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I am having an issue with my attempt in speeding up the computation of my program. In the serialized python version of my code, I'm computing the values of a function f(x), which returns a float, for sliding windows of the NumPy array as can be seen below:
a = np.array([i for i in range(1, 10000000)]) # Some data here
N = 100
result = []
for i in range(N, len(a)):
result.append(f(a[i - N:i]))
Since the NumPy array is really large and f(x) runtime is high, I've tried to apply multiprocessing to speed up my code. Through my research, I found that charm4py might be a great solution and it has a Pool feature, which breaks up an array in chunks and distributes work between spawned processes. I've implemented charm4py's multiprocessing example and then, translated it to my case:
# Split an array into subarrays for sequential processing (takes only 5 seconds)
a = np.array([a[i - N:i] for i in range(N, len(a))])
result = charm.pool.map(f, a, chunksize=512, ncores=-1)
# I'm running this code through "charmrun +p18 example.py"
The issue that I've encountered is that code was running a lot slower, despite being executed on a more powerful instance (18 physical cores vs 6 physical cores).
I've expected to see ~3x improvement, but it didn't happen. While searching for solutions I've learned that there is some overhead for expensive deserialization/spinning up new processes, but I am not sure if this is the case.
I would really appreciate any feedback or suggestions on how one can implement fast parallel processing of a NumPy array (assuming that function f(x) is not vectorized, takes a pretty long time to compute, and internally makes a large number of specific/individual calls that cannot be parallelized)?
Thank you!
It sounds like you're trying to parallelize this operation with either Charm or Ray (it's not clear how you would use both together).
If you choose to use Ray, and your data is a numpy array, you can take advantage of zero-copy reads to avoid any deserialization overhead.
You may want to optimize your sliding window function a bit, but it will likely look like this:
#ray.remote
def apply_rolling(f, arr, start, end, window_size):
results_arr = []
for i in range(start, end - window_size):
results_arr.append(f(arr[i : i + windows_size])
return np.array(results_arr)
note that this structure lets us call f multiple times within a single task (aka batching).
To use our function:
# Some small setup
big_arr = np.arange(10000000)
big_arr_ref = ray.put(big_arr)
batch_size = len(big_arr) // ray.available_resources()["CPU"]
window_size = 100
# Kick off our tasks
result_refs = []
for i in range(0, big_arr, batch_size):
end_point = min(i + batch_size, len(big_arr))
ref = apply_rolling.remote(f, big_arr_ref, i, end_point)
result_refs.append(ref)
# Handle the results
flattened = []
for section in ray.get(result_refs):
flattened.extend(section)
I'm sure you'll want to customize this code, but here are 2 important and nice properties that you'll likely want to maintain.
Batching: We're utilizing batching to avoid starting too many tasks. In any system, parallelizing incurs overhead, so we always want to be careful and make sure we don't start too many tasks. Furthermore, we are calculating batch_size = len(big_arr) // ray.available_resources()["CPU"] to make sure we use exactly the same number of batches as we have CPUs.
Shared memory: Since Ray's object store supports zero copy reads from numpy arrays, calling ray.get or reading from a numpy array is pretty much free (on a single machine where there are no network costs). There is some overhead in serializing/calling ray.put though, so this approach only calls put (the expensive operation) once, and ray.get (which is implicitly called) many times.
Tip: Be careful when passing arrays as parameters directly into remote functions. It will call ray.put multiple times, even if you pass the same object.
Here's an example based off of your code snippet that uses Ray to parallelize the array computations.
Note that the best way to do this will depend on what your function f looks like.
import numpy as np
import ray
import time
ray.init()
N = 100000
a = np.arange(10**7)
a_id = ray.put(a)
#ray.remote
def f(array, index):
# Do processing
time.sleep(0.2)
return 1
result_ids = []
for i in range(len(a) // N):
result_ids.append(f.remote(a_id, i))
results = ray.get(result_ids)
I have a function which I will run using multi-processing. However the function returns a value and I do not know how to store that value once it's done.
