tkt : continue s-MailBox = null ST-ST barrier s-Turn = tkt + Mask + 1 return v The basic idea derives from the Partitioned Ticket Lock "PTL" (US20120240126-A1) and the MultiLane Concurrent Bag (US8689237). The latter is essentially a circular ring-buffer where the elements themselves are queues or concurrent collections. You can think of the PTLQueue as a partitioned ticket lock "PTL" augmented to pass values from lock to unlock via the slots. Alternatively, you could conceptualize of PTLQueue as a degenerate MultiLane bag where each slot or "lane" consists of a simple single-word MailBox instead of a general queue. Each lane in PTLQueue also has a private Turn field which acts like the Turn (Grant) variables found in PTL. Turn enforces strict FIFO ordering and restricts concurrency on the slot mailbox field to at most one simultaneous put() and take() operation. PTL uses a single "ticket" variable and per-slot Turn (grant) fields while MultiLane has distinct PutCursor and TakeCursor cursors and abstract per-slot sub-queues. Both PTL and MultiLane advance their cursor and ticket variables with atomic fetch-and-increment. PTLQueue borrows from both PTL and MultiLane and has distinct put and take cursors and per-slot Turn fields. Instead of a per-slot queues, PTLQueue uses a simple single-word MailBox field. PutCursor and TakeCursor act like a pair of ticket locks, conferring "put" and "take" access to a given slot. PutCursor, for instance, assigns an incoming put() request to a slot and serves as a PTL "Ticket" to acquire "put" permission to that slot's MailBox field. To better explain the operation of PTLQueue we deconstruct the operation of put() and take() as follows. Put() first increments PutCursor obtaining a new unique ticket. That ticket value also identifies a slot. Put() next waits for that slot's Turn field to match that ticket value. This is tantamount to using a PTL to acquire "put" permission on the slot's MailBox field. Finally, having obtained exclusive "put" permission on the slot, put() stores the message value into the slot's MailBox. Take() similarly advances TakeCursor, identifying a slot, and then acquires and secures "take" permission on a slot by waiting for Turn. Take() then waits for the slot's MailBox to become non-empty, extracts the message, and clears MailBox. Finally, take() advances the slot's Turn field, which releases both "put" and "take" access to the slot's MailBox. Note the asymmetry : put() acquires "put" access to the slot, but take() releases that lock. At any given time, for a given slot in a PTLQueue, at most one thread has "put" access and at most one thread has "take" access. This restricts concurrency from general MPMC to 1-vs-1. We have 2 ticket locks -- one for put() and one for take() -- each with its own "ticket" variable in the form of the corresponding cursor, but they share a single "Grant" egress variable in the form of the slot's Turn variable. Advancing the PutCursor, for instance, serves two purposes. First, we obtain a unique ticket which identifies a slot. Second, incrementing the cursor is the doorway protocol step to acquire the per-slot mutual exclusion "put" lock. The cursors and operations to increment those cursors serve double-duty : slot-selection and ticket assignment for locking the slot's MailBox field. At any given time a slot MailBox field can be in one of the following states: empty with no pending operations -- neutral state; empty with one or more waiting take() operations pending -- deficit; occupied with no pending operations; occupied with one or more waiting put() operations -- surplus; empty with a pending put() or pending put() and take() operations -- transitional; or occupied with a pending take() or pending put() and take() operations -- transitional. The partial