parallel | Ruby : parallel processing | Architecture library

 by   grosser Ruby Version: v1.23.0 License: MIT

kandi X-RAY | parallel Summary

kandi X-RAY | parallel Summary

parallel is a Ruby library typically used in Architecture applications. parallel has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

Ruby: parallel processing made simple and fast

            kandi-support Support

              parallel has a medium active ecosystem.
              It has 4021 star(s) with 257 fork(s). There are 78 watchers for this library.
              It had no major release in the last 6 months.
              There are 33 open issues and 148 have been closed. On average issues are closed in 81 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of parallel is v1.23.0

            kandi-Quality Quality

              parallel has 0 bugs and 10 code smells.

            kandi-Security Security

              parallel has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              parallel code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              parallel is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              parallel releases are not available. You will need to build from source code and install.
              parallel saves you 724 person hours of effort in developing the same functionality from scratch.
              It has 1672 lines of code, 56 functions and 66 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed parallel and discovered the below as its top functions. This is intended to give you an instant insight into parallel implemented functionality, and help decide if they suit your requirements.
            • Get the number of memory processors .
            • Return next item from the queue
            • Initialize the client
            • Sleep for the process .
            • Closes the write method
            • Pack the item .
            • Get the number of processors .
            • Unpack a new producer .
            • Push an item to the queue .
            Get all kandi verified functions for this library.

            parallel Key Features

            No Key Features are available at this moment for parallel.

            parallel Examples and Code Snippets

            Parallel processing
            mavendot img1Lines of Code : 13dot img1no licencesLicense : No License
            copy iconCopy
            Flowable.range(1, 10)
              .flatMap(v ->
                    .map(w -> w * w)
            Flowable.range(1, 10)
            Parallel processing
            mavendot img2Lines of Code : 13dot img2no licencesLicense : No License
            copy iconCopy
            Flowable.range(1, 10)
              .flatMap(v ->
                    .map(w -> w * w)
            Flowable.range(1, 10)
            Generator for parallel walk .
            pythondot img3Lines of Code : 66dot img3License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def parallel_walk(node, other):
              """Walks two ASTs in parallel.
              The two trees must have identical structure.
                node: Union[ast.AST, Iterable[ast.AST]]
                other: Union[ast.AST, Iterable[ast.AST]]
                Tuple[ast.AST, ast.AST]
            Create a parallel interleave dataset .
            pythondot img4Lines of Code : 58dot img4License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def parallel_interleave(map_func,
            Creates a parallel map and returns the result .
            pythondot img5Lines of Code : 38dot img5License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def _benchmark_map_and_interleave(self, autotune, benchmark_id):
                k = 1024 * 1024
                a = (np.random.rand(1, 8 * k), np.random.rand(8 * k, 1))
                b = (np.random.rand(1, 4 * k), np.random.rand(4 * k, 1))
                c = (np.random.rand(1, 2 * k), np.rando  

            Community Discussions


            Parallelization in Durable Function
            Asked 2021-Jun-16 at 01:02

            I'm trying to understand how parallelization works in Durable Function. I have a durable function with the following code:



            Answered 2021-Jun-10 at 08:44

            There are two approaches that are possible. The first is to use a suborchestrator for each job so that each suborchestrator handles just a specific job. Here is the docs for this approach Example from docs seem to be alike to yours.

            The other is to use ContinueWith so that each job has its own "chain"



            Parallelize histogram creation in c++ with futures: how to use a template function with future?
            Asked 2021-Jun-16 at 00:46

            Giving a bit of context. I'm using c++17. I'm using pointer T* data because this will interop with cuda code. I'm trying write a parallel version (on CPU) of a histogram creator. The sequential version:



            Answered 2021-Jun-16 at 00:46

            The issue you are having has nothing to do with templates. You cannot invoke std::async() on a member function without binding it to an instance. Wrapping the call in a lambda does the trick.