I read somewhere online about using a queue but I don't know how to implement it or if that'd even work.
cores = []
for i in range(os.cpu_count()):
cores.append(Process(target=processImages, args=(dataSets[i],)))
for core in cores:
core.start()
for core in cores:
core.join()
Where the function 'processImages' returns a value. How do I save the returned value?
In your code fragment you have input dataSets which is a list of some unspecified size. You have a function processImages which takes a dataSet element and apparently returns a value you want to capture.
cpu_count == dataset length ?
The first problem I notice is that os.cpu_count() drives the range of values i which then determines which datasets you process. I'm going to assume you would prefer these two things to be independent. That is, you want to be able to crunch some X number of datasets and you want it to work on any machine, having anywhere from 1 - 1000 (or more...) cores.
An aside about CPU-bound work
I'm also going to assume that you have already determined that the task really is CPU-bound, thus it makes sense to split by core. If, instead, your task is disk io-bound, you would want more workers. You could also be memory bound or cache bound. If optimal parallelization is important to you, you should consider doing some trials to see which number of workers really gives you maximum performance.
Here's more reading if you like
Pool class
Anyway, as mentioned by Michael Butscher, the Pool class simplifies this for you. Yours is a standard use case. You have a set of work to be done (your list of datasets to be processed) and a number of workers to do it (in your code fragment, your number of cores).
TLDR
Use those simple multiprocessing concepts like this:
from multiprocessing import Pool
# Renaming this variable just for clarity of the example here
work_queue = datasets
# This is the number you might want to find experimentally. Or just run with cpu_count()
worker_count = os.cpu_count()
# This will create processes (fork) and join all for you behind the scenes
worker_pool = Pool(worker_count)
# Farm out the work, gather the results. Does not care whether dataset count equals cpu count
processed_work = worker_pool.map(processImages, work_queue)
# Do something with the result
print(processed_work)
You cannot return the variable from another process. The recommended way would be to create a Queue (multiprocessing.Queue), then have your subprocess put the results to that queue, and once it's done, you may read them back -- this works if you have a lot of results.
If you just need a single number -- using Value or Array could be easier.
Just remember, you cannot use a simple variable for that, it has to be wrapped with above mentioned classes from multiprocessing lib.
If you want to use the result object returned by a multiprocessing, try this
from multiprocessing.pool import ThreadPool
def fun(fun_argument1, ... , fun_argumentn):
<blabla>
return object_1, object_2
pool = ThreadPool(processes=number_of_your_process)
async_num1 = pool.apply_async(fun, (fun_argument1, ... , fun_argumentn))
object_1, object_2 = async_num1.get()
then you can do whatever you want.
I am trying to parallelize my code to find the similarity matrix using multiprocessing module in Python. It works fine when I use the small np.ndarray with 10 X 15 elements. But, when I scale my np.ndarray to 3613 X 7040 elements, system runs out of memory.
Below, is my code.
import multiprocessing
from multiprocessing import Pool
## Importing Jacard_similarity_score
from sklearn.metrics import jaccard_similarity_score
# Function for finding the similarities between two np arrays
def similarityMetric(a,b):
return (jaccard_similarity_score(a,b))
## Below functions are used for Parallelizing the scripts
# auxiliary funciton to make it work
def product_helper1(args):
return (similarityMetric(*args))
def parallel_product1(list_a, list_b):
# spark given number of processes
p = Pool(8)
# set each matching item into a tuple
job_args = getArguments(list_a,list_b)
# map to pool
results = p.map(product_helper1, job_args)
p.close()
p.join()
return (results)
## getArguments function is used to get the combined list
def getArguments(list_a,list_b):
arguments = []
for i in list_a:
for j in list_b:
item = (i,j)
arguments.append(item)
return (arguments)
Now when I run the below code, system runs out of memory and gets hanged. I am passing two numpy.ndarrays testMatrix1 and testMatrix2 which are of size (3613, 7040)
resultantMatrix = parallel_product1(testMatrix1,testMatrix2)
I am new to using this module in Python and trying to understand where I am going wrong. Any help is appreciated.