put() and take() operators can be implemented with an atomic fetch-and-increment operation, which may confer a performance advantage over a CAS-based loop. In addition we have independent PutCursor and TakeCursor cursors. Critically, a put() operation modifies PutCursor but does not access the TakeCursor and a take() operation modifies the TakeCursor cursor but does not access the PutCursor. This acts to reduce coherence traffic relative to some other queue designs. It's worth noting that slow threads or obstruction in one slot (or "lane") does not impede or obstruct operations in other slots -- this gives us some degree of obstruction isolation. PTLQueue is not lock-free, however. The implementation above is expressed with polite busy-waiting (Pause) but it's trivial to implement per-slot parking and unparking to deschedule waiting threads. It's also easy to convert the queue to a more general deque by replacing the PutCursor and TakeCursor cursors with Left/Front and Right/Back cursors that can move either direction. Specifically, to push and pop from the "left" side of the deque we would decrement and increment the Left cursor, respectively, and to push and pop from the "right" side of the deque we would increment and decrement the Right cursor, respectively. We used a variation of PTLQueue for message passing in our recent OPODIS 2013 paper. ul { list-style:none; padding-left:0; padding:0; margin:0; margin-left:0; } ul#myTagID { padding: 0px; margin: 0px; list-style:none; margin-left:0;} -- -- There's quite a bit of related literature in this area. I'll call out a few relevant references: Wilson's NYU Courant Institute UltraComputer dissertation from 1988 is classic and the canonical starting point : Operating System Data Structures for Shared-Memory MIMD Machines with Fetch-and-Add. Regarding provenance and priority, I think PTLQueue or queues effectively equivalent to PTLQueue have been independently rediscovered a number of times. See CB-Queue and BNPBV, below, for instance. But Wilson's dissertation anticipates the basic idea and seems to predate all the others. Gottlieb et al : Basic Techniques for the Efficient Coordination of Very Large Numbers of Cooperating Sequential Processors Orozco et al : CB-Queue in Toward high-throughput algorithms on many-core architectures which appeared in TACO 2012. Meneghin et al : BNPVB family in Performance evaluation of inter-thread communication mechanisms on multicore/multithreaded architecture Dmitry Vyukov : bounded MPMC queue (highly recommended) Alex Otenko : US8607249 (highly related). John Mellor-Crummey : Concurrent queues: Practical fetch-and-phi algorithms. Technical Report 229, Department of Computer Science, University of Rochester Thomasson : FIFO Distributed Bakery Algorithm (very similar to PTLQueue). Scott and Scherer : Dual Data Structures I'll propose an optimization left as an exercise for the reader. Say we wanted to reduce memory usage by eliminating inter-slot padding. Such padding is usually "dark" memory and otherwise unused and wasted. But eliminating the padding leaves us at risk of increased false sharing. Furthermore lets say it was usually the case that the PutCursor and TakeCursor were numerically close to each other. (That's true in some use cases). We might still reduce false sharing by incrementing the cursors by some value other than 1 that is not trivially small and is coprime with the number of slots. Alternatively, we might increment the cursor by one and mask as usual, resulting in a logical index. We then use that logical index value to index into a permutation table, yielding an effective index for use in the slot array. The permutation table would be constructed so that nearby logical indices would map to more distant effective indices. (Open question: