What factors determine an optimal
chunksize argument to methods like
.map() method seems to use an arbitrary heuristic for its default chunksize (explained below); what motivates that choice and is there a more thoughtful approach based on some particular situation/setup?
Example – say that I am:
- Passing an
.map()that has ~15 million elements;
- Working on a machine with 24 cores and using the default
processes = os.cpu_count()within
My naive thinking is to give each of 24 workers an equally-sized chunk, i.e.
15_000_000 / 24 or 625,000. Large chunks should reduce turnover/overhead while fully utilizing all workers. But it seems that this is missing some potential downsides of giving large batches to each worker. Is this an incomplete picture, and what am I missing?
Part of my question stems from the default logic for if
.map_async(), which looks like this:
def _map_async(self, func, iterable, mapper, chunksize=None, callback=None, error_callback=None): # ... (materialize `iterable` to list if it's an iterator) if chunksize is None: chunksize, extra = divmod(len(iterable), len(self._pool) * 4) # ???? if extra: chunksize += 1 if len(iterable) == 0: chunksize = 0
What’s the logic behind
divmod(len(iterable), len(self._pool) * 4)? This implies that the chunksize will be closer to
15_000_000 / (24 * 4) == 156_250. What’s the intention in multiplying
len(self._pool) by 4?
This makes the resulting chunksize a factor of 4 smaller than my “naive logic” from above, which consists of just dividing the length of the iterable by number of workers in
Lastly, there is also this snippet from the Python docs on
.imap() that further drives my curiosity:
chunksizeargument is the same as the one used by the
method. For very long iterables using a large value for
make the job complete much faster than using the default value of 1.
Related answer that is helpful but a bit too high-level: Python multiprocessing: why are large chunksizes slower?.
Pool’s chunksize-algorithm is a heuristic. It provides a simple solution for all imaginable problem scenarios you are trying to stuff into Pool’s methods. As a consequence, it cannot be optimized for any specific scenario.
The algorithm arbitrarily divides the iterable in approximately four times more chunks than the naive approach. More chunks mean more overhead, but increased scheduling flexibility. How this answer will show, this leads to a higher worker-utilization on average, but without the guarantee of a shorter overall computation time for every case.
“That’s nice to know” you might think, “but how does knowing this help me with my concrete multiprocessing problems?” Well, it doesn’t. The more honest short answer is, “there is no short answer”, “multiprocessing is complex” and “it depends”. An observed symptom can have different roots, even for similar scenarios.
This answer tries to provide you with basic concepts helping you to get a clearer picture of Pool’s scheduling black box. It also tries to give you some basic tools at hand for recognizing and avoiding potential cliffs as far they are related to chunksize.
Table of Contents
- Parallelization Goals
- Parallelization Scenarios
- Risks of Chunksize > 1
- Pool’s Chunksize-Algorithm
Quantifying Algorithm Efficiency
6.2 Parallel Schedule
6.3.1 Absolute Distribution Efficiency (ADE)
6.3.2 Relative Distribution Efficiency (RDE)
- Naive vs. Pool’s Chunksize-Algorithm
- Reality Check
It is necessary to clarify some important terms first.
A chunk here is a share of the
iterable-argument specified in a pool-method call. How the chunksize gets calculated and what effects this can have, is the topic of this answer.
A task’s physical representation in a worker-process in terms of data can be seen in the figure below.
The figure shows an example call to
pool.map(), displayed along a line of code, taken from the
multiprocessing.pool.worker function, where a task read from the
inqueue gets unpacked.
worker is the underlying main-function in the
MainThread of a pool-worker-process. The
func-argument specified in the pool-method will only match the
func-variable inside the
worker-function for single-call methods like
apply_async and for
chunksize=1. For the rest of the pool-methods with a
chunksize-parameter the processing-function
func will be a mapper-function (
starmapstar). This function maps the user-specified
func-parameter on every element of the transmitted chunk of the iterable (–> “map-tasks”). The time this takes, defines a task also as a unit of work.
While the usage of the word “task” for the whole processing of one chunk is matched by code within
multiprocessing.pool, there is no indication how a single call to the user-specified
func, with one
element of the chunk as argument(s), should be referred to. To avoid confusion emerging from naming conflicts (think of
maxtasksperchild-parameter for Pool’s
__init__-method), this answer will refer to
the single units of work within a task as taskel.
