I have a python program which 1) Reads from a very large file from Disk(~95% time) and then 2) Process and Provide a relatively small output (~5% time). This Program is to be run on TeraBytes of files .
Now i am looking to Optimize this Program by utilizing Multi Processing and Multi Threading . The platform I am running is a Virtual Machine with 4 Processors on a virtual Machine .
I plan to have a scheduler Process which will execute 4 Processes (same as processors) and then Each Process should have some threads as most part is I/O . Each Thread will process 1 file & will report result to the Main Thread which in turn will report it back to scheduler Process via IPC . Scheduler can queue these and eventually write them to disk in ordered manner
So wondering How does one decide number of Processes and Threads to create for such scenario ? Is there a Mathematical way to figure out whats the best mix .
Thankyou
I think I would arrange it the inverse of what you are doing. That is, I would create a thread pool of a certain size that would be responsible for producing the results. The tasks that get submitted to this pool would be passed as an argument a processor pool that could be used by the worker thread for submitting the CPU-bound portions of work. In other words, the thread pool workers would primarily be doing all the disk-related operations and handing off to the processor pool any CPU-intensive work.
The size of the processor pool should just be the number of processors you have in your environment. It's difficult to give a precise size for the thread pool; it depends on how many concurrent disk operations it can handle before the law of diminishing returns come into play. It also depends on your memory: The larger the pool, the greater the memory resources that will be taken, especially if entire files have to be read into memory for processing. So, you may have to experiment with this value. The code below outlines these ideas. What you gain from the thread pool is overlapping of I/O operations greater than you would achieve if you just used a small processor pool:
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
from functools import partial
import os
def cpu_bound_function(arg1, arg2):
...
return some_result
def io_bound_function(process_pool_executor, file_name):
with open(file_name, 'r') as f:
# Do disk related operations:
. . . # code omitted
# Now we have to do a CPU-intensive operation:
future = process_pool_executor.submit(cpu_bound_function, arg1, arg2)
result = future.result() # get result
return result
file_list = [file_1, file_2, file_n]
N_FILES = len(file_list)
MAX_THREADS = 50 # depends on your configuration on how well the I/O can be overlapped
N_THREADS = min(N_FILES, MAX_THREADS) # no point in creating more threds than required
N_PROCESSES = os.cpu_count() # use the number of processors you have
with ThreadPoolExecutor(N_THREADS) as thread_pool_executor:
with ProcessPoolExecutor(N_PROCESSES) as process_pool_executor:
results = thread_pool_executor.map(partial(io_bound_function, process_pool_executor), file_list)
Important Note:
Another far simpler approach is to just have a single, processor pool whose size is greater than the number of CPU processors you have, for example, 25. The worker processes will do both I/O and CPU operations. Even though you have more processes than CPUs, many of the processes will be in a wait state waiting for I/O to complete allowing CPU-intensive work to run.
The downside to this approach is that the overhead in creating a N processes is far greater than the overhead in creating N threads + a small number of processes. However, as the running time of the tasks submitted to the pool becomes increasingly larger, then this increased overhead becomes decreasingly a smaller percentage of the total run time. So, if your tasks are not trivial, this could be a reasonably performant simplification.
