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Shared Memory

Shared Memory

The asynchronous scheduler accepts any concurrent.futures.Executor instance. This includes instances of the ThreadPoolExecutor and ProcessPoolExecutor defined in the Python standard library as well as any other subclass from a 3rd party library. Dask also defines its own SynchronousExecutor for that simply runs functions on the main thread (useful for debugging).

Full dask get functions exist in each of dask.threaded.get, dask.multiprocessing.get and dask.get respectively.

Policy

The asynchronous scheduler maintains indexed data structures that show which tasks depend on which data, what data is available, and what data is waiting on what tasks to complete before it can be released, and what tasks are currently running. It can update these in constant time relative to the number of total and available tasks. These indexed structures make the dask async scheduler scalable to very many tasks on a single machine.

Embarrassingly parallel dask flow

To keep the memory footprint small, we choose to keep ready-to-run tasks in a last-in-first-out stack such that the most recently made available tasks get priority. This encourages the completion of chains of related tasks before new chains are started. This can also be queried in constant time.

Performance

tl;dr The threaded scheduler overhead behaves roughly as follows:

  • 200us overhead per task

  • 10us startup time (if you wish to make a new ThreadPoolExecutor each time)

  • Constant scaling with number of tasks

  • Linear scaling with number of dependencies per task

Schedulers introduce overhead. This overhead effectively limits the granularity of our parallelism. Below we measure overhead of the async scheduler with different apply functions (threaded, sync, multiprocessing), and under different kinds of load (embarrassingly parallel, dense communication).

The quickest/simplest test we can do it to use IPython’s timeit magic:

In [1]: import dask.array as da

In [2]: x = da.ones(1000, chunks=(2,)).sum()

In [3]: len(x.dask)
Out[3]: 1168

In [4]: %timeit x.compute()
129 ms +- 5.49 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)

So this takes ~90 microseconds per task. About 100ms of this is from overhead:

In [5]: x = da.ones(1000, chunks=(1000,)).sum()

In [6]: %timeit x.compute()
1.3 ms +- 28.2 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)

There is some overhead from spinning up a ThreadPoolExecutor each time. This may be mediated by using a global or contextual pool:

>>> from concurrent.futures import ThreadPoolExecutor
>>> pool = ThreadPoolExecutor()
>>> dask.config.set(pool=pool)  # set global ThreadPoolExecutor

or

>>> with dask.config.set(pool=pool)  # use ThreadPoolExecutor throughout with block
...     ...

We now measure scaling the number of tasks and scaling the density of the graph:

Adding nodes

Linear scaling with number of tasks

As we increase the number of tasks in a graph, we see that the scheduling overhead grows linearly. The asymptotic cost per task depends on the scheduler. The schedulers that depend on some sort of asynchronous pool have costs of a few milliseconds and the single threaded schedulers have costs of a few microseconds.

Graph depicting how well Dask scales with the number of nodes in the task graph. Graph shows the duration in seconds on the y-axis versus number of edges per task on the x-axis. The time to schedule the entire graph is constant initially, followed by a linear increase after roughly 500 tasks for multiprocessing and threaded schedulers and 10 tasks for async and core schedulers. The inverse is true for the cost per task, with a linear cost decrease, followed by more or less constant cost.

Scheduling overhead for the entire graph (left) vs. per task (right)

Adding edges

Linear scaling with number of edges

As we increase the number of edges per task, the scheduling overhead again increases linearly.

Note: Neither the naive core scheduler nor the multiprocessing scheduler are good at workflows with non-trivial cross-task communication; they have been removed from the plot.

Graph depicting how well Dask scales with the number of edges in the task graph. Graph shows the duration in seconds on the y-axis versus number of edges per task on the x-axis. As the number of edges increases from 0 to 100, the time to schedule the entire graph using the threaded scheduler goes from 2 to 8 seconds whereas using the async scheduler goes from 0 to 3 seconds. The cost per edge decreases up until about 10 edges, after which the cost plateaus for both the threaded and async schedulers, with the async scheduler being consistently faster.

Scheduling overhead of the entire graph (left) vs. per edge (right)

Download scheduling script

Known Limitations

The shared memory scheduler has some notable limitations:

  1. It works on a single machine

  2. The threaded scheduler is limited by the GIL on Python code, so if your operations are pure python functions, you should not expect a multi-core speedup

  3. The multiprocessing scheduler must serialize functions between workers, which can fail

  4. The multiprocessing scheduler must serialize data between workers and the central process, which can be expensive

  5. The multiprocessing scheduler cannot transfer data directly between worker processes; all data routes through the main process.

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Scheduling in Depth