A taskel (from task + element) is the smallest unit of work within a task.
It is the single execution of the function specified with the
func-parameter of a
Pool-method, called with arguments obtained from a single element of the transmitted chunk.
A task consists of
Parallelization Overhead (PO)
PO consists of Python-internal overhead and overhead for inter-process communication (IPC). The per-task overhead within Python comes with the code needed for packaging and unpacking the tasks and its results. IPC-overhead comes with the necessary synchronization of threads and the copying of data between different address spaces (two copy steps needed: parent -> queue -> child). The amount of IPC-overhead is OS-, hardware- and data-size dependent, what makes generalizations about the impact difficult.
2. Parallelization Goals
When using multiprocessing, our overall goal (obviously) is to minimize total processing time for all tasks. To reach this overall goal, our technical goal needs to be optimizing the utilization of hardware resources.
Some important sub-goals for achieving the technical goal are:
- minimize parallelization overhead (most famously, but not alone: IPC)
- high utilization across all cpu-cores
- keeping memory usage limited to prevent the OS from excessive paging (trashing)
At first, the tasks need to be computationally heavy (intensive) enough, to earn back the PO we have to pay for parallelization. The relevance of PO decreases with increasing absolute computation time per taskel. Or, to put it the other way around, the bigger the absolute computation time per taskel for your problem, the less relevant gets the need for reducing PO. If your computation will take hours per taskel, the IPC overhead will be negligible in comparison. The primary concern here is to prevent idling worker processes after all tasks have been distributed. Keeping all cores loaded means, we are parallelizing as much as possible.
3. Parallelization Scenarios
What factors determine an optimal chunksize argument to methods like multiprocessing.Pool.map()
The major factor in question is how much computation time may vary across our single taskels. To name it, the choice for an optimal chunksize is determined by the Coefficient of Variation (CV) for computation times per taskel.
The two extreme scenarios on a scale, following from the extent of this variation are:
- All taskels need exactly the same computation time.
- A taskel could take seconds or days to finish.
For better memorability, I will refer to these scenarios as:
- Dense Scenario
- Wide Scenario
In a Dense Scenario it would be desirable to distribute all taskels at once, to keep necessary IPC and context switching at a minimum. This means we want to create only as much chunks, as much worker processes there are. How already stated above, the weight of PO increases with shorter computation times per taskel.
For maximal throughput, we also want all worker processes busy until all tasks are processed (no idling workers). For this goal, the distributed chunks should be of equal size or close to.
The prime example for a Wide Scenario would be an optimization problem, where results either converge quickly or computation can take hours, if not days. Usually it is not predictable what mixture of “light taskels” and “heavy taskels” a task will contain in such a case, hence it’s not advisable to distribute too many taskels in a task-batch at once. Distributing less taskels at once than possible, means increasing scheduling flexibility. This is needed here to reach our sub-goal of high utilization of all cores.
Pool methods, by default, would be totally optimized for the Dense Scenario, they would increasingly create suboptimal timings for every problem located closer to the Wide Scenario.
4. Risks of Chunksize > 1
Consider this simplified pseudo-code example of a Wide Scenario-iterable, which we want to pass into a pool-method:
good_luck_iterable = [60, 60, 86400, 60, 86400, 60, 60, 84600]
Instead of the actual values, we pretend to see the needed computation time in seconds, for simplicity only 1 minute or 1 day.
We assume the pool has four worker processes (on four cores) and
chunksize is set to
2. Because the order will be kept, the chunks send to the workers will be these:
[(60, 60), (86400, 60), (86400, 60), (60, 84600)]
Since we have enough workers and the computation time is high enough, we can say, that every worker process will get a chunk to work on in the first place. (This does not have to be the case for fast completing tasks). Further we can say, the whole processing will take about 86400+60 seconds, because that’s the highest total computation time for a chunk in this artificial scenario and we distribute chunks only once.