Update: Benchmarks of Both Approaches
I did some benchmarks against the two approaches processing 24 files whose sizes were approximately 10,000KB (actually, these were just 3 different files processed 8 times each, so there might have been some caching done):
Method 1 (thread pool + processor pool)
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
from functools import partial
import os
from math import sqrt
import timeit
def cpu_bound_function(b):
sum = 0.0
for x in b:
sum += sqrt(float(x))
return sum
def io_bound_function(process_pool_executor, file_name):
with open(file_name, 'rb') as f:
b = f.read()
future = process_pool_executor.submit(cpu_bound_function, b)
result = future.result() # get result
return result
def main():
file_list = ['/download/httpd-2.4.16-win32-VC14.zip'] * 8 + ['/download/curlmanager-1.0.6-x64.exe'] * 8 + ['/download/Element_v2.8.0_UserManual_RevA.pdf'] * 8
N_FILES = len(file_list)
MAX_THREADS = 50 # depends on your configuration on how well the I/O can be overlapped
N_THREADS = min(N_FILES, MAX_THREADS) # no point in creating more threds than required
N_PROCESSES = os.cpu_count() # use the number of processors you have
with ThreadPoolExecutor(N_THREADS) as thread_pool_executor:
with ProcessPoolExecutor(N_PROCESSES) as process_pool_executor:
results = list(thread_pool_executor.map(partial(io_bound_function, process_pool_executor), file_list))
print(results)
if __name__ == '__main__':
print(timeit.timeit(stmt='main()', number=1, globals=globals()))
Method 2 (processor pool only)
from concurrent.futures import ProcessPoolExecutor
from math import sqrt
import timeit
def cpu_bound_function(b):
sum = 0.0
for x in b:
sum += sqrt(float(x))
return sum
def io_bound_function(file_name):
with open(file_name, 'rb') as f:
b = f.read()
result = cpu_bound_function(b)
return result
def main():
file_list = ['/download/httpd-2.4.16-win32-VC14.zip'] * 8 + ['/download/curlmanager-1.0.6-x64.exe'] * 8 + ['/download/Element_v2.8.0_UserManual_RevA.pdf'] * 8
N_FILES = len(file_list)
MAX_PROCESSES = 50 # depends on your configuration on how well the I/O can be overlapped
N_PROCESSES = min(N_FILES, MAX_PROCESSES) # no point in creating more threds than required
with ProcessPoolExecutor(N_PROCESSES) as process_pool_executor:
results = list(process_pool_executor.map(io_bound_function, file_list))
print(results)
if __name__ == '__main__':
print(timeit.timeit(stmt='main()', number=1, globals=globals()))
Results:
(I have 8 cores)
Thread Pool + Processor Pool: 13.5 seconds
Processor Pool Alone: 13.3 seconds
Conclusion: I would try the simpler approach first of just using a processor pool for everything. Now the tricky bit is deciding what the maximum number of processes to create, which was part of your original question and had a simple answer when all it was doing was the CPU-intensive computations. If the number of files you are reading are not too many, then the point is moot; you can have one process per file. But if you have hundreds of files, you will not want to have hundreds of processes in your pool (there is also an upper limit to how many processes you can create and again there are those nasty memory constraints). There is just no way I can give you an exact number. If you do have a large number of files, start with a smallish pool size and keep incrementing until you get no further benefit (of course, you probably do not want to be processing more files than some maximum number for these tests or you will be running forever just deciding on a good pool size for the real run).
For parallel processing:
I saw this question, and quoting the accepted answer:
In practice, it can be difficult to find the optimal number of threads and even that number will likely vary each time you run the program. So, theoretically, the optimal number of threads will be the number of cores you have on your machine. If your cores are "hyper threaded" (as Intel calls it) it can run 2 threads on each core. Then, in that case, the optimal number of threads is double the number of cores on your machine.
For multiprocessing:
Someone asked a similar question here, and the accepted answer said this:
If all of your threads/processes are indeed CPU-bound, you should run as many processes as the CPU reports cores. Due to HyperThreading, each physical CPU cores may be able to present multiple virtual cores. Call multiprocessing.cpu_count to get the number of virtual cores.
If only p of 1 of your threads is CPU-bound, you can adjust that number by multiplying by p. For example, if half your processes are CPU-bound (p = 0.5) and you have two CPUs with 4 cores each and 2x HyperThreading, you should start 0.5 * 2 * 4 * 2 = 8 processes.
The key here is understand what machine are you using, from that, you can choose a nearly optimal number of threads/processes to split the execution of you code. And I said nearly optimal because it will vary a little bit every time you run your script, so it'll be difficult to predict this optimal number from a mathematical point of view.
For your specific situation, if your machine has 4 cores, I would recommend you to only create 4 threads max, and then split them:
1 to the main thread.
3 for file reading and process.
using multiple processes to speed up IO performance may not be a good idea, check this and the sample code below it to see wether it is helpful
One idea can be to have a thread only reading the file (If I understood well, there is only one file) and pushing the independent parts (for ex. rows) into queue with messages.
The messages can be processed by 4 threads. In this way, you can optimize the load between the processors.
On a strongly I/O-bound process (like what you are describing), you do not necessarily need multithreading nor multiprocessing: you could also use more advanced I/O primitives from your OS.