Odds are, the problem is just combinatoric explosion. You're trying to realize all the pairs in the main process up front, rather than generating them live, so you're storing a huge amount of memory. Assuming the ndarrays contain double values, which become Python float, then the memory usage of the list returned by getArguments is roughly the cost of a tuple and two floats per pair, or about:
3613 * 7040 * (sys.getsizeof((0., 0.)) + sys.getsizeof(0.) * 2)
On my 64 bit Linux system, that means ~2.65 GB of RAM on Py3, or ~2.85 GB on Py2, before the workers even do anything.
If you can process the data in a streaming fashion using a generator, so arguments are produced lazily and discarded when no longer needed, you could probably reduce memory usage dramatically:
import itertools
def parallel_product1(list_a, list_b):
# spark given number of processes
p = Pool(8)
# set each matching item into a tuple
# Returns a generator that lazily produces the tuples
job_args = itertools.product(list_a,list_b)
# map to pool
results = p.map(product_helper1, job_args)
p.close()
p.join()
return (results)
This still requires all the results to fit in memory; if product_helper returns floats, then the expected memory usage for the result list on a 64 bit machine would still be around 0.75 GB or so, which is pretty large; if you can process the results in a streaming fashion, iterating the results of p.imap or even better, p.imap_unordered (the latter returns results as computed, not in the order the generator produced the arguments) and writing them to disk or otherwise ensuring they're released in memory quickly would save a lot of memory; the following just prints them out, but writing them to a file in some reingestable format would also be reasonable.
def parallel_product1(list_a, list_b):
# spark given number of processes
p = Pool(8)
# set each matching item into a tuple
# Returns a generator that lazily produces the tuples
job_args = itertools.product(list_a,list_b)
# map to pool
for result in p.imap_unordered(product_helper1, job_args):
print(result)
p.close()
p.join()
The map method sends all data to the workers via inter-process communication. As currently done, this consumes a huge amount of resources, because you're sending
What I would suggest it to modify getArguments to make a list of tuple of indices in the matrix that need to be combined. That's only two numbers that have to be sent to the worker process, instead of two rows of a matrix! Each worker then knows which rows in the matrix to use.
Load the two matrices and store them in global variables before calling map. This way every worker has access to them. And as long as they're not modified in the workers, the OS's virtual memory manager will not copy identical memory pages, keeping memory usage down.
The upshot of the below is that I have an embarrassingly parallel for loop that I am trying to thread. There's a bit of rigamarole to explain the problem, but despite all the verbosity, I think this should be a rather trivial problem that the multiprocessing module is designed to solve easily.
I have a large length-N array of k distinct functions, and a length-N array of abcissa. Thanks to the clever solution provided by #senderle described in Efficient algorithm for evaluating a 1-d array of functions on a same-length 1d numpy array, I have a fast numpy-based algorithm that I can use to evaluate the functions at the abcissa to return a length-N array of ordinates:
def apply_indexed_fast(abcissa, func_indices, func_table):
""" Returns the output of an array of functions evaluated at a set of input points
if the indices of the table storing the required functions are known.
Parameters
----------
func_table : array_like
Length k array of function objects
abcissa : array_like
Length Npts array of points at which to evaluate the functions.
func_indices : array_like
Length Npts array providing the indices to use to choose which function
operates on each abcissa element. Thus func_indices is an array of integers
ranging between 0 and k-1.
Returns
-------
out : array_like
Length Npts array giving the evaluation of the appropriate function on each
abcissa element.
"""
func_argsort = func_indices.argsort()
func_ranges = list(np.searchsorted(func_indices[func_argsort], range(len(func_table))))
func_ranges.append(None)
out = np.zeros_like(abcissa)
for i in range(len(func_table)):
f = func_table[i]
start = func_ranges[i]
end = func_ranges[i+1]
ix = func_argsort[start:end]
out[ix] = f(abcissa[ix])
return out
What I'm now trying to do is use multiprocessing to parallelize the for loop inside this function. Before describing my approach, for clarity I'll briefly sketch how the algorithm #senderle developed works. If you can read the above code and understand it immediately, just skip the next paragraph of text.
