multiprocess

Yesterday i asked a question: Reading data in parallel with multiprocess I got very good answers, and i implemented the solution mentioned in the answer i marked as correct. def read_energies(motif): os.chdir("blabla/working_directory") complx_ener = pd.DataFrame() # complex function to fill that dataframe lig_ener = pd.DataFrame() # complex function to fill that dataframe return motif, complx_ener, lig_ener COMPLEX_ENERGIS = {} LIGAND_ENERGIES = {} p = multiprocessing.Pool(processes=CPU) for x in p.imap_unordered(read_energies, peptide_kd.keys()): COMPLEX_ENERGIS[x[0]] = x[1] LIGAND_ENERGIES

I was about to write a shell with C language. Here is the source code below: #include <unistd.h> #include <stdio.h> #include <string.h> #include <sys/wait.h> #include <stdlib.h> int getcmd(char *buf, int nbuf) { memset(buf, 0, nbuf); fgets(buf, nbuf, stdin); printf("pid: %d, ppid: %d\n", getpid(), getppid()); printf("buf: %s", buf); if(buf[0] == 0) {// EOF printf("end of getcmd\n"); return -1; } return 0; } int main(void) { static char buf[100]; int fd, r, ret; // Read and run input commands. while((ret = getcmd(buf, sizeof(buf))) >= 0){ if(fork() == 0) exit(0); wait(&r); } exit(0); } When I

I have a multidimensional array ( result ) that should be filled by some nested loops. Function fun() is a complex and time-consuming function. I want to fill my array elements in a parallel manner, so I can use all my system's processing power. Here's the code: import numpy as np def fun(x, y, z): # time-consuming computation... # ... return output dim1 = 10 dim2 = 20 dim3 = 30 result = np.zeros([dim1, dim2, dim3]) for i in xrange(dim1): for j in xrange(dim2): for k in xrange(dim3): result[i, j, k] = fun(i, j, k) My question is that "Can I parallelize this code or not? if yes, How?" I'm using

A few years ago, in the Windows environment, I did some testing, by letting multiple instances of CPU computation intensive + memory access intensive + I/O access intensive application run. I developed 2 versions: One is running under multi-processing, another is running under multi-threading. I found that the performance is much better for multi-processing. I read somewhere else (but I can't remember the site). Which states that the reason is that under multi-threading, they are "fighting" for a single memory pipeline and I/O pipeline, which makes the performance worse compared to multi

After spending a lot of time trying to wrap my head around multiprocessing I came up with this code which is a benchmark test: Example 1: from multiprocessing import Process class Alter(Process): def __init__(self, word): Process.__init__(self) self.word = word self.word2 = '' def run(self): # Alter string + test processing speed for i in range(80000): self.word2 = self.word2 + self.word if __name__=='__main__': # Send a string to be altered thread1 = Alter('foo') thread2 = Alter('bar') thread1.start() thread2.start() # wait for both to finish thread1.join() thread2.join() print(thread1.word2)

I am implementing a multithreaded program that uses different cores, and many threads are executed simultaneously. Each thread makes a printf() call, and the result is not readable. How can I make printf() atomic, so that a printf() call in one thread doesn't conflict with a printf() call in another? POSIX Specifications The POSIX specification includes these functions: getc_unlocked() getchar_unlocked() putc_unlocked() putchar_unlock() Versions of the functions getc() , getchar() , putc() , and putchar() respectively named getc_unlocked() , getchar_unlocked() , putc_unlocked() , and putchar