parallel-processing

问题 Why do scripts parallelized with mclapply print on a cluster but not in RStudio? Just asking out of curiosity. mclapply(1:10, function(x) { print("Hello!") return(TRUE) }, mc.cores = 2) # Hello prints in slurm but not RStudio 回答1: None of the functions in the 'parallel' package guarantee proper displaying of output sent to the standard output (stdout) or the standard error (stderr) on workers. This is true for all types of parallelization approaches, e.g. forked processing ( mclapply() ), or

问题 Hi i am new to c++ and i made a code which runs but it is slow because of many nested for loops i want to speed it up by openmp anyone who can guide me. i tried to use ' #pragma omp parallel ' before ip loop and inside this loop i used ' #pragma omp parallel for ' before it loop but it does not works #pragma omp parallel for(int ip=0; ip !=nparticle; ip++){ inf14>>r>>xp>>yp>>zp; zp/=sqrt(gamma2); counter++; double para[7]={0,0,Vz,x0-xp,y0-yp,z0-zp,0}; if(ip>=0 && ip<=43){ #pragma omp parallel

问题 My aim is to pass a parent array to mp.Pool and fill it with 2 s while distributing it to different processes. This works for arrays of 1 dimension: import numpy as np import multiprocessing as mp import itertools def worker_function(i=None): global arr val = 2 arr[i] = val print(arr[:]) def init_arr(arr=None): globals()['arr'] = arr def main(): arr = mp.Array('i', np.zeros(5, dtype=int), lock=False) mp.Pool(1, initializer=init_arr, initargs=(arr,)).starmap(worker_function, zip(range(5)))

问题 My aim is to pass a parent array to mp.Pool and fill it with 2 s while distributing it to different processes. This works for arrays of 1 dimension: import numpy as np import multiprocessing as mp import itertools def worker_function(i=None): global arr val = 2 arr[i] = val print(arr[:]) def init_arr(arr=None): globals()['arr'] = arr def main(): arr = mp.Array('i', np.zeros(5, dtype=int), lock=False) mp.Pool(1, initializer=init_arr, initargs=(arr,)).starmap(worker_function, zip(range(5)))

问题 I'm trying to learn to run a custom function through multidplyr::do() on a cluster. Consider this simple self contained example. For example's sake, I'm trying to apply my custom function myWxTest to each common_dest (destinations with more than 50 flights) in the flight dataset: library(dplyr) library(multidplyr) library(nycflights13) library(quantreg) myWxTest <- function(x){ stopifnot(!is.null(x$dep_time)) stopifnot(!is.null(x$dep_delay)) stopifnot(!is.null(x$sched_dep_time)) stopifnot(!is

问题 I'm looking for a suitable way to display the processing time of parallel running Tasks on a separate stage. I want to execute different tasks combined in an ArrayList - one after the other. For this case I'm using a ThreadPool. After each executed list, I want to wait until all tasks are completed. Only when the tasks have reached the status „succeeded“, I want to do something in the MainThread. After that I want to execute another list of tasks and visualize them on a separate stage as well

问题 I'm using VS2019 and have an NVIDIA GeForce GPU. I tried the code from this link: https://towardsdatascience.com/writing-lightning-fast-code-with-cuda-c18677dcdd5f The author of that post claims to get a speedup when using CUDA. However, for me, the serial version takes around 7 milliseconds while the CUDA version takes around 28 milliseconds. Why is CUDA slower for this code? The code I used is below: __global__ void add(int n, float* x, float* y) { int index = blockIdx.x * blockDim.x +

问题 I want to implement a matrix-vector multiplication in C. My matrix is 1000 * 1000^2 and highly sparse (less than 0.01% non-zero elements). The non-zero elements are dispersed among the rows (between 0 to 126 non-zero elements per row). I have heard that generally, using parallel processing for sparse matrix-vector multiplication is challenging and not as efficient as dense matrices because the ratio of computation to memory access is low (Here). But I cannot really understand what is the main

问题 Is nesting parallel::mclapply calls a good idea? require(parallel) ans <- mclapply(1:3, function(x) mclapply(1:3, function(y) y * x)) unlist(ans) Outputs: [1] 1 2 3 2 4 6 3 6 9 So it's "working". But is it recommended for real compute-intensive tasks that outnumber the number of cores? what is going on when this is executed? Are the multiple forks involved more potentially wasteful? What are the considerations for mc.cores and mc.preschedule ? Edit Just to clarify the motivation, often it

问题 I have written a routine that takes considerable time without parallelization. The issue is that I am unsure what to iterate over, since I have a repeat loop with breaks. The loop consists of the following code snippet (for loop not shown): repeat{ if(R < p){ HAC.sim(K = K, N = ceiling(Nstar), Hstar = Hstar, probs = probs, perms = perms, equal.freq = FALSE, subset.haps = NULL) } else{ break } } I would like to use foreach() with the parallel backend; however I am not certain what is needed