what should that permutation look like? Possibly some perversion of a Gray code or De Bruijn sequence might be suitable). As an aside, say we need to busy-wait for some condition as follows : "while C == 0 : Pause". Lets say that C is usually non-zero, so we typically don't wait. But when C happens to be 0 we'll have to spin for some period, possibly brief. We can arrange for the code to be more machine-friendly with respect to the branch predictors by transforming the loop into : "if C == 0 : for { Pause; if C != 0 : break; }". Critically, we want to restructure the loop so there's one branch that controls entry and another that controls loop exit. A concern is that your compiler or JIT might be clever enough to transform this back to "while C == 0 : Pause". You can sometimes avoid this by inserting a call to a some type of very cheap "opaque" method that the compiler can't elide or reorder. On Solaris, for instance, you could use :"if C == 0 : { gethrtime(); for { Pause; if C != 0 : break; }}". It's worth noting the obvious duality between locks and queues. If you have strict FIFO lock implementation with local spinning and succession by direct handoff such as MCS or CLH,then you can usually transform that lock into a queue. Hidden commentary and annotations - invisible : * And of course there's a well-known duality between queues and locks, but I'll leave that topic for another blog post. * Compare and contrast : PTLQ vs PTL and MultiLane * Equivalent : Turn; seq; sequence; pos; position; ticket * Put = Lock; Deposit Take = identify and reserve slot; wait; extract & clear; unlock * conceptualize : Distinct PutLock and TakeLock implemented as ticket lock or PTL Distinct arrival cursors but share per-slot "Turn" variable provides exclusive role-based access to slot's mailbox field put() acquires exclusive access to a slot for purposes of "deposit" assigns slot round-robin and then acquires deposit access rights/perms to that slot take() acquires exclusive access to slot for purposes of "withdrawal" assigns slot round-robin and then acquires withdrawal access rights/perms to that slot At any given time, only one thread can have withdrawal access to a slot at any given time, only one thread can have deposit access to a slot Permissible for T1 to have deposit access and T2 to simultaneously have withdrawal access * round-robin for the purposes of; role-based; access mode; access role mailslot; mailbox; allocate/assign/identify slot rights; permission; license; access permission; * PTL/Ticket hybrid Asymmetric usage ; owner oblivious lock-unlock pairing K-exclusion add Grant cursor pass message m from lock to unlock via Slots[] array Cursor performs 2 functions : + PTL ticket + Assigns request to slot in round-robin fashion Deconstruct protocol : explication put() : allocate slot in round-robin fashion acquire PTL for "put" access store message into slot associated with PTL index take() : Acquire PTL for "take" access // doorway step seq = fetchAdd (&Grant, 1) s = &Slots[seq & Mask] // waiting phase while s-Turn != seq : pause Extract : wait for s-mailbox to be full v = s-mailbox s-mailbox = null Release PTL for both "put" and "take" access s-Turn = seq + Mask + 1 * Slot round-robin assignment and lock "doorway" protocol leverage the same cursor and FetchAdd operation on that cursor FetchAdd (&Cursor,1) + round-robin slot assignment and dispersal + PTL/ticket lock "doorway" step waiting phase is via "Turn" field in slot * PTLQueue uses 2 cursors -- put and take. Acquire "put" access to slot via PTL-like lock Acquire "take" access to slot via PTL-like lock 2 locks : put and take -- at most one thread can access slot's mailbox Both locks use same "turn" field Like multilane : 2 cursors : put and take slot is simple 1-capacity mailbox instead of queue