Now consider this iterable, which has only one element switching its position compared to the previous iterable:
bad_luck_iterable = [60, 60, 86400, 86400, 60, 60, 60, 84600]
…and the corresponding chunks:
[(60, 60), (86400, 86400), (60, 60), (60, 84600)]
Just bad luck with the sorting of our iterable nearly doubled (86400+86400) our total processing time! The worker getting the vicious (86400, 86400)-chunk is blocking the second heavy taskel in its task from getting distributed to one of the idling workers already finished with their (60, 60)-chunks. We obviously would not risk such an unpleasant outcome if we set
This is the risk of bigger chunksizes. With higher chunksizes we trade scheduling flexibility for less overhead and in cases like above, that’s a bad deal.
How we will see in chapter 6. Quantifying Algorithm Efficiency, bigger chunksizes can also lead to suboptimal results for Dense Scenarios.
5. Pool’s Chunksize-Algorithm
Below you will find a slightly modified version of the algorithm inside the source code. As you can see, I cut off the lower part and wrapped it into a function for calculating the
chunksize argument externally. I also replaced
4 with a
factor parameter and outsourced the
# mp_utils.py def calc_chunksize(n_workers, len_iterable, factor=4): """Calculate chunksize argument for Pool-methods. Resembles source-code within `multiprocessing.pool.Pool._map_async`. """ chunksize, extra = divmod(len_iterable, n_workers * factor) if extra: chunksize += 1 return chunksize
To ensure we are all on the same page, here’s what
divmod(x, y) is a builtin function which returns
x // y is the floor division, returning the down rounded quotient from
x / y, while
x % y is the modulo operation returning the remainder from
x / y.
divmod(10, 3) returns
Now when you look at
chunksize, extra = divmod(len_iterable, n_workers * 4), you will notice
n_workers here is the divisor
x / y and multiplication by
4, without further adjustment through
if extra: chunksize +=1 later on, leads to an initial chunksize at least four times smaller (for
len_iterable >= n_workers * 4) than it would be otherwise.
For viewing the effect of multiplication by
4 on the intermediate chunksize result consider this function:
def compare_chunksizes(len_iterable, n_workers=4): """Calculate naive chunksize, Pool's stage-1 chunksize and the chunksize for Pool's complete algorithm. Return chunksizes and the real factors by which naive chunksizes are bigger. """ cs_naive = len_iterable // n_workers or 1 # naive approach cs_pool1 = len_iterable // (n_workers * 4) or 1 # incomplete pool algo. cs_pool2 = calc_chunksize(n_workers, len_iterable) real_factor_pool1 = cs_naive / cs_pool1 real_factor_pool2 = cs_naive / cs_pool2 return cs_naive, cs_pool1, cs_pool2, real_factor_pool1, real_factor_pool2
The function above calculates the naive chunksize (
cs_naive) and the first-step chunksize of Pool’s chunksize-algorithm (
cs_pool1), as well as the chunksize for the complete Pool-algorithm (
cs_pool2). Further it calculates the real factors
rf_pool1 = cs_naive / cs_pool1 and
rf_pool2 = cs_naive / cs_pool2, which tell us how many times the naively calculated chunksizes are bigger than Pool’s internal version(s).
Below you see two figures created with output from this function. The left figure just shows the chunksizes for
n_workers=4 up until an iterable length of
500. The right figure shows the values for
rf_pool1. For iterable length
16, the real factor becomes
len_iterable >= n_workers * 4) and it’s maximum value is
7 for iterable lengths
28-31. That’s a massive deviation from the original factor
4 the algorithm converges to for longer iterables. ‘Longer’ here is relative and depends on the number of specified workers.
cs_pool1 still lacks the
extra-adjustment with the remainder from
divmod contained in
cs_pool2 from the complete algorithm.
The algorithm goes on with:
if extra: chunksize += 1
Now in cases were there is a remainder (an
extra from the divmod-operation), increasing the chunksize by 1 obviously cannot work out for every task. After all, if it would, there would not be a remainder to begin with.
How you can see in the figures below, the “extra-treatment” has the effect, that the real factor for
rf_pool2 now converges towards
4 from below
4 and the deviation is somewhat smoother. Standard deviation for
len_iterable=500 drops from
chunksize by 1 has the effect, that the last task transmitted only has a size of
len_iterable % chunksize or chunksize.
The more interesting and how we will see later, more consequential, effect of the extra-treatment however can be observed for the number of generated chunks (
For long enough iterables, Pool’s completed chunksize-algorithm (
n_pool2 in the figure below) will stabilize the number of chunks at
n_chunks == n_workers * 4.