For example on Linux you can submit read requests to the kernel along with a suitably sized mutable buffer and be notified when the buffer is filled. This can be done using the AIO API, for which I've written a pure-python binding: python-libaio (libaio on pypi)), or with the more recent io_uring API for which there seems to be a CFFI python binding (liburing on pypy) (I have neither used io_uring nor this python binding).
This removes the complexity of parallel processing at your level, may reduce the number of OS/userland context switches (reducing the cpu time even further), and lets the OS know more about what you are trying to do, giving it the opportunity of scheduling the IO more efficiently (in a virtualised environment I would not be surprised if it reduced the number of data copies, although I have not tried it myself).
Of course, the downside is that your program will be more tightly bound to the OS you are executing it on, requiring more effort to get it to run on another one.
Related
I'm bruteforcing a 8-digit pin on a ELF executable (it's for a CTF) and I'm using asynchronous parallel processing. The code is very fast but it fills the memory even faster.
It takes about 10% of the total iterations to fill 8gbs of ram, and I have no idea what's causing it. Any help?
from pwn import *
import multiprocessing as mp
from tqdm import tqdm
def check_pin(pin):
program = process('elf_exe')
program.recvn(36)
program.sendline(str(pin))
program.recvline()
program.recvline()
res = program.recvline()
program.close()
if 'Access denied.' in str(res):
return null, null
else:
return res, pin
def process_result(res, pin):
if(res != null):
print(pin)
if __name__ == '__main__':
print(f'Starting bruteforce on {mp.cpu_count()} cores :)\n')
pool = mp.Pool(mp.cpu_count())
min = 10000000
max = 99999999
for pin in tqdm(range(min, max)):
pool.apply_async(check_pin, args=(pin), callback=process_result)
pool.close()
pool.join()
Multiprocessing pools create several processes. Calls to apply_async create a task that is added to a shared data structure (eg. queue). The data structure is read by processes thanks to inter-process communication (IPC). The thing is apply_async return a synchronization object that you do not use and so there is not synchronizations. Items appended in the data structure take some memory space (at least 32*3=96 bytes due to 3 CPython objects being allocated) and the data structure grow in memory to hold the 89_999_999 items hence at least 8 GiB of RAM. The process are not fast enough to execute the work. What tqdm print is totally is completely misleading: it just print the processing of the number of task submitted, not the one executed that is only a tiny fraction. Almost all the work is done when tqdm print 100% and the submission loop is done. I actually doubt the "code is very fast" since it appears to run 90 millions process while running a process is known to be an expensive operation.
To speed up this code and avoid a big memory usage, you need to aggregate the work in bigger tasks. You can for example and a range of pin variable to be computed and add a loop in check_pin. A reasonable range size is for example 1000. Additionally, you need to accumulate the AsyncResult objects returned by apply_async in a list and perform periodic synchronizations when the list becomes too big so that processes does not have too much work and so the shared data structure can remain small. Here is a simple untested example:
lst = []
for rng in allRanges:
lst.append(pool.apply_async(check_pin, args=(rng), callback=process_result))
if len(lst) > 100:
# Naive synchronization
for i in lst:
i.wait()
lst = []
I have edited the code , currently it is working fine . But thinks it is not executing parallely or dynamically . Can anyone please check on to it
Code :
def folderStatistic(t):
j, dir_name = t
row = []
for content in dir_name.split(","):
row.append(content)
print(row)
def get_directories():
import csv
with open('CONFIG.csv', 'r') as file:
reader = csv.reader(file,delimiter = '\t')
return [col for row in reader for col in row]
def folderstatsMain():
freeze_support()
start = time.time()
pool = Pool()
worker = partial(folderStatistic)
pool.map(worker, enumerate(get_directories()))
def datatobechecked():
try:
folderstatsMain()
except Exception as e:
# pass
print(e)
if __name__ == '__main__':
datatobechecked()
Config.CSV
C:\USERS, .CSV
C:\WINDOWS , .PDF
etc.
There may be around 200 folder paths in config.csv
welcome to StackOverflow and Python programming world!
Moving on to the question.
Inside the get_directories() function you open the file in with context, get the reader object and close the file immediately after the moment you leave the context so when the time comes to use the reader object the file is already closed.