Borrow per-slot turn/grant from PTL Provides strict FIFO Lock slot : put-vs-put take-vs-take at most one put accesses slot at any one time at most one put accesses take at any one time reduction to 1-vs-1 instead of N-vs-M concurrency Per slot locks for put/take Release put/take by advancing turn * is instrumental in ... * P-V Semaphore vs lock vs K-exclusion * See also : FastQueues-excerpt.java dice-etc/queue-mpmc-bounded-blocking-circular-xadd/ * PTLQueue is the same as PTLQB - identical * Expedient return; ASAP; prompt; immediately * Lamport's Bakery algorithm : doorway step then waiting phase Threads arriving at doorway obtain a unique ticket number Threads enter in ticket order * In the terminology of Reed and Kanodia a ticket lock corresponds to the busy-wait implementation of a semaphore using an eventcount and a sequencer It can also be thought of as an optimization of Lamport's bakery lock was designed for fault-tolerance rather than performance Instead of spinning on the release counter, processors using a bakery lock repeatedly examine the tickets of their peers --

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  • RHEL - blocked FC remote port time out: saving binding

    - by Dev G
    My Server went into a faulty state since the database could not write on the partition. I found out that the partition went into Read Only mode. Finally to fix it, I had to do a hard reboot. Linux 2.6.18-164.el5PAE #1 SMP Tue Aug 18 15:59:11 EDT 2009 i686 i686 i386 GNU/Linux /var/log/messages Oct 31 00:56:45 ota3g1 Had[17275]: VCS ERROR V-16-1-10214 Concurrency Violation:CurrentCount increased above 1 for failover group sg_network Oct 31 00:57:05 ota3g1 Had[17275]: VCS CRITICAL V-16-1-50086 CPU usage on ota3g1.mtsallstream.com is 100% Oct 31 01:01:47 ota3g1 Had[17275]: VCS ERROR V-16-1-10214 Concurrency Violation:CurrentCount increased above 1 for failover group sg_network Oct 31 01:06:50 ota3g1 Had[17275]: VCS ERROR V-16-1-10214 Concurrency Violation:CurrentCount increased above 1 for failover group sg_network Oct 31 01:11:52 ota3g1 Had[17275]: VCS ERROR V-16-1-10214 Concurrency Violation:CurrentCount increased above 1 for failover group sg_network Oct 31 01:12:10 ota3g1 kernel: lpfc 0000:29:00.1: 1:1305 Link Down Event x2 received Data: x2 x20 x80000 x0 x0 Oct 31 01:12:10 ota3g1 kernel: lpfc 0000:29:00.1: 1:1303 Link Up Event x3 received Data: x3 x1 x10 x1 x0 x0 0 Oct 31 01:12:12 ota3g1 kernel: lpfc 0000:29:00.1: 1:1305 Link Down Event x4 received Data: x4 x20 x80000 x0 x0 Oct 31 01:12:40 ota3g1 kernel: rport-8:0-0: blocked FC remote port time out: saving binding Oct 31 01:12:40 ota3g1 kernel: lpfc 0000:29:00.1: 1:(0):0203 Devloss timeout on WWPN 20:25:00:a0:b8:74:f5:65 NPort x0000e4 Data: x0 x7 x0 Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 38617577 Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 283532153 Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 90825 Oct 31 01:12:40 ota3g1 kernel: Aborting journal on device dm-16. Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 868841 Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: Aborting journal on device dm-10. Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 37759889 Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 283349449 Oct 31 01:12:40 ota3g1 kernel: printk: 6 messages suppressed. Oct 31 01:12:40 ota3g1 kernel: Aborting journal on device dm-12. Oct 31 01:12:40 ota3g1 kernel: EXT3-fs error (device dm-12) in ext3_reserve_inode_write: Journal has aborted Oct 31 01:12:40 ota3g1 kernel: Buffer I/O error on device dm-16, logical block 1545 Oct 31 01:12:40 ota3g1 kernel: lost page write due to I/O error on dm-16 Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 12745 Oct 31 01:12:40 ota3g1 kernel: Buffer I/O error on device dm-10, logical block 