OMP | OMP is an open-source music player being developed for Linux | Audio Utils library

 by   TheWiseNoob C++ Version: Current License: Non-SPDX

kandi X-RAY | OMP Summary

kandi X-RAY | OMP Summary

OMP is a C++ library typically used in Audio, Audio Utils applications. OMP has no bugs, it has no vulnerabilities and it has low support. However OMP has a Non-SPDX License. You can download it from GitHub.

OMP is an open-source music player being developed for Linux. It is programmed in C++ and some C using gtkmm3, GStreamer, TagLib, libconfig, libclastfm, and standard C and C++ libraries. It can play mp3, FLAC, Ogg, Ogg FLAC, ALAC, APE, WavPack, and AAC(m4a container).

            kandi-support Support

              OMP has a low active ecosystem.
              It has 109 star(s) with 3 fork(s). There are 14 watchers for this library.
              It had no major release in the last 6 months.
              There are 12 open issues and 20 have been closed. On average issues are closed in 56 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of OMP is current.

            kandi-Quality Quality

              OMP has no bugs reported.

            kandi-Security Security

              OMP has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              OMP has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              OMP releases are not available. You will need to build from source code and install.
              Installation instructions, examples and code snippets are available.

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            OMP Key Features

            No Key Features are available at this moment for OMP.

            OMP Examples and Code Snippets

            No Code Snippets are available at this moment for OMP.

            Community Discussions


            How to parallelize this array correct way using OpenMP?
            Asked 2021-Jun-12 at 13:14

            After I try to parallelize the code with openmp, the elements in the array are wrong, as for the order of the elements is not very important. Or is it more convenient to use c++ std vector instead of array to parallelize, could you suggest a easy way?



            Answered 2021-Jun-11 at 06:20

            Your threads are all accessing the shared count.

            You would be better off eliminating count and have each loop iteration determine where to write its output based only on the (per-thread) values of i and j.

            Alternatively, use a vector to accumulate the results:



            OpenMP vectorised code runs way slower than O3 optimized code
            Asked 2021-Jun-11 at 14:46

            I have a minimally reproducible sample which is as follows -



            Answered 2021-Jun-11 at 14:46

            The non-OpenMP vectorizer is defeating your benchmark with loop inversion.
            Make your function __attribute__((noinline, noclone)) to stop GCC from inlining it into the repeat loop. For cases like this with large enough functions that call/ret overhead is minor, and constant propagation isn't important, this is a pretty good way to make sure that the compiler doesn't hoist work out of the loop.

            And in future, check the asm, and/or make sure the benchmark time scales linearly with the iteration count. e.g. increasing 500 up to 1000 should give the same average time in a benchmark that's working properly, but it won't with -O3. (Although it's surprisingly close here, so that smell test doesn't definitively detect the problem!)

            After adding the missing #pragma omp simd to the code, yeah I can reproduce this. On i7-6700k Skylake (3.9GHz with DDR4-2666) with GCC 10.2 -O3 (without -march=native or -fopenmp), I get 18266, but with -O3 -fopenmp I get avg time 39772.

            With the OpenMP vectorized version, if I look at top while it runs, memory usage (RSS) is steady at 771 MiB. (As expected: init code faults in the two inputs, and the first iteration of the timed region writes to result, triggering page-faults for it, too.)

            But with the "normal" vectorizer (not OpenMP), I see the memory usage climb from ~500 MiB until it exits just as it reaches the max 770MiB.

            So it looks like gcc -O3 performed some kind of loop inversion after inlining and defeated the memory-bandwidth-intensive aspect of your benchmark loop, only touching each array element once.

            The asm shows the evidence: GCC 9.3 -O3 on Godbolt doesn't vectorize, and it leaves an empty inner loop instead of repeating the work.



            Do COMMON blocks in Fortran have to be declared threadprivate in every subroutine for OpenMP?
            Asked 2021-Jun-11 at 12:18

            I am modifying some old, old Fortran code to run with OpenMP directives, and it makes heavy use of COMMON block. I have found multiple sources that say that using OMP directives to declare COMMON blocks as THREADPRIVATE solves the issue of COMMON blocks residing in global scope by giving each OpenMP thread its own copy. What I'm unsure of though, is whether the THREADPRIVATE directive needs to be after declaration in every single subroutine, or whether having it in the main (and only) PROGRAM is enough?



            Answered 2021-Jun-11 at 07:44

            Yes, it must be at every occurrence. Quoting from the OpenMP 5.0 standard

            If a threadprivate directive that specifies a common block name appears in one program unit, then such a directive must also appear in every other program unit that contains a COMMON statement that specifies the same name. It must appear after the last such COMMON statement in the program unit.

            As a comment putting OpenMP into a program full of global variables is likely to lead to a life of pain. I would at least give some thought to "do I want to start from here" before I begin such an endeavour - modernisation of the code before you add OpenMP might turn out to be an easier and cheaper option, especially in the long run.