I don't want to discourage you, but if you are very new to programming do not dive into parallel programing yet. Difficulty in handling multiple threads simultaneously grows exponentially with every thread you add (pools greatly simplify this process though). Processes are even worse as they don't share memory and can't communicate with each other easily.
My advice is, try to write it as a single-thread program first. If you have it working and still need to parallelize it, isolate a single function with input file path as a parameter that does all the work and then use thread/process pool on that function.
EDIT:
From what I can understand from your code, you get directory names from the CSV file and then for each "cell" in the file you run parallel folderStatistics. This part seems correct. The problem may lay in dir_name.split(","), notice that you pass individual "cells" to the folderStatistics not rows. What makes you think it's not running paralelly?.
There is a certain amount of overhead in creating a multiprocessing pool because creating processes is, unlike creating threads, a fairly costly operation. Then those submitted tasks, represented by each element of the iterable being passed to the map method, are gathered up in "chunks" and written to a multiprocessing queue of tasks that are read by the pool processes. This data has to move from one address space to another and that has a cost associated with it. Finally when your worker function, folderStatistic, returns its result (which is None in this case), that data has to be moved from one process's address space back to the main process's address space and that too has a cost associated with it.
All of those added costs become worthwhile when your worker function is sufficiently CPU-intensive such that these additional costs is small compared to the savings gained by having the tasks run in parallel. But your worker function's CPU requirements are so small as to reap any benefit from multiprocessing.
Here is a demo comparing single-processing time vs. multiprocessing times for invoking a worker function, fn, twice where the first time it only performs its internal loop 10 times (low CPU requirements) while the second time it performs its internal loop 1,000,000 times (higher CPU requirements). You can see that in the first case the multiprocessing version runs considerable slower (you can't even measure the time for the single processing run). But when we make fn more CPU-intensive, then multiprocessing achieves gains over the single-processing case.
from multiprocessing import Pool
from functools import partial
import time
def fn(iterations, x):
the_sum = x
for _ in range(iterations):
the_sum += x
return the_sum
# required for Windows:
if __name__ == '__main__':
for n_iterations in (10, 1_000_000):
# single processing time:
t1 = time.time()
for x in range(1, 20):
fn(n_iterations, x)
t2 = time.time()
# multiprocessing time:
worker = partial(fn, n_iterations)
t3 = time.time()
with Pool() as p:
results = p.map(worker, range(1, 20))
t4 = time.time()
print(f'#iterations = {n_iterations}, single processing time = {t2 - t1}, multiprocessing time = {t4 - t3}')
Prints:
#iterations = 10, single processing time = 0.0, multiprocessing time = 0.35399389266967773
#iterations = 1000000, single processing time = 1.182999849319458, multiprocessing time = 0.5530076026916504
But even with a pool size of 8, the running time is not reduced by a factor of 8 (it's more like a factor of 2) due to the fixed multiprocessing overhead. When I change the number of iterations for the second case to be 100,000,000 (even more CPU-intensive), we get ...
#iterations = 100000000, single processing time = 109.3077495098114, multiprocessing time = 27.202054023742676
... which is a reduction in running time by a factor of 4 (I have many other processes running in my computer, so there is competition for the CPU).
I am trying to speed up some code with multiprocessing in Python, but I cannot understand one point. Assume I have the following dumb function:
import time
from multiprocessing.pool import Pool
def foo(_):
for _ in range(100000000):
a = 3
When I run this code without using multiprocessing (see the code below) on my laptop (Intel - 8 cores cpu) time taken is ~2.31 seconds.
t1 = time.time()
foo(1)
print(f"Without multiprocessing {time.time() - t1}")
Instead, when I run this code by using Python multiprocessing library (see the code below) time taken is ~6.0 seconds.
pool = Pool(8)
t1 = time.time()
pool.map(foo, range(8))
print(f"Sample multiprocessing {time.time() - t1}")
From the best of my knowledge, I understand that when using multiprocessing there is some time overhead mainly caused by the need to spawn the new processes and to copy the memory state. However, this operation should be performed just once when the processed are initially spawned at the very beginning and should not be that huge.
So what I am missing here? Is there something wrong in my reasoning?