1545 Oct 31 01:12:40 ota3g1 kernel: EXT3-fs error (device dm-16) in ext3_reserve_inode_write: Journal has aborted Oct 31 01:12:40 ota3g1 kernel: lost page write due to I/O error on dm-10 Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 37749121 Oct 31 01:12:40 ota3g1 kernel: Buffer I/O error on device dm-12, logical block 0 Oct 31 01:12:40 ota3g1 kernel: lost page write due to I/O error on dm-12 Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: EXT3-fs error (device dm-12) in ext3_dirty_inode: Journal has aborted Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 37757897 Oct 31 01:12:40 ota3g1 kernel: Buffer I/O error on device dm-12, logical block 1097 Oct 31 01:12:40 ota3g1 kernel: lost page write due to I/O error on dm-12 Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 283337089 Oct 31 01:12:40 ota3g1 kernel: Buffer I/O error on device dm-16, logical block 0 Oct 31 01:12:40 ota3g1 kernel: lost page write due to I/O error on dm-16 Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: EXT3-fs error (device dm-16) in ext3_dirty_inode: Journal has aborted Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 37749121 Oct 31 01:12:40 ota3g1 kernel: Buffer I/O error on device dm-12, logical block 0 Oct 31 01:12:41 ota3g1 kernel: lost page write due to I/O error on dm-12 Oct 31 01:12:41 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:41 ota3g1 kernel: end_request: I/O error, dev sdi, sector 283337089 Oct 31 01:12:41 ota3g1 kernel: Buffer I/O error on device dm-16, logical block 0 Oct 31 01:12:41 ota3g1 kernel: lost page write due to I/O error on dm-16 Oct 31 01:12:41 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 df -h Filesystem Size Used Avail Use% Mounted on /dev/mapper/cciss-root 4.9G 730M 3.9G 16% / /dev/mapper/cciss-home 9.7G 1.2G 8.1G 13% /home /dev/mapper/cciss-var 9.7G 494M 8.8G 6% /var /dev/mapper/cciss-usr 15G 2.6G 12G 19% /usr /dev/mapper/cciss-tmp 3.9G 153M 3.6G 5% /tmp /dev/sda1 996M 43M 902M 5% /boot tmpfs 5.9G 0 5.9G 0% /dev/shm /dev/mapper/cciss-product 25G 16G 7.4G 68% /product /dev/mapper/cciss-opt 20G 4.5G 14G 25% /opt /dev/mapper/dg_db1-vol_db1_system 18G 2.2G 15G 14% /database/OTADB/sys /dev/mapper/dg_db1-vol_db1_undo 18G 5.8G 12G 35% /database/OTADB/undo /dev/mapper/dg_db1-vol_db1_redo 8.9G 4.3G 4.2G 51% /database/OTADB/redo /dev/mapper/dg_db1-vol_db1_sgbd 8.9G 654M 7.8G 8% /database/OTADB/admin /dev/mapper/dg_db1-vol_db1_arch 98G 24G 69G 26% /database/OTADB/arch /dev/mapper/dg_db1-vol_db1_indexes 240G 14G 214G 6% /database/OTADB/index /dev/mapper/dg_db1-vol_db1_data 275G 47G 215G 18% /database/OTADB/data /dev/mapper/dg_dbrman-vol_db_rman 8.9G 351M 8.1G 5% /database/RMAN /dev/mapper/dg_app1-vol_app1 151G 113G 31G 79% /files/ota /etc/fstab /dev/cciss/root / ext3 defaults 1 1 /dev/cciss/home /home ext3 defaults 1 2 /dev/cciss/var /var ext3 defaults 1 2 /dev/cciss/usr /usr ext3 defaults 1 2 /dev/cciss/tmp /tmp ext3 defaults 1 2 LABEL=/boot /boot ext3 defaults 1 2 tmpfs /dev/shm tmpfs defaults 0 0 devpts /dev/pts devpts gid=5,mode=620 0 0 sysfs /sys sysfs defaults 0 0 proc /proc proc defaults 0 0 /dev/cciss/swap swap swap defaults 0 0 /dev/cciss/product /product ext3 defaults 1 2 /dev/cciss/opt /opt ext3 defaults 1 2 /dev/dg_db1/vol_db1_system /database/OTADB/sys ext3 defaults 1 2 /dev/dg_db1/vol_db1_undo /database/OTADB/undo ext3 defaults 1 2 /dev/dg_db1/vol_db1_redo /database/OTADB/redo ext3 defaults 1 2 /dev/dg_db1/vol_db1_sgbd /database/OTADB/admin ext3 defaults 1 2 /dev/dg_db1/vol_db1_arch /database/OTADB/arch ext3 defaults 1 2 /dev/dg_db1/vol_db1_indexes /database/OTADB/index ext3 defaults 1 2 /dev/dg_db1/vol_db1_data /database/OTADB/data ext3 defaults 1 2 /dev/dg_dbrman/vol_db_rman /database/RMAN ext3 defaults 1 2 /dev/dg_app1/vol_app1 /files/ota ext3 defaults 1 2 Thanks for all the help.

