Spilling from device
====================

By default, Dask-CUDA enables spilling from GPU to host memory when a GPU reaches a memory utilization of 80%.
This can be changed to suit the needs of a workload, or disabled altogether, by explicitly setting ``device_memory_limit``.
This parameter accepts an integer or string memory size, or a float representing a percentage of the GPU's total memory:

.. code-block:: python

    from dask_cuda import LocalCUDACluster

    cluster = LocalCUDACluster(device_memory_limit=50000)  # spilling after 50000 bytes
    cluster = LocalCUDACluster(device_memory_limit="5GB")  # spilling after 5 GB
    cluster = LocalCUDACluster(device_memory_limit=0.3)    # spilling after 30% memory utilization

Memory spilling can be disabled by setting ``device_memory_limit`` to 0:

.. code-block:: python

    cluster = LocalCUDACluster(device_memory_limit=0)  # spilling disabled

The same applies for ``dask cuda worker``, and spilling can be controlled by setting ``--device-memory-limit``:

.. code-block::

    $ dask scheduler
    distributed.scheduler - INFO -   Scheduler at:  tcp://127.0.0.1:8786

    $ dask cuda worker --device-memory-limit 50000
    $ dask cuda worker --device-memory-limit 5GB
    $ dask cuda worker --device-memory-limit 0.3
    $ dask cuda worker --device-memory-limit 0


JIT-Unspill
-----------
The regular spilling in Dask and Dask-CUDA has some significate issues. Instead of tracking individual objects, it tracks task outputs.
This means that a task returning a collection of CUDA objects will either spill all of the CUDA objects or none of them.
Other issues includes *object duplication*, *wrong spilling order*, and *non-tracking of sharing device buffers*
(`see discussion <https://github.com/dask/distributed/issues/4568#issuecomment-805049321>`_).

In order to address all of these issues, Dask-CUDA introduces JIT-Unspilling, which can improve performance and memory usage significantly.
For workloads that require significant spilling
(such as large joins on infrastructure with less available memory than data) we have often
seen greater than 50% improvement (i.e., something taking 300 seconds might take only 110 seconds). For workloads that do not,
we would not expect to see much difference.

In order to enable JIT-Unspilling use the ``jit_unspill`` argument:

.. code-block::

    >>> import dask​
    >>> from distributed import Client​
    >>> from dask_cuda import LocalCUDACluster​

    >>> cluster = LocalCUDACluster(n_workers=10, device_memory_limit="1GB", jit_unspill=True)​
    >>> client = Client(cluster)​

    >>> with dask.config.set(jit_unspill=True):​
    ...   cluster = LocalCUDACluster(n_workers=10, device_memory_limit="1GB")​
    ...   client = Client(cluster)


Or set the worker argument ``--enable-jit-unspill​``

.. code-block::

    $ dask scheduler
    distributed.scheduler - INFO - Scheduler at:  tcp://127.0.0.1:8786

    $ dask cuda worker --enable-jit-unspill​

Or environment variable ``DASK_JIT_UNSPILL=True``

.. code-block::

    $ dask scheduler
    distributed.scheduler - INFO -   Scheduler at:  tcp://127.0.0.1:8786

    $ DASK_JIT_UNSPILL=True dask cuda worker​


Limitations
~~~~~~~~~~~

JIT-Unspill wraps CUDA objects, such as ``cudf.Dataframe``, in a ``ProxyObject``.
Objects proxied by an instance of ``ProxyObject`` will be JIT-deserialized when
accessed. The instance behaves as the proxied object and can be accessed/used
just like the proxied object.

ProxyObject has some limitations and doesn't mimic the proxied object perfectly.
Most noticeable, type checking using ``instance()`` works as expected but direct
type checking doesn't:

.. code-block:: python

        >>> import numpy as np
        >>> from dask_cuda.proxy_object import asproxy
        >>> x = np.arange(3)
        >>> isinstance(asproxy(x), type(x))
        True
        >>>  type(asproxy(x)) is type(x)
        False

Thus, if encountering problems remember that it is always possible to use ``unproxy()``
to access the proxied object directly, or set ``DASK_JIT_UNSPILL_COMPATIBILITY_MODE=True``
to enable compatibility mode, which automatically calls ``unproxy()`` on all function inputs.