Edit: I think it is better to be more explicit on my question. What I expected here was the multiprocessed code to be slightly slower than the sequential one. It is true that I don't split the whole work across the 8 cores, but I am using 8 cores in parallel to do the same job (hence in an ideal world the processing time should more or less stay the same). Considering the overhead of spawning new processes, I expected a total increase in time of some (not too big) percentage, but not of a ~2.60x increase as I got here.
Well, multiprocessing can't possibly make this faster: you're not dividing the work across 8 processes, you're asking each of 8 processes to do the entire thing. Each process will take at least as long as your code doing it just once without using multiprocessing.
So if multiprocessing weren't helping at all, you'd expect it to take about 8 times as long (it's doing 8x the work!) as your single-processor run. But you said it's not taking 2.31 * 8 ~= 18.5 seconds, but "only" about 6. So you're getting better than a factor of 3 speedup.
Why not more than that? Can't guess from here. That will depend on how many physical cores your machine has, and how much other stuff you're running at the same time. Each process will be 100% CPU-bound for this specific function, so the number of "logical" cores is pretty much irrelevant - there's scant opportunity for processor hyper-threading to help. So I'm guessing you have 4 physical cores.
On my box
Sample timing on my box, which has 8 logical cores but only 4 physical cores, and otherwise left the box pretty quiet:
Without multiprocessing 2.468580484390259
Sample multiprocessing 4.78624415397644
As above, none of that surprises me. In fact, I was a little surprised (but pleasantly) at how effectively the program used up the machine's true capacity.
#TimPeters already answered that you are actually just running the job 8 times across the 8 Pool subprocesses, so it is slower not faster.
That answers the issue but does not really answer what your real underlying question was. It is clear from your surprise at this result, that you were expecting that the single job to somehow be automatically split up and run in parts across the 8 Pool processes. That is not the way that it works. You have to build in/tell it how to split up the work.
Different kinds of jobs needs need to be subdivided in different ways, but to continue with your example you might do something like this:
import time
from multiprocessing.pool import Pool
def foo(_):
for _ in range(100000000):
a = 3
def foo2(job_desc):
start, stop = job_desc
print(f"{start}, {stop}")
for _ in range(start, stop):
a = 3
def main():
t1 = time.time()
foo(1)
print(f"Without multiprocessing {time.time() - t1}")
pool_size = 8
pool = Pool(pool_size)
t1 = time.time()
top_num = 100000000
size = top_num // pool_size
job_desc_list = [[size * j, size * (j+1)] for j in range(pool_size)]
# this is in case the the upper bound is not a multiple of pool_size
job_desc_list[-1][-1] = top_num
pool.map(foo2, job_desc_list)
print(f"Sample multiprocessing {time.time() - t1}")
if __name__ == "__main__":
main()
Which results in:
Without multiprocessing 3.080709171295166
0, 12500000
12500000, 25000000
25000000, 37500000
37500000, 50000000
50000000, 62500000
62500000, 75000000
75000000, 87500000
87500000, 100000000
Sample multiprocessing 1.5312283039093018
As this shows, splitting the job up does allow it to take less time. The speedup will depend on the number of CPUs. In a CPU bound job you should try to limit it the pool size to the number of CPUs. My laptop has plenty more CPU's but some of the benefit is lost to the overhead. If the jobs were longer this should look more useful.
I'm beginner to python and machine learning. I'm trying to reproduce the code for countvectorizer() using multi-threading. I'm working with yelp dataset to do sentiment analysis using LogisticRegression. This is what I've written so far:
Code snippet:
from multiprocessing.dummy import Pool as ThreadPool
from threading import Thread, current_thread
from functools import partial
data = df['text']
rev = df['stars']
y = []
def product_helper(args):
return featureExtraction(*args)
def featureExtraction(p,t):
temp = [0] * len(bag_of_words)
for word in p.split():
if word in bag_of_words:
temp[bag_of_words.index(word)] += 1
return temp
# function to be mapped over
def calculateParallel(threads):
pool = ThreadPool(threads)
job_args = [(item_a, rev[i]) for i, item_a in enumerate(data)]
l = pool.map(product_helper,job_args)
pool.close()
pool.join()
return l
temp_X = calculateParallel(12)
Here this is just part of code.
Explanation:
df['text'] has all the reviews and df['stars'] has the ratings (1 through 5). I'm trying to find the word count vector temp_X using multi-threading. bag_of_words is a list of some frequent words of choice.
