I think this is the simplest and most efficient way: @echo off echo %time% ( start call batch1.bat start call batch2.bat start call batch3.bat start call batch4.bat ) | set /P “=” echo %time% In this method the waiting state in the main file is event driven, so it does not consume any CPU time! … Read more
You can use the multiprocessing module. For this case I might use a processing pool: from multiprocessing import Pool pool = Pool() result1 = pool.apply_async(solve1, [A]) # evaluate “solve1(A)” asynchronously result2 = pool.apply_async(solve2, [B]) # evaluate “solve2(B)” asynchronously answer1 = result1.get(timeout=10) answer2 = result2.get(timeout=10) This will spawn processes that can do generic work for you. … Read more
The guidance on not using a thread abort is controversial. I think there is still a place for it but in exceptional circumstance. However you should always attempt to design around it and see it as a last resort. Example; You have a simple windows form application that connects to a blocking synchronous web service. … Read more
Try using multiprocessing.Manager to manage your queue and to also make it accessible to different workers. import multiprocessing def worker(name, que): que.put(“%d is done” % name) if __name__ == ‘__main__’: pool = multiprocessing.Pool(processes=3) m = multiprocessing.Manager() q = m.Queue() workers = pool.apply_async(worker, (33, q))
What Giulio Franco says is true for multithreading vs. multiprocessing in general. However, Python* has an added issue: There’s a Global Interpreter Lock that prevents two threads in the same process from running Python code at the same time. This means that if you have 8 cores, and change your code to use 8 threads, … Read more
General information A lot of functions have implicit multi-threading built-in, making a parfor loop not more efficient, when using these functions, than a serial for loop, since all cores are already being used. parfor will actually be a detriment in this case, since it has the allocation overhead, whilst being as parallel as the function … Read more
Concurrent programming regards operations that appear to overlap and is primarily concerned with the complexity that arises due to non-deterministic control flow. The quantitative costs associated with concurrent programs are typically both throughput and latency. Concurrent programs are often IO bound but not always, e.g. concurrent garbage collectors are entirely on-CPU. The pedagogical example of … Read more
Edit – I modified the script to optionally display the output of each process Here is a native batch solution that reliably runs a list of commands in parallel, never launching more than n processes at a time. It even has a mechanism built in to distribute the processes to specific CPUs or remote machines … Read more
If you use an operating system that uses copy-on-write fork() semantics (like any common unix), then as long as you never alter your data structure it will be available to all child processes without taking up additional memory. You will not have to do anything special (except make absolutely sure you don’t alter the object). … Read more
If your threads don’t do I/O, synchronization, etc., and there’s nothing else running, 1 thread per core will get you the best performance. However that very likely not the case. Adding more threads usually helps, but after some point, they cause some performance degradation. Not long ago, I was doing performance testing on a 2 … Read more