parallel-processing

问题 I'm not sure that I am correctly understanding what MPI_Scatterv is supposed to do. I have 79 items to scatter amounts a variable amount of nodes. However, when I use the MPI_Scatterv command I get ridiculous numbers (as if the array elements of my receiving buffer are uninitialized). Here is the relevant code snippet: MPI_Init(&argc, &argv); int id, procs; MPI_Comm_rank(MPI_COMM_WORLD, &id); MPI_Comm_size(MPI_COMM_WORLD, &procs); //Assign each file a number and figure out how many files

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 4 years ago . Can anyone help me get my head around the MPI Groups, Inter and Intra communicators. I have already gone through the MPI documentation(http://www.mpi-forum.org/docs/mpi-2.2/mpi22-report.pdf ) and I couldnt make good sense of these concepts. I would especially appreciate any code

问题 I am trying to understand if using await Task.Run(async () => MethodName()) in MVC 5 gives the benefits of freeing up the thread of a long running IO operation, while continuing with other code tasks in Parallel. I know that simply using "await MethodName()" will free up the thread, but it will not move to the next line of code unit MethodName() is done executing. (Please correct me if I am wrong). I'd like to be able to free up the thread while the async operation is executing, as well as

问题 I am trying to understand if using await Task.Run(async () => MethodName()) in MVC 5 gives the benefits of freeing up the thread of a long running IO operation, while continuing with other code tasks in Parallel. I know that simply using "await MethodName()" will free up the thread, but it will not move to the next line of code unit MethodName() is done executing. (Please correct me if I am wrong). I'd like to be able to free up the thread while the async operation is executing, as well as

问题 The description of the Task.WhenAny method says, that it will return the first task finished, even if it's faulted. Is there a way to change this behavior, so it would return first successful task? 回答1: Something like this should do it (may need some tweaks - haven't tested): private static async Task<Task> WaitForAnyNonFaultedTaskAsync(IEnumerable<Task> tasks) { IList<Task> customTasks = tasks.ToList(); Task completedTask; do { completedTask = await Task.WhenAny(customTasks); customTasks

问题 This question already has answers here : Parallel.ForEach Slower than ForEach (5 answers) Closed 5 years ago . I created a simple test with big arrays, and I get a big difference between parallel for and normal for using System; using System.Collections.Generic; using System.Linq; using System.Text; using System.Threading.Tasks; namespace ConsoleApplication1 { class Program { static void Main(string[] args) { var rn = new Random(541); var tmStart = DateTime.Now; int[,] a = new int[2048, 2048]

问题 I am trying to use tf.while_loop to run loops in parallel. However, in the following toy examples,loops don't appear to be running in parallel. iteration = tf.constant(0) c = lambda i: tf.less(i, 1000) def print_fun(iteration): print(f"This is iteration {iteration}") iteration+=1 return (iteration,) r = tf.while_loop(c, print_fun, [iteration], parallel_iterations=10) Or i = tf.constant(0) c = lambda i: tf.less(i, 1000) b = lambda i: (tf.add(i, 1),) r = tf.while_loop(c, b, [i]) What is

问题 I am currently rewriting the automated testing framework for my company's mobile testing. We are attempting to use an interface which is implemented by multiple Page Object Models dependent on the Operating System of the mobile device the application is being run on. I can get this framework to run sequentially and even create multiple threads but it will not run in parallel no matter what I do. Of Note, we use Appium and something called the DeviceCart / DeviceConnect which allows me to

问题 I am training my neural network using tensorflow on CentOS HPC. However I got this error at start of the training process: OMP: Error #15: Initializing libiomp5.so, but found libiomp5.so already initialized. OMP: Hint: This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by

问题 I am training my neural network using tensorflow on CentOS HPC. However I got this error at start of the training process: OMP: Error #15: Initializing libiomp5.so, but found libiomp5.so already initialized. OMP: Hint: This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by