First we find the array of indices that sorts the input func_indices, which we use to define the length-k func_ranges array of integers. The integer entries of func_ranges control the function that gets applied to the appropriate sub-array of the input abcissa, which works as follows. Let f be the i^th function in the input func_table. Then the slice of the input abcissa to which we should apply the function f is slice(func_ranges[i], func_ranges[i+1]). So once func_ranges is calculated, we can just run a simple for loop over the input func_table and successively apply each function object to the appropriate slice, filling our output array. See the code below for a minimal example of this algorithm in action.
def trivial_functional(i):
def f(x):
return i*x
return f
k = 250
func_table = np.array([trivial_functional(j) for j in range(k)])
Npts = 1e6
abcissa = np.random.random(Npts)
func_indices = np.random.random_integers(0,len(func_table)-1,Npts)
result = apply_indexed_fast(abcissa, func_indices, func_table)
So my goal now is to use multiprocessing to parallelize this calculation. I thought this would be straightforward using my usual trick for threading embarrassingly parallel for loops. But my attempt below raises an exception that I do not understand.
from multiprocessing import Pool, cpu_count
def apply_indexed_parallelized(abcissa, func_indices, func_table):
func_argsort = func_indices.argsort()
func_ranges = list(np.searchsorted(func_indices[func_argsort], range(len(func_table))))
func_ranges.append(None)
out = np.zeros_like(abcissa)
num_cores = cpu_count()
pool = Pool(num_cores)
def apply_funci(i):
f = func_table[i]
start = func_ranges[i]
end = func_ranges[i+1]
ix = func_argsort[start:end]
out[ix] = f(abcissa[ix])
pool.map(apply_funci, range(len(func_table)))
pool.close()
return out
result = apply_indexed_parallelized(abcissa, func_indices, func_table)
PicklingError: Can't pickle <type 'function'>: attribute lookup __builtin__.function failed
I have seen this elsewhere on SO: Multiprocessing: How to use Pool.map on a function defined in a class?. One by one, I have tried each method proposed there; in all cases, I get a "too many files open" error because the threads were never closed, or the adapted algorithm simply hangs. This seems like there should be a simple solution since this is nothing more than threading an embarrassingly parallel for loop.
Warning/Caveat:
You may not want to apply multiprocessing to this problem. You'll find that relatively simple operations on large arrays, the problems will be memory bound with numpy. The bottleneck is moving data from RAM to the CPU caches. The CPU is starved for data, so throwing more CPUs at the problem doesn't help much. Furthermore, your current approach will pickle and make a copy of the entire array for every item in your input sequence, which adds lots of overhead.
There are plenty of cases where numpy + multiprocessing is very effective, but you need to make sure you're working with a CPU-bound problem. Ideally, it's a CPU-bound problem with relatively small inputs and outputs to alleviate the overhead of pickling the input and output. For many of the problems that numpy is most often used for, that's not the case.
Two Problems with Your Current Approach
On to answering your question:
Your immediate error is due to passing in a function that's not accessible from the global scope (i.e. a function defined inside a function).
However, you have another issue. You're treating the numpy arrays as though they're shared memory that can be modified by each process. Instead, when using multiprocessing the original array will be pickled (effectively making a copy) and passed to each process independently. The original array will never be modified.