In contrast, the naive algorithm (after an initial burp) keeps alternating between
n_chunks == n_workers and
n_chunks == n_workers + 1 as the length of the iterable grows.
Below you will find two enhanced info-functions for Pool’s and the naive chunksize-algorithm. The output of these functions will be needed in the next chapter.
# mp_utils.py from collections import namedtuple Chunkinfo = namedtuple( 'Chunkinfo', ['n_workers', 'len_iterable', 'n_chunks', 'chunksize', 'last_chunk'] ) def calc_chunksize_info(n_workers, len_iterable, factor=4): """Calculate chunksize numbers.""" chunksize, extra = divmod(len_iterable, n_workers * factor) if extra: chunksize += 1 # `+ (len_iterable % chunksize > 0)` exploits that `True == 1` n_chunks = len_iterable // chunksize + (len_iterable % chunksize > 0) # exploit `0 == False` last_chunk = len_iterable % chunksize or chunksize return Chunkinfo( n_workers, len_iterable, n_chunks, chunksize, last_chunk )
Don’t be confused by the probably unexpected look of
divmod is not used for calculating the chunksize.
def calc_naive_chunksize_info(n_workers, len_iterable): """Calculate naive chunksize numbers.""" chunksize, extra = divmod(len_iterable, n_workers) if chunksize == 0: chunksize = 1 n_chunks = extra last_chunk = chunksize else: n_chunks = len_iterable // chunksize + (len_iterable % chunksize > 0) last_chunk = len_iterable % chunksize or chunksize return Chunkinfo( n_workers, len_iterable, n_chunks, chunksize, last_chunk )
6. Quantifying Algorithm Efficiency
Now, after we have seen how the output of
Pool‘s chunksize-algorithm looks different compared to output from the naive algorithm…
- How to tell if Pool’s approach actually improves something?
- And what exactly could this something be?
As shown in the previous chapter, for longer iterables (a bigger number of taskels), Pool’s chunksize-algorithm approximately divides the iterable into four times more chunks than the naive method. Smaller chunks mean more tasks and more tasks mean more Parallelization Overhead (PO), a cost which must be weighed against the benefit of increased scheduling-flexibility (recall “Risks of Chunksize>1”).
For rather obvious reasons, Pool’s basic chunksize-algorithm cannot weigh scheduling-flexibility against PO for us. IPC-overhead is OS-, hardware- and data-size dependent. The algorithm cannot know on what hardware we run our code, nor does it have a clue how long a taskel will take to finish. It’s a heuristic providing basic functionality for all possible scenarios. This means it cannot be optimized for any scenario in particular. As mentioned before, PO also becomes increasingly less of a concern with increasing computation times per taskel (negative correlation).
When you recall the Parallelization Goals from chapter 2, one bullet-point was:
- high utilization across all cpu-cores
The previously mentioned something, Pool’s chunksize-algorithm can try to improve is the minimization of idling worker-processes, respectively the utilization of cpu-cores.
A repeating question on SO regarding
multiprocessing.Pool is asked by people wondering about unused cores / idling worker-processes in situations where you would expect all worker-processes busy. While this can have many reasons, idling worker-processes towards the end of a computation are an observation we can often make, even with Dense Scenarios (equal computation times per taskel) in cases where the number of workers is not a divisor of the number of chunks (
n_chunks % n_workers > 0).
The question now is:
How can we practically translate our understanding of chunksizes into something which enables us to explain observed worker-utilization, or even compare the efficiency of different algorithms in that regard?
For gaining deeper insights here, we need a form of abstraction of parallel computations which simplifies the overly complex reality down to a manageable degree of complexity, while preserving significance within defined boundaries. Such an abstraction is called a model. An implementation of such a “Parallelization Model” (PM) generates worker-mapped meta-data (timestamps) as real computations would, if the data were to be collected. The model-generated meta-data allows predicting metrics of parallel computations under certain constraints.
One of two sub-models within the here defined PM is the Distribution Model (DM). The DM explains how atomic units of work (taskels) are distributed over parallel workers and time, when no other factors than the respective chunksize-algorithm, the number of workers, the input-iterable (number of taskels) and their computation duration is considered. This means any form of overhead is not included.