            OpenMP Parallelizing not performing as expected
            Asked 2021-Jun-10 at 07:07

            I have a code that looks like this:



            Answered 2021-Jun-07 at 17:40

            so I don't know what to do

            You have to measure.

            I made only a simple for-loop to fill one array and this takes half of the time. I made two global arrays with 10 Mio floats.

            For comparison:



            "omp parallel for" does not work in "omp parallel"
            Asked 2021-Jun-09 at 07:35

            I expect to get the following output:



            Answered 2021-Jun-02 at 17:56

            You should not repeat parallel, you are already inside a parallel block, so you only need pragma omp for for the loop, and each thread executing the parallel block will automatically take a chunk of the loop if you specify pragma omp for. If you want to specify the number of threads you can do pragma omp parallel num_threads(4) and then pragma omp for. In any case for such a simple piece of code you can just drop the entire outer block which seems unneeded.

            Here's the correct version:



            Openmp c++: error: collapsed loops not perfectly nested
            Asked 2021-Jun-02 at 07:27

            I have the following serial code that I would like to make parallel. I understand when using the collapse clause for nested loops, it's important to not have code before and after the for(i) loop since is not allowed. Then how do I parallel a nested for loop with if statements like this:



            Answered 2021-Jun-01 at 20:04

            As pointed out in the comments by 1201ProgramAlarm, you can get rid of the error by eliminating the if branch that exists between the two loops:



            How can I paralelize two for statements equally between threads using OpenMP?
            Asked 2021-Jun-02 at 07:18

            Lets say I have the following code:



            Answered 2021-Jun-02 at 07:18

            The problem with your code is that multiple threads will try to modify array2 at the same time (race condition). This can easily be avoided by reordering the loops. If array2.size doesn't provide enough parallelism, you may apply the collapse clause, as the loops are now in canonical form.



            Speed up and scheduling with OpenMP
            Asked 2021-Jun-01 at 15:53

            i'm using OpenMP for a kNN project. The two parallelized for loops are:



            Answered 2021-Jun-01 at 10:36

            Why the 16 Threads case differs so much from the others? I'm running the algorithm on a Google VM machine with 24 Threads and 96 GB of ram.

            As you have mentioned on the comments:

            It's a Intel Xeon CPU @2.30 GHz, 12 physical core

            That is the reason that when you moved to 16 thread you stop (almost) linearly scaling, because you are no longer just using physical cores but also logic cores (i.e., hyper-threading).

            I expected that static would be the best since the iterations takes approximately the same time, while the dynamic would introduce too much overhead.

            Most of the overhead of the dynamic distribution comes from the locking step performed by the threads to acquire the new iteration to work with. It just looks to me that there is not much thread locking contention going on, and even if it is, it is being compensated by better loading balancing achieved with the dynamic scheduler. I have seen this exact pattern before there is not wrong with it.

            Aside note you can transform your code into:



            How to combine constexpr and vectorized code?
            Asked 2021-Jun-01 at 14:43

            I am working on a C++ intrinsic wrapper for x64 and neon. I want my functions to be constexpr. My motivation is similar to Constexpr and SSE intrinsics, but #pragma omp simd and intrinsics may not be supported by the compiler (GCC) in a constexpr function. The following code is just a demonstration (auto-vectorization is good enough for addition).



            Answered 2021-Jun-01 at 14:43

            Using std::is_constant_evaluated, you can get exactly what you want:



            Why in some cases #pragma omp critical directive is inefficient?
            Asked 2021-May-30 at 16:12

            I have read that using #pragma omp critical upon one statement like that is inefficient, i do not know why?



            Answered 2021-May-10 at 01:50

            A naive compiler/runtime would do at each iteration:

            • take a lock
            • compute `4.0 / (1.0 + x*x)
            • perform area += ...
            • release the lock

            An alternative would be not to use locks, but perform area += ... with an atomic instruction.

            In both cases, this is way less efficient that using a reduction clause, in which each thread runs without any synchronization, and the reduction (possibly tree-based) only happens at the end of the OpenMP region.


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install OMP

            OMP now has Flatpak support! That means any distribution that supports Flatpak is now supported by OMP! An official Flathub package will be provided soon. It is still being worked to meet Flathub's approval standards. More news about it is coming in the near future. Check for mentions about it in the Weekly News Updates on OMP's website at Until the official Flathub release, read the guide for installing OMP's Flatpak at OMP's official website. You can install OMP via AUR with the official omp AUR package for the stable build or the omp-git AUR package for the latest git build. Once compiled and installed, you can run omp as a command to open it. You will need to manually install all of the dependencies before being able to use the compile and install instructions for this method. OMP is currently only tested as working with Arch Linux. To compile and then install, run the following commands in a new folder containing the source. Once compiled and installed, you can run omp as a command to open it.


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