            Here's an example:



            Implement barrier with pthreads on C
            Asked 2021-Jun-15 at 18:32

            I'm trying to parallelize a merge-sort algorithm. What I'm doing is dividing the input array for each thread, then merging the threads results. The way I'm trying to merge the results is something like this:



            Answered 2021-Jun-15 at 01:58

            I'm trying to parallelize a merge-sort algorithm. What I'm doing is dividing the input array for each thread, then merging the threads results.

            Ok, but yours is an unnecessarily difficult approach. At each step of the merge process, you want half of your threads to wait for the other half to finish, and the most natural way for one thread to wait for another to finish is to use pthread_join(). If you wanted all of your threads to continue with more work after synchronizing then that would be different, but in this case, those that are not responsible for any more merges have nothing at all left to do.

            This is what I've tried:



            How to thread a generator
            Asked 2021-Jun-15 at 16:02

            I have a generator object, that loads quite big amount of data and hogs the I/O of the system. The data is too big to fit into memory all at once, hence the use of generator. And I have a consumer that all of the CPU to process the data yielded by generator. It does not consume much of other resources. Is it possible to interleave these tasks using threads?

            For example I'd guess it is possible to run the simplified code below in 11 seconds.



            Answered 2021-Jun-15 at 16:02

            Send your data to separate processes. I used concurrent.futures because I like the simple interface.

            This runs in about 11 seconds on my computer.



            Recommended way of measuring execution time in Tensorflow Federated
            Asked 2021-Jun-15 at 13:49

            I would like to know whether there is a recommended way of measuring execution time in Tensorflow Federated. To be more specific, if one would like to extract the execution time for each client in a certain round, e.g., for each client involved in a FedAvg round, saving the time stamp before the local training starts and the time stamp just before sending back the updates, what is the best (or just correct) strategy to do this? Furthermore, since the clients' code run in parallel, are such a time stamps untruthful (especially considering the hypothesis that different clients may be using differently sized models for local training)?

            To be very practical, using tf.timestamp() at the beginning and at the end of @tf.function client_update(model, dataset, server_message, client_optimizer) -- this is probably a simplified signature -- and then subtracting such time stamps is appropriate?

            I have the feeling that this is not the right way to do this given that clients run in parallel on the same machine.

            Thanks to anyone can help me on that.



            Answered 2021-Jun-15 at 12:01

            There are multiple potential places to measure execution time, first might be defining very specifically what is the intended measurement.

            1. Measuring the training time of each client as proposed is a great way to get a sense of the variability among clients. This could help identify whether rounds frequently have stragglers. Using tf.timestamp() at the beginning and end of the client_update function seems reasonable. The question correctly notes that this happens in parallel, summing all of these times would be akin to CPU time.

            2. Measuring the time it takes to complete all client training in a round would generally be the maximum of the values above. This might not be true when simulating FL in TFF, as TFF maybe decided to run some number of clients sequentially due to system resources constraints. In practice all of these clients would run in parallel.

            3. Measuring the time it takes to complete a full round (the maximum time it takes to run a client, plus the time it takes for the server to update) could be done by moving the tf.timestamp calls to the outer training loop. This would be wrapping the call to in the snippet on This would be most similar to elapsed real time (wall clock time).



            SLURM and Python multiprocessing pool on a cluster
            Asked 2021-Jun-15 at 13:42

            I am trying to run a simple parallel program on a SLURM cluster (4x raspberry Pi 3) but I have no success. I have been reading about it, but I just cannot get it to work. The problem is as follows:

            I have a Python program named This program is executed on a single node (node=1xraspberry pi) and inside the program there is a multiprocessing loop section that looks something like:



            Answered 2021-Jun-15 at 06:17

            Pythons multiprocessing package is limited to shared memory parallelization. It spawns new processes that all have access to the main memory of a single machine.

            You cannot simply scale out such a software onto multiple nodes. As the different machines do not have a shared memory that they can access.

