parallel-processing

问题 What I'd like to know is if it is possible, inside a debugging session in Linux, (read: gdb :)) to stop the execution of a single thread, leaving the other threads to run. If someone is curious to know why keep reading: I wrote a software watchdog C++ class (using Qt). I tested it with a simple multithreaded program, but I'd like to test the code once I integrate it inside the real application as well. If I could stop a thread from the debugger, that will simplify this testing phase. :)

问题 It is given in the caret documentation that to allow parallel processing the following code works library(doMC) registerDoMC(cores = 5) ## All subsequent models are then run in parallel But in the latest R version(3.4) the package doMC is not available. Can anyone let me know of any other way to do parallel processing? Update : What Roman suggested worked. DoMC is not available for windows. For windows use doParallel package cls = makeCluster(no of cores to use) and then registerDoParallel

问题 We generate pdf files with data regarding monthly financial balance of tens of thousands of clients. At it's peak (100.000 files at the end of year), the process may take as long as 5 days to complete using distribute the load between 5 servers. The distribution of workload is a manual process (eg. server 1 generates pdf for clients 1 to 20.000, server 2 from 20.001 to 40.000, and so on). We use Java, so we would like to use a Java tool or framework in a fashion similar to BOINC (BOINC is not

问题 We generate pdf files with data regarding monthly financial balance of tens of thousands of clients. At it's peak (100.000 files at the end of year), the process may take as long as 5 days to complete using distribute the load between 5 servers. The distribution of workload is a manual process (eg. server 1 generates pdf for clients 1 to 20.000, server 2 from 20.001 to 40.000, and so on). We use Java, so we would like to use a Java tool or framework in a fashion similar to BOINC (BOINC is not

问题 I have been doing some research in Google and cant quite get my head around the differences (if any) between concurrent and parallel programs in java. Some of the information I have looked at suggests no differences between both. Is this the case?? 回答1: It depends on who is defining it. The people who created the Go programming language call code Concurrent if it is broken up into pieces which could be treated in parallel, whereas Parallelism implies that those pieces are actually running at

问题 EDITED: I have a code which looks like: __author__ = 'feynman' cimport cython @cython.boundscheck(False) @cython.wraparound(False) @cython.nonecheck(False) def MC_Surface(volume, mc_vol): Perm_area = { "00000000": 0.000000, "11111111": 0.000000, ... ... "11100010": 1.515500, "00011101": 1.515500 } cdef int j, i, k for k in range(volume.shape[2] - 1): for j in range(volume.shape[1] - 1): for i in range(volume.shape[0] - 1): pattern = '%i%i%i%i%i%i%i%i' % ( volume[i, j, k], volume[i, j + 1, k],

问题 I have just did a sample for multithreading using This Link like below: Console.WriteLine("Number of Threads: {0}", System.Diagnostics.Process.GetCurrentProcess().Threads.Count); int count = 0; Parallel.For(0, 50000, options,(i, state) => { count++; }); Console.WriteLine("Number of Threads: {0}", System.Diagnostics.Process.GetCurrentProcess().Threads.Count); Console.ReadKey(); It gives me 15 thread before Parellel.For and after it gives me 17 thread only. So only 2 thread is occupy with

问题 I have a problem with my code, it should print number of appearances of a certain number. I want parallelize this code with OpenMP, and I tried to use reduction for arrays but it's obviously didn't working as I wanted. The error is: "segmentation fault". Should some variables be private? or it's the problem with the way I'm trying to use the reduction? I think each thread should count some part of array, and then merge it somehow. #pragma omp parallel for reduction (+: reasult[:i]) for (i = 0

问题 I have a problem with my code, it should print number of appearances of a certain number. I want parallelize this code with OpenMP, and I tried to use reduction for arrays but it's obviously didn't working as I wanted. The error is: "segmentation fault". Should some variables be private? or it's the problem with the way I'm trying to use the reduction? I think each thread should count some part of array, and then merge it somehow. #pragma omp parallel for reduction (+: reasult[:i]) for (i = 0

问题 I learnt OpenMP using Tim Matterson's lecture notes, and he gave an example of false sharing as below. The code is simple and is used to calculate pi from numerical integral of 4.0/(1+x*x) with x ranges from 0 to 1. The code uses a vector to contain the value of 4.0/(1+x*x) for each x from 0 to 1, then sum the vector at the end: #include <omp.h> static long num_steps = 100000; double step; #define NUM_THREADS 2 void main() { int i, nthreads; double pi, sum[NUM_THREADS]; step = 1.0/(double)num