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  • array and array_view from amp.h

    - by Daniel Moth
    This is a very long post, but it also covers what are probably the classes (well, array_view at least) that you will use the most with C++ AMP, so I hope you enjoy it! Overview The concurrency::array and concurrency::array_view template classes represent multi-dimensional data of type T, of N dimensions, specified at compile time (and you can later access the number of dimensions via the rank property). If N is not specified, it is assumed that it is 1 (i.e. single-dimensional case). They are rectangular (not jagged). The difference between them is that array is a container of data, whereas array_view is a wrapper of a container of data. So in that respect, array behaves like an STL container, whereas the closest thing an array_view behaves like is an STL iterator (albeit with random access and allowing you to view more than one element at a time!). The data in the array (whether provided at creation time or added later) resides on an accelerator (which is specified at creation time either explicitly by the developer, or set to the default accelerator at creation time by the runtime) and is laid out contiguously in memory. The data provided to the array_view is not stored by/in the array_view, because the array_view is simply a view over the real source (which can reside on the CPU or other accelerator). The underlying data is copied on demand to wherever the array_view is accessed. Elements which differ by one in the least significant dimension of the array_view are adjacent in memory. array objects must be captured by reference into the lambda you pass to the parallel_for_each call, whereas array_view objects must be captured by value (into the lambda you pass to the parallel_for_each call). Creating array and array_view objects and relevant properties You can create array_view objects from other array_view objects of the same rank and element type (shallow copy, also possible via assignment operator) so they point to the same underlying data, and you can also create array_view objects over array objects of the same rank and element type e.g.   array_view<int,3> a(b); // b can be another array or array_view of ints with rank=3 Note: Unlike the constructors above which can be called anywhere, the ones in the rest of this section can only be called from CPU code. You can create array objects from other array objects of the same rank and element type (copy and move constructors) and from other array_view objects, e.g.   array<float,2> a(b); // b can be another array or array_view of floats with rank=2 To create an array from scratch, you need to at least specify an extent object, e.g. array<int,3> a(myExtent);. Note that instead of an explicit extent object, there are convenience overloads when N<=3 so you can specify 1-, 2-, 3- integers (dependent on the array's rank) and thus have the extent created for you under the covers. At any point, you can access the array's extent thought the extent property. The exact same thing applies to array_view (extent as constructor parameters, incl. convenience overloads, and property). While passing only an extent object to create an array is enough (it means that the array will be written to later), it is not enough for the array_view case which must always wrap over some other container (on which it relies for storage space and actual content). So in addition to the extent object (that describes the shape you'd like to be viewing/accessing that data through), to create an array_view from another container (e.g. std::vector) you must pass in the container itself (which must expose .data() and a .size() methods, e.g. like std::array does), e.g.   array_view<int,2> aaa(myExtent, myContainerOfInts); Similarly, you can create an array_view from a raw pointer of data plus an extent object. Back to the array case, to optionally initialize the array with data, you can pass an iterator pointing to the start (and optionally one pointing to the end of the source container) e.g.   array<double,1> a(5, myVector.begin(), myVector.end()); We saw that arrays are bound to an accelerator at creation time, so in case you don’t want the C++ AMP runtime to assign the array to the default accelerator, all array constructors have overloads that let you pass an accelerator_view object, which you can later access via the accelerator_view property. Note that at the point of initializing an array with data, a synchronous copy of the data takes place to the accelerator, and then to copy any data back we'll see that an explicit copy call is required. This does not happen with the array_view where copying is on demand... refresh and synchronize on array_view Note that in the previous section on constructors, unlike the array case, there was no overload that accepted an accelerator_view for array_view. That is because the array_view is simply a wrapper, so the allocation of the data has already taken place before you created the array_view. When you capture an array_view variable in your call to parallel_for_each, the copy of data between the non-CPU accelerator and the CPU takes place on demand (i.e. it is implicit, versus the explicit copy that has to happen with the array). There are some subtleties to the on-demand-copying that we cover next. The assumption when using an array_view is that you will continue to access the data through the array_view, and not through the original underlying source, e.g. the pointer to the data that you passed to the array_view's constructor. So if you modify the data through the array_view on the GPU, the original pointer on the CPU will not "know" that, unless