For obtaining a complete PM, the DM is extended with an Overhead Model (OM), representing various forms of Parallelization Overhead (PO). Such a model needs to be calibrated for each node individually (hardware-, OS-dependencies). How many forms of overhead are represented in a OM is left open and so multiple OMs with varying degrees of complexity can exist. Which level of accuracy the implemented OM needs is determined by the overall weight of PO for the specific computation. Shorter taskels lead to a higher weight of PO, which in turn requires a more precise OM if we were attempting to predict Parallelization Efficiencies (PE).
6.2 Parallel Schedule (PS)
The Parallel Schedule is a two-dimensional representation of the parallel computation, where the x-axis represents time and the y-axis represents a pool of parallel workers. The number of workers and the total computation time mark the extend of a rectangle, in which smaller rectangles are drawn in. These smaller rectangles represent atomic units of work (taskels).
Below you find the visualization of a PS drawn with data from the DM of Pool’s chunksize-algorithm for the Dense Scenario.
- The x-axis is sectioned into equal units of time, where each unit stands for the computation time a taskel requires.
- The y-axis is divided into the number of worker-processes the pool uses.
- A taskel here is displayed as the smallest cyan-colored rectangle, put into a timeline (a schedule) of an anonymized worker-process.
- A task is one or multiple taskels in a worker-timeline continuously highlighted with the same hue.
- Idling time units are represented through red colored tiles.
- The Parallel Schedule is partitioned into sections. The last section is the tail-section.
The names for the composed parts can be seen in the picture below.
In a complete PM including an OM, the Idling Share is not limited to the tail, but also comprises space between tasks and even between taskels.
The Models introduced above allow quantifying the rate of worker-utilization. We can distinguish:
- Distribution Efficiency (DE) – calculated with help of a DM (or a simplified method for the Dense Scenario).
- Parallelization Efficiency (PE) – either calculated with help of a calibrated PM (prediction) or calculated from meta-data of real computations.
It’s important to note, that calculated efficiencies do not automatically correlate with faster overall computation for a given parallelization problem. Worker-utilization in this context only distinguishes between a worker having a started, yet unfinished taskel and a worker not having such an “open” taskel. That means, possible idling during the time span of a taskel is not registered.
All above mentioned efficiencies are basically obtained by calculating the quotient of the division Busy Share / Parallel Schedule. The difference between DE and PE comes with the Busy Share
occupying a smaller portion of the overall Parallel Schedule for the overhead-extended PM.
This answer will further only discuss a simple method to calculate DE for the Dense Scenario. This is sufficiently adequate to compare different chunksize-algorithms, since…
- … the DM is the part of the PM, which changes with different chunksize-algorithms employed.
- … the Dense Scenario with equal computation durations per taskel depicts a “stable state”, for which these time spans drop out of the equation. Any other scenario would just lead to random results since the ordering of taskels would matter.
6.3.1 Absolute Distribution Efficiency (ADE)
This basic efficiency can be calculated in general by dividing the Busy Share through the whole potential of the Parallel Schedule:
Absolute Distribution Efficiency (ADE) = Busy Share / Parallel Schedule
For the Dense Scenario, the simplified calculation-code looks like this:
# mp_utils.py def calc_ade(n_workers, len_iterable, n_chunks, chunksize, last_chunk): """Calculate Absolute Distribution Efficiency (ADE). `len_iterable` is not used, but contained to keep a consistent signature with `calc_rde`. """ if n_workers == 1: return 1 potential = ( ((n_chunks // n_workers + (n_chunks % n_workers > 1)) * chunksize) + (n_chunks % n_workers == 1) * last_chunk ) * n_workers n_full_chunks = n_chunks - (chunksize > last_chunk) taskels_in_regular_chunks = n_full_chunks * chunksize real = taskels_in_regular_chunks + (chunksize > last_chunk) * last_chunk ade = real / potential return ade
If there is no Idling Share, Busy Share will be equal to Parallel Schedule, hence we get an ADE of 100%. In our simplified model, this is a scenario where all available processes will be busy through the whole time needed for processing all tasks. In other words, the whole job gets effectively parallelized to 100 percent.
But why do I keep referring to PE as absolute PE here?
To comprehend that, we have to consider a possib