parallel-processing

问题 Is there a simple way to track the overall progress of a joblib.Parallel execution? I have a long-running execution composed of thousands of jobs, which I want to track and record in a database. However, to do that, whenever Parallel finishes a task, I need it to execute a callback, reporting how many remaining jobs are left. I've accomplished a similar task before with Python's stdlib multiprocessing.Pool, by launching a thread that records the number of pending jobs in Pool's job list.

问题 I am currently building a Monte Carlo application in C++ and I have a question regarding parallelization with MPI. The process I want to parallelize is the MC generation of data. To have good precision in my final results, I specify the goal number of data points. Each data point is generated independently, but might require vastly differing amounts of time. How do I organize the parallelization and workload distribution of the data generation most efficiently? What I have done so far So far

问题 from concurrent.futures import ProcessPoolExecutor import os import time def parInnerLoop(item): print(f'Processing {os.getpid()} started on {item}') time.sleep(3) print(f'Processing {os.getpid()} done on {item}') def main(): executor = ProcessPoolExecutor(max_workers=4) for itemNo in range(10): executor.submit(parInnerLoop(itemNo)) if __name__ == '__main__': main() What I'm trying to achieve is parallel for loop, similar to MatLab, e.g.: parfor itemNo = 0:9 parInnerLoop(itemNo); end What I'm

问题 I am defining a function in python. Program file name itself is abc_d.py . I don't understand if i can import the same file inside again. import numpy as np import matplotlib.pyplot as plt import sys import multiprocessing num_processor=4 pool = multiprocessing.Pool(num_processor) def abc(data): w=np.dot(data.reshape(25,1),data.reshape(1,25)) return w data_final=np.array(range(100)) n=100 error=[] k_list=[50,100,500,1000,2000] for k in k_list: dict_data={} for d_set in range(num_processor):

问题 There is an example in Numba doc about parallel race condition import numba as nb import numpy as np @nb.njit(parallel=True) def prange_wrong_result(x): n = x.shape[0] y = np.zeros(4) for i in nb.prange(n): y[:]+= x[i] return y I have ran it, and it indeed outputs abnormal result like prange_wrong_result(np.ones(10000)) #array([5264., 5273., 5231., 5234.]) then I tried to change the loop into import numba as nb import numpy as np @nb.njit(parallel=True) def prange_wrong_result(x): n = x.shape

问题 Is foreach by-definition guaranteed to iterate the subject collection (if it defines order) sequentially from the very first to the very last (unless accidentally interrupted) element? Aren't there any compiler optimization switches which can brake it (shuffle the sequence) or plans to make the ordinary foreach parallel in future versions? 回答1: Foreach is guaranteed to be sequential for sequential collections (that is, the normal hierarchy, or for anything transformed by .seq ). The parallel

问题 Have one hidden column with encrypted values and i want to copy and decrypt these values to another column, for speed up this process i'm using parallel for loop, but it's working only on my desktop PC, when i tried it on my notebook i get these errors: DataTable internal index corrupted: '5' DataTable internal index corrupted: '13' public void LoadKeyStarter() { DataTable dt; DataSet DS = new DataSet(); mySqlDataAdapter.Fill(DS); dt = DS.Tables[0]; dt.Columns.Add("Decrypted", typeof(System

问题 I have millions of log files which generating every day and I need to read all of them and put together as a single file to do some process on it in other app. I'm looking for the fastest way to do this. Currently I'm using Threads, Tasks and parallel like this: Parallel.For(0, files.Length, new ParallelOptions { MaxDegreeOfParallelism = 100 }, i => { ReadFiles(files[i]); }); void ReadFiles(string file) { try { var txt = File.ReadAllText(file); filesTxt.Add(tmp); } catch { } GlobalCls

问题 This may look like an innocuously simple problem but it takes a very long time to execute. Any ideas for speeding it up or vectorization etc. would be greatly appreciated. I have a R data frame with 5 million rows and 50 columns : OriginalDataFrame A list of Indices from that Frame : IndexList (55000 [ numIndex ] unique indices) Its a time series so there are ~ 5 Million rows for 55K unique indices. The OriginalDataFrame has been ordered by dataIndex . All the indices in IndexList are not

问题 I haven't worked with distributed computing before, but I'm trying to integrate mpi4py into a program in order to parallelize a for loop on a compute cluster. This is a pseudocode of what I want to do: for file in directory: Initialize a class Run class methods Conglomerate results I've looked all over stack overflow and I can't find any solution to this. Is there any way to do this simply with mpi4py, or is there another tool that can do it with easy installation and setup? 回答1: In order to