Python multiprocessing: sharing global real-only large data without reloading from disk for child processes Question: Say I need to read from disk a large data and do some read-only work on it. I need to use multiprocessing, but to share it across processes using multiprocessing.Manager() or Array() is way too slow. Since my operation on …
Apply slicing, conditionals to Sparse Arrays with Pallalization in Python Question: Apply slicing, conditionals to Sparse Arrays with Pallalization I want to do something like dynamic programming on sparse array. could you check the following example function,which I would like to implement for Sparse Array (the first example is for numpy.array) First,importing modules from numba …
Why parallelized code with concurrent.futures is slower then regular code? Question: I tried parallelizing with concurrent.futures expecting that parallelized code will be faster. I made a dafault code to test the parallelization. It is not important what the code does. I’m mainly interested in the speed of the dafault code and parallelized code. All it …
Multi-Processing nested loops in Python? Question: I have a multi-nested for loop and I’d like to parallelize this as much as possible, in Python. Suppose I have some arbitrary function, which accepts two arguments func(a,b) and I’d like to compute this function on all combinations of M and N. What I’ve done so far is …
The printed outputs being broken when doing parallel computation by multiprocessing module in Python Question: I suspect this might be a classic problem but I’ve couldn’t find the keyword concerning this issue so I’m here to ask for advice. Considering the following code regarding multiprocessing module in Python import multiprocessing def parallel(n): while n <= …
How to parallel the following code using Multiprocessing in Python Question: I have a function generate(file_path) which returns an integer index and a numpy array. The simplified of generate function is as follows: def generate(file_path): temp = np.load(file_path) #get index from the string file_path idx = int(file_path.split["_"][0]) #do some mathematical operation on temp result = …
Parallel processing with rapidfuzz function Question: I have a dataset of 100 000 records. My problem is many to many type where i need to calculate the fuzzy score of name column in each row with 100k rows. I am using for loop to iterate each row and calculating the fuzz score by using pandas …
How to combine parallelization and concurrency in a single python library? Question: I want to speed up my code by using concurrency and parallelization within one library using Python. My current set up goes in single thread: – input a, b, c – downloading data into a list: data = [downloadedData(a), downloadedData(b), downloadedData(c)] – transforming …
Retrieving all numpy array indices where condition Question: Say I have a numpy array, a, of elements (which do not repeat) np.array([1,3,5,2,4]), I would like to retrieve the indices a contains [4,2]. Desired output: np.array([3,4]) as these are the indices the requested elements. So far, I’ve tried np.all(np.array([[1,2,3,4]]).transpose(), axis=1, where=lambda x: x in [1,2]) >>> …
How to create lists by running functions in parallel in python Question: I want to create two lists by running two functions (returning a value each for every run) in parallel. My code below works, but is still taking too much time. Is there a more efficient way to parallelize this code? import time from …