Avoiding the PicklingError
As a minimal example to reproduce your error, consider the following:
import multiprocessing
def apply_parallel(input_sequence):
def func(x):
pass
pool = multiprocessing.Pool()
pool.map(func, input_sequence)
pool.close()
foo = range(100)
apply_parallel(foo)
This will result in:
PicklingError: Can't pickle <type 'function'>: attribute lookup
__builtin__.function failed
Of course, in this simple example, we could simply move the function definition back into the __main__ namespace. However, in yours, you need it to refer to data that's passed in. Let's look at an example that's a bit closer to what you're doing:
import numpy as np
import multiprocessing
def parallel_rolling_mean(data, window):
data = np.pad(data, window, mode='edge')
ind = np.arange(len(data)) + window
def func(i):
return data[i-window:i+window+1].mean()
pool = multiprocessing.Pool()
result = pool.map(func, ind)
pool.close()
return result
foo = np.random.rand(20).cumsum()
result = parallel_rolling_mean(foo, 10)
There are multiple ways you could handle this, but a common approach is something like:
import numpy as np
import multiprocessing
class RollingMean(object):
def __init__(self, data, window):
self.data = np.pad(data, window, mode='edge')
self.window = window
def __call__(self, i):
start = i - self.window
stop = i + self.window + 1
return self.data[start:stop].mean()
def parallel_rolling_mean(data, window):
func = RollingMean(data, window)
ind = np.arange(len(data)) + window
pool = multiprocessing.Pool()
result = pool.map(func, ind)
pool.close()
return result
foo = np.random.rand(20).cumsum()
result = parallel_rolling_mean(foo, 10)
Great! It works!
However, if you scale this up to large arrays, you'll soon find that it will either run very slow (which you can speed up by increasing chunksize in the pool.map call) or you'll quickly run out of RAM (once you increase the chunksize).
multiprocessing pickles the input so that it can be passed to separate and independent python processes. This means you're making a copy of the entire array for every i you operate on.
We'll come back to this point in a bit...
multiprocessing Does not share memory between processes
The multiprocessing module works by pickling the inputs and passing them to independent processes. This means that if you modify something in one process the other process won't see the modification.
However, multiprocessing also provides primitives that live in shared memory and can be accessed and modified by child processes. There are a few different ways of adapting numpy arrays to use a shared memory multiprocessing.Array. However, I'd recommend avoiding those at first (read up on false sharing if you're not familiar with it). There are cases where it's very useful, but it's typically to conserve memory, not to improve performance.
Therefore, it's best to do all modifications to a large array in a single process (this is also a very useful pattern for general IO). It doesn't have to be the "main" process, but it's easiest to think about that way.
As an example, let's say we wanted to have our parallel_rolling_mean function take an output array to store things in. A useful pattern is something similar to the following. Notice the use of iterators and modifying the output only in the main process:
import numpy as np
import multiprocessing
def parallel_rolling_mean(data, window, output):
def windows(data, window):
padded = np.pad(data, window, mode='edge')
for i in xrange(len(data)):
yield padded[i:i + 2*window + 1]
pool = multiprocessing.Pool()
results = pool.imap(np.mean, windows(data, window))
for i, result in enumerate(results):
output[i] = result
pool.close()
foo = np.random.rand(20000000).cumsum()
output = np.zeros_like(foo)
parallel_rolling_mean(foo, 10, output)
print output
Hopefully that example helps clarify things a bit.
chunksize and performance
One quick note on performance: If we scale this up, it will get very slow very quickly. If you look at a system monitor (e.g. top/htop), you may notice that your cores are sitting idle most of the time.
By default, the master process pickles each input for each process and passes it in immediately and then waits until they're finished to pickle the next input. In many cases, this means that the master process works, then sits idle while the worker processes are busy, then the worker processes sit idle while the master process is pickling the next input.
The key is to increase the chunksize parameter. This will cause pool.imap to "pre-pickle" the specified number of inputs for each process. Basically, the master thread can stay busy pickling inputs and the worker processes can stay busy processing. The downside is that you're using more memory. If each input uses up a large amount of RAM, this can be a bad idea. If it doesn't, though, this can dramatically speed things up.
