This document describes the policies used to select the preference of tasks and to select the preference of workers used by Dask’s distributed scheduler. For more information on how this these policies are enacted efficiently see Scheduling State.
When a task transitions from waiting to a processing state, we decide a suitable worker for that task. If the task has significant data dependencies or if the workers are under heavy load, then this choice of worker can strongly impact global performance. Similarly, the placement of root tasks affects performance of downstream computations, since it can determine how much data will need to be transferred between workers in the future. Different heuristics are used for these different scenarios:
Initial Task Placement¶
We want neighboring root tasks to run on the same worker, since there’s a good chance those neighbors will be combined in a downstream operation:
i j / \ / \ e f g h | | | | a b c d \ \ / / X
In the above case, we want
b to run on the same worker,
d to run on the same worker, reducing future
data transfer. We can also ignore the location of
X, because assuming
we split the
a b c d group across all workers to maximize parallelism,
X will eventually get transferred everywhere.
(Note that wanting to co-locate
a b and
c d would still apply even if
X didn’t exist.)
Calculating these cousin tasks directly by traversing the graph would be expensive.
Instead, we use the task’s TaskGroup, which is the collection of all tasks with the
same key prefix. (
would all belong to the TaskGroup
To identify the root(ish) tasks, we use this heuristic:
The TaskGroup has 2x more tasks than there are threads in the cluster
The TaskGroup has fewer than 5 dependencies across all tasks in the group.
We don’t just say “The task has no dependencies”, because real-world cases like
dask.array.from_arrayproduce graphs like the one above, where the data-creation tasks (
a b c d) all share one dependency (
X)—the Zarr dataset, for example. Though
a b c dare not technically root tasks, we want to treat them as such, hence allowing a small number of trivial dependencies shard by all tasks.
Then, we use the same priority described in Break ties with children and depth to determine which tasks are related. This depth-first-with-child-weights metric can usually be used to properly segment the leaves of a graph into decently well-separated sub-graphs with relatively low inter-sub-graph connectedness.
Iterating through tasks in this priority order, we assign a batch of subsequent tasks to a worker, then select a new worker (the least-busy one) and repeat.
Though this does not provide perfect initial task assignment (a handful of sibling tasks may be split across workers), it does well in most cases, while adding minimal scheduling overhead.
Initial task placement is a forward-looking decision. By colocating related root tasks, we ensure that their downstream tasks are set up for success.
Downstream Task Placement¶
When initial tasks are well-placed, placing subsequent tasks is backwards-looking: where can the task run the soonest, considering both data transfer and worker busyness?
Tasks that don’t meet the root-ish criteria described above are selected as follows:
If the task has no dependencies and no restrictions, then we find the least-occupied worker.
Otherwise, if a task has user-provided restrictions (for example it must run on a machine with a GPU) then we restrict the available pool of workers to just that set, otherwise we consider all workers.
From among this pool of workers, we determine the workers to whom the least amount of data would need to be transferred.
We break ties by choosing the worker that currently has the fewest tasks, counting both those tasks in memory and those tasks processing currently.
This process is easy to change (and indeed this document may be outdated). We
encourage readers to inspect the
decide_worker functions in
Decide which worker should take task ts.
Decide on a worker for task ts.
We often have a choice between running many valid tasks. There are a few competing interests that might motivate our choice:
Run tasks on a first-come-first-served basis for fairness between multiple clients
Run tasks that are part of the critical path in an effort to reduce total running time and minimize straggler workloads
Run tasks that allow us to release many dependencies in an effort to keep the memory footprint small
Run tasks that are related so that large chunks of work can be completely eliminated before running new chunks of work
Accomplishing all of these objectives simultaneously is impossible. Optimizing for any of these objectives perfectly can result in costly overhead. The heuristics with the scheduler do a decent but imperfect job of optimizing for all of these (they all come up in important workloads) quickly.
Last in, first out¶
When a worker finishes a task the immediate dependencies of that task get top priority. This encourages a behavior of finishing ongoing work immediately before starting new work. This often conflicts with the first-come-first-served objective but often results in shorter total runtimes and significantly reduced memory footprints.
Break ties with children and depth¶
Often a task has multiple dependencies and we need to break ties between them with some other objective. Breaking these ties has a surprisingly strong impact on performance and memory footprint.
When a client submits a graph we perform a few linear scans over the graph to determine something like the number of descendants of each node (not quite, because it’s a DAG rather than a tree, but this is a close proxy). This number can be used to break ties and helps us to prioritize nodes with longer critical paths and nodes with many children. The actual algorithms used are somewhat more complex and are described in detail in dask/order.py
The last-in-first-out behavior used by the workers to minimize memory footprint can distort the task order provided by the clients. Tasks submitted recently may run sooner than tasks submitted long ago because they happen to be more convenient given the current data in memory. This behavior can be unfair but improves global runtimes and system efficiency, sometimes quite significantly.
However, workers inevitably run out of tasks that were related to tasks they were just working on and the last-in-first-out policy eventually exhausts itself. In these cases workers often pull tasks from the common task pool. The tasks in this pool are ordered in a first-come-first-served basis and so workers do behave in a fair scheduling manner at a coarse level if not a fine grained one.
Dask’s scheduling policies are short-term-efficient and long-term-fair.
Where these decisions are made¶
The objectives above are mostly followed by small decisions made by the client, scheduler, and workers at various points in the computation.
As we submit a graph from the client to the scheduler we assign a numeric priority to each task of that graph. This priority focuses on computing deeply before broadly, preferring critical paths, preferring nodes with many dependencies, etc.. This is the same logic used by the single-machine scheduler and lives in dask/order.py.
When the graph reaches the scheduler the scheduler changes each of these numeric priorities into a tuple of two numbers, the first of which is an increasing counter, the second of which is the client-generated priority described above. This per-graph counter encourages a first-in-first-out policy between computations. All tasks from a previous call to compute have a higher priority than all tasks from a subsequent call to compute (or submit, persist, map, or any operation that generates futures).
Whenever a task is ready to run the scheduler assigns it to a worker. The scheduler does not wait based on priority.
However when the worker receives these tasks it considers their priorities when determining which tasks to prioritize for communication or for computation. The worker maintains a heap of all ready-to-run tasks ordered by this priority.