one of two things happen: you access the data through the array_view on the CPU side, i.e. using indexing that we cover below you explicitly call the array_view's synchronize method on the CPU (this also gets called in the array_view's destructor for you) Conversely, if you make a change to the underlying data through the original source (e.g. the pointer), the array_view will not "know" about those changes, unless you call its refresh method. Finally, note that if you create an array_view of const T, then the data is copied to the accelerator on demand, but it does not get copied back, e.g.   array_view<const double, 5> myArrView(…); // myArrView will not get copied back from GPU There is also a similar mechanism to achieve the reverse, i.e. not to copy the data of an array_view to the GPU. copy_to, data, and global copy/copy_async functions Both array and array_view expose two copy_to overloads that allow copying them to another array, or to another array_view, and these operations can also be achieved with assignment (via the = operator overloads). Also both array and array_view expose a data method, to get a raw pointer to the underlying data of the array or array_view, e.g. float* f = myArr.data();. Note that for array_view, this only works when the rank is equal to 1, due to the data only being contiguous in one dimension as covered in the overview section. Finally, there are a bunch of global concurrency::copy functions returning void (and corresponding concurrency::copy_async functions returning a future) that allow copying between arrays and array_views and iterators etc. Just browse intellisense or amp.h directly for the full set. Note that for array, all copying described throughout this post is deep copying, as per other STL container expectations. You can never have two arrays point to the same data. indexing into array and array_view plus projection Reading or writing data elements of an array is only legal when the code executes on the same accelerator as where the array was bound to. In the array_view case, you can read/write on any accelerator, not just the one where the original data resides, and the data gets copied for you on demand. In both cases, the way you read and write individual elements is via indexing as described next. To access (or set the value of) an element, you can index into it by passing it an index object via the subscript operator. Furthermore, if the rank is 3 or less, you can use the function ( ) operator to pass integer values instead of having to use an index object. e.g. array<float,2> arr(someExtent, someIterator); //or array_view<float,2> arr(someExtent, someContainer); index<2> idx(5,4); float f1 = arr[idx]; float f2 = arr(5,4); //f2 ==f1 //and the reverse for assigning, e.g. arr(idx[0], 7) = 6.9; Note that for both array and array_view, regardless of rank, you can also pass a single integer to the subscript operator which results in a projection of the data, and (for both array and array_view) you get back an array_view of rank N-1 (or if the rank was 1, you get back just the element at that location). Not Covered In this already very long post, I am not going to cover three very cool methods (and related overloads) that both array and array_view expose: view_as, section, reinterpret_as. We'll revisit those at some point in the future, probably on the team blog. Comments about this post by Daniel Moth welcome at the original blog.

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  • State Changes in a Component Based Architecture [closed]

    - by Maxem
    I'm currently working on a game and using the naive component based architecture thingie (Entities are a bag of components, entity.Update() calls Update on each updateable component), while the addition of new features is really simple, it makes a few things really difficult: a) multithreading / currency b) networking c) unit testing. Multithreading / Concurrency is difficult because I basically have to do poor mans concurrency (running the entity updates in separate threads while locking only stuff that crashes (like lists) and ignoring the staleness of read state (some states are already updated, others aren't)) Networking: There are no explicit state changes that I could efficiently push over the net. Unit testing: All updates may or may not conflict, so automated testing is at least awkward. I was thinking about these issues a bit and would like your input on these changes / idea: Switch from the naive cba to a cba with sub systems that work on lists of components Make all state changes explicit Combine 1 and 2 :p Example world update: statePostProcessing.Wait() // ensure that post processing has finished Apply(postProcessedState) state = new StateBag() Concurrently( () => LifeCycleSubSystem.Update(state), // populates the state bag () => MovementSubSystem.Update(state), // populates the state bag .... }) statePostProcessing = Future(() => PostProcess(state)) statePostProcessing.Start() // Tick is finished, the post processing happens in the background So basically the changes are (consistently) based on the data for the last tick; the post processing can a) generate network packages and b) fix conflicts / remove useless changes (example: entity has been destroyed - ignore movement etc.). EDIT: To clarify the granularity of the state changes: If I save these post processed state bags and apply them to an empty world, I see exactly what has happened in the game these state bags originated from - "Free" replay capability. EDIT2: I guess I should have used the term Event instead of State Change and point out that I kind of want to use the Event Sourcing pattern

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