As a quick example:
import numpy as np
import multiprocessing
def parallel_rolling_mean(data, window, output):
def windows(data, window):
padded = np.pad(data, window, mode='edge')
for i in xrange(len(data)):
yield padded[i:i + 2*window + 1]
pool = multiprocessing.Pool()
results = pool.imap(np.mean, windows(data, window), chunksize=1000)
for i, result in enumerate(results):
output[i] = result
pool.close()
foo = np.random.rand(2000000).cumsum()
output = np.zeros_like(foo)
parallel_rolling_mean(foo, 10, output)
print output
With chunksize=1000, it takes 21 seconds to process a 2-million element array:
python ~/parallel_rolling_mean.py 83.53s user 1.12s system 401% cpu 21.087 total
But with chunksize=1 (the default) it takes about eight times as long (2 minutes, 41 seconds).
python ~/parallel_rolling_mean.py 358.26s user 53.40s system 246% cpu 2:47.09 total
In fact, with the default chunksize, it's actually far worse than a single-process implementation of the same thing, which takes only 45 seconds:
python ~/sequential_rolling_mean.py 45.11s user 0.06s system 99% cpu 45.187 total
I am trying to get to grips with multiprocessing in Python. I started by creating this code. It simply computes cos(i) for integers i and measures the time taken when one uses multiprocessing and when one does not. I am not observing any time difference. Here is my code:
import multiprocessing
from multiprocessing import Pool
import numpy as np
import time
def tester(num):
return np.cos(num)
if __name__ == '__main__':
starttime1 = time.time()
pool_size = multiprocessing.cpu_count()
pool = multiprocessing.Pool(processes=pool_size,
)
pool_outputs = pool.map(tester, range(5000000))
pool.close()
pool.join()
endtime1 = time.time()
timetaken = endtime1 - starttime1
starttime2 = time.time()
for i in range(5000000):
tester(i)
endtime2 = time.time()
timetaken2 = timetaken = endtime2 - starttime2
print( 'The time taken with multiple processes:', timetaken)
print( 'The time taken the usual way:', timetaken2)
I am observing no (or very minimal) difference between the two times measured. I am using a machine with 8 cores, so this is surprising. What have I done incorrectly in my code?
Note that I learned all of this from this.
http://pymotw.com/2/multiprocessing/communication.html
I understand that "joblib" might be more convenient for an example like this, but the ultimate thing that this needs to be applied to does not work with "joblib".
Your job seems the computation of a single cos value. This is going to be basically unnoticeable compared to the time of communicating with the slave.
Try making 5 computations of 1000000 cos values and you should see them going in parallel.
First, you wrote :
timetaken2 = timetaken = endtime2 - starttime2
So it is normal if you have the same times displayed. But this is not the important part.
I ran your code on my computer (i7, 4 cores), and I get :
('The time taken with multiple processes:', 14.95710802078247)
('The time taken the usual way:', 6.465447902679443)
The multiprocessed loop is slower than doing the for loop. Why?
The multiprocessing module can use multiple processes, but still has to work with the Python Global Interpreter Lock, wich means you can't share memory between your processes. So when you try to launch a Pool, you need to copy useful variables, process your calculation, and retrieve the result. This cost you a little time for every process, and makes you less effective.
But this happens because you do a very small computation : multiprocessing is only useful for larger calculation, when the memory copying and results retrieving is cheaper (in time) than the calculation.
I tried with following tester, which is much more expensive, on 2000 runs:
def expenser_tester(num):
A=np.random.rand(10*num) # creation of a random Array 1D
for k in range(0,len(A)-1): # some useless but costly operation
A[k+1]=A[k]*A[k+1]
return A
('The time taken with multiple processes:', 4.030329942703247)
('The time taken the usual way:', 8.180987119674683)
You can see that on an expensive calculation, it is more efficient with the multiprocessing, even if you don't always have what you could expect (I could have a x4 speedup, but I only got x2)
Keep in mind that Pool has to duplicate every bit of memory used in calculation, so it may be memory expensive.
If you really want to improve a small calculation like your example, make it big by grouping and sending a list of variable to the pool instead of one variable by process.
You should also know that numpy and scipy have a lot of expensive function written in C/Fortran and already parallelized, so you can't do anything much to speed them.
If the problem is cpu bounded then you should see the required speed-up (if the operation is long enough and overhead is not significant). But when multiprocessing (because memory is not shared between processes) it's easier to have a memory bound problem.