            To run your program on multiple nodes at once, you should have a look into MPI (Message Passing Interface). There is also a python package for that.

            Depending on your task, it may also be suitable to run the program 4 times (so one job per node) and have it work on a subset of the data. It is often the simpler approach, but not always possible.



            what is the meaning of "map" from map function?
            Asked 2021-Jun-15 at 11:06

            I'm happy to use "map function" in python for parallelized calculations. such as below.



            Answered 2021-Jun-14 at 20:26

            "Map" is also a synonym for "function" in the mathematical sense: something that sends an input to an output. You should be able to find it in any English dictionary. It can also be used as a verb for the process of transformation: "map each element to its square".

            The word "map" for a geographic drawing is related, in that it also "maps" each point of the real terrain to a point on the paper map, or vice versa.

            It is not an acronym.



            How python multithreaded program can run on different Cores of CPU simultaneously despite of having GIL
            Asked 2021-Jun-15 at 08:23

            In this video, he shows how multithreading runs on physical(Intel or AMD) processor cores.



            is python capable of running on multiple cores?

            All these links basically say:
            Python threads cannot take advantage of many physical cores. This is due to an internal implementation detail called the GIL (global interpreter lock) and if we want to utilize multiple physical cores of the CPU we must use true parallel multiprocessing module

            But when I ran this below code on my laptop



            Answered 2021-Jun-15 at 08:06


            The math module consists mostly of thin wrappers around the platform C math library functions.

            While python itself can only execute a single instruction at a time, a low level c function that is called by python does not have this limitation.
            So it's not python that is using multiple cores but your system's well optimized math library that is wrapped by python's math module.

            That basically answers both your questions.

            Regarding the usefulness of multiprocessing: It is still useful for those cases, where you're trying to parallelize pure python code or code that does not call libraries that already use multiple cores. However, it comes with inter process communication (IPC) overhead that may or may not be larger than the performance gain that you get from using multiple cores. Tuning IPC is therefore often crucial for multiprocessing in python.



            Play and task execution with multiple groups and servers with ansible
            Asked 2021-Jun-14 at 21:08

            We have this Ansible inventory with dozens of servers, being grouped in servers per microservice. So say we have several application groups in the inventory with servers in it.




            Answered 2021-Jun-08 at 15:26

            there is already an answer on how to run playbooks on multiple hosts answered here Ansible: deploy on multiple hosts in the same time

            Maybe you could start form there. However if running only first servers in parallel interests you than it will be more difficult, as it would require writing a custom script or something similar



            Is there a metric to quantify the perspectiveness in two images?
            Asked 2021-Jun-14 at 16:59

            I am coding a program in OpenCV where I want to adjust camera position. I would like to know if there is any metric in OpenCV to measure the amount of perspectiveness in two images. How can homography be used to quantify the degree of perspectiveness in two images as follows. The method that comes to my mind is to run edge detection and compare the parallel edge sizes but that method is prone to errors.



            Answered 2021-Jun-14 at 16:59

            As a first solution I'd recommend maximizing the distance between the image of the line at infinity and the center of your picture.

            Identify at least two pairs of lines that are parallel in the original image. Intersect the lines of each pair and connect the resulting points. Best do all of this in homogeneous coordinates so you won't have to worry about lines being still parallel in the transformed version. Compute the distance between the center of the image and that line, possibly taking the resolution of the image into account somehow to make the result invariant to resampling. The result will be infinity for an image obtained from a pure affine transformation. So the larger that value the closer you are to the affine scenario.


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


            No vulnerabilities reported

            Install parallel

            You can download it from GitHub.
            On a UNIX-like operating system, using your system’s package manager is easiest. However, the packaged Ruby version may not be the newest one. There is also an installer for Windows. Managers help you to switch between multiple Ruby versions on your system. Installers can be used to install a specific or multiple Ruby versions. Please refer for more information.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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