Question:
Without multi-threading , I was able to compute the temp_X in around 24 minutes and the above code took 33 mins for a dataset of size 100k reviews. My machine has 128GB of DRAM and 12 cores (6 physical cores with hyperthreading i.e., threads per core=2).
What am I doing wrong here?
Your whole code seems CPU Bound rather than IO Bound.You are just using threads which are under GIL so effectively running just one thread plus overheads.It runs only on one core.To run on multiple cores use
Use
import multiprocessing
pool = multiprocessing.Pool()
l = pool.map_async(product_helper,job_args)
from multiprocessing.dummy import Pool as ThreadPool is just a wrapper over thread module.It utilises just one core and not more than that.
Python and threads dont really work together very well. There is a known issue called the GIL (global interperter lock). Basically there is a lock in the interperter that makes all threads not run in parallel (even if you have multiple cpu cores). Python will simply give each thread a few milliseconds of cpu time one after another (and the reason it became slower is the overhead from context switching between those threads).
Here is a really good document explaining how it works: http://www.dabeaz.com/python/UnderstandingGIL.pdf
To fix your problem i suggest you try multi processing:
https://pymotw.com/2/multiprocessing/basics.html
Note: multiprocessing is not 100% equivilent to multithreading. Multiprocessing will run at parallel but the diffrent processes wont share memory so if you change a variable in one of them it will not be changed in the other process.
I need to run multiple instances of a binary in parallel. For this I am using python multiprocessing module. The binary itself has a parallelization which can be set using OMP_NUM_THREADS environment variable. A minimalist example of my code is the following
import sys
import os
from numpy import *
import time
import xml.etree.ElementTree as ET
from multiprocessing import Process, Queue
def cal_dist(filename):
tic = time.time()
################################### COPY THE INPUP FILE ########################################
tree = ET.parse(inputfilename+'.feb')
tree.write(filename+'.feb',xml_declaration=True,encoding="ISO-8859-1")
##################################### SUBMIT THE JOB ###########################################
os.system('export OMP_NUM_THREADS=12')
os.system('$HOME/febiosource-2.0/bin/febio2.lnx64 -noconfig -i ' + filename + '.feb -silent')
toc = time.time()
print "Job %s completed in %5.2f minutes" %(filename,(toc-tic)/60.);
return
# INPUT PARAMETERS
inputfilename="main-step1"
tempfilename='temp';
nCPU=7;
for iter in range(0,1):
################################### PARALLEL PROCESSING STARTS ########################################
# CREATE ALL THE PROCESSES,
p=[];
maxj=nCPU;
for j in range(0,nCPU):
p.append(Process(target=cal_dist, args=(tempfilename+str(j),)))
# START THE PROCESSES,
for j in range(0,nCPU):
p[j].start();
time.sleep(0.2);
# JOIN THEM,
for j in range(0,nCPU):
p[j].join();
################################### PARALLEL PROCESSING ENDS ########################################
If I set OMP_NUM_THREADS=1, then increasing the nCPU gives a good scaling. That is,
for nCPU=1, job time=3.5 minutes
for nCPU=7, job time=4.2 minutes
However, if I set OMP_NUM_THREADS=12, then increasing the nCPU gives a very bad scaling. That is,
for nCPU=1, job time=3.4 minutes
for nCPU=5, job time=5.7 minutes
for nCPU=7, job time=7.5 minutes
Any ideas on how I can solve this issue? I really need to use high number of CPUs and OMP_NUM_THREADS for my actual problem (and I know that the architecture of computer is that each node has 12 processors and I run it on nCPU*12 number of processors.
It looks like you're overloading your CPUs. With nCPU set to 1 with OMP_NUM_THREADS=12, you're spawning one process that uses twelve threads, which means you're keeping all your CPUs fully saturated. When you set nCPU to 7 with OMP_NUM_THREADS=12, you're spawning seven processes that use twelve threads each, which means you've got 12 * 7 = 84 threads running in parallel, fighting over 12 CPUs. My guess is this is creating a high context-switching overhead for the OS, and that's slowing you down.
With only 12 CPUs to work with, you're going to get diminishing returns if you try to run more than 12 threads+processes in parallel. (Unless a bunch of the work being done is I/O-bound, which doesn't seem to be the case here.)