Library Design#

Note

This page is significantly outdated! It will be updated in 25.04 to reflect the current state of cuDF. Which includes libcudf, pylibcudf, cudf classic, cudf.pandas, and cudf.polars.

At a high level, cuDF is structured in three layers, each of which serves a distinct purpose:

  1. The Frame layer: The user-facing implementation of pandas-like data structures like DataFrame and Series.

  2. The Column layer: The core internal data structures used to bridge the gap to our lower-level implementations.

  3. The Cython layer: The wrappers around the fast C++ libcudf library.

In this document we will review each of these layers, their roles, and the requisite tradeoffs. Finally we tie these pieces together to provide a more holistic view of the project.

The Frame layer#

../../_images/frame_class_diagram.png

This class diagram shows the relationship between the principal components of the Frame layer. The eponymous Frame class is, at its core, an object that holds a mapping of one or more columns. Some types of Frame contain indexes; in particular, any DataFrame or Series has an index. However, as a general container of columnar data, Frame is also the parent class for most types of index.

Frames#

Frame exposes numerous methods common to all pandas data structures. Any methods that have the same API across Series, DataFrame, and Index and return the caller’s type with the same shape should be defined here. Additionally any (internal) methods that could be used to share code between those classes may also be defined here.

The primary internal subclass of Frame is IndexedFrame, a Frame with an index. An IndexedFrame represents the first type of object mentioned above: indexed tables. In particular, IndexedFrame is a parent class for DataFrame and Series. Any pandas methods that are defined for those two classes and returns the caller’s type with the same shape should be defined here.

The second internal subclass of Frame is SingleColumnFrame. As you may surmise, it is a Frame with a single column of data. This class is a parent for Index and Series. While IndexedFrame provides a large amount of functionality, this class is much simpler. It defines shared APIs provided by all 1D pandas objects, and it flattens outputs where needed.

Indexes#

Index and its subclasses largely utilize the implementation of SingleColumnFrame except for the following subclasses:

  • A RangeIndex is backed by a Python range object, not a column. Wherever possible, its methods have special implementations designed to avoid materializing to a column. Where such an implementation is infeasible, we fall back to converting it to an Index of int64 dtype first instead.

  • A MultiIndex can be backed by multiple columns of data. Therefore, MultiIndex overrides methods in SingleColumnFrame that assume 1 column of data to support multiple columns.

The Column layer#

The next layer in the cuDF stack is the Column layer. This layer forms the glue between pandas-like APIs and our underlying data layouts. The principal objects in the Column layer are the ColumnAccessor and the various Column classes. The Column is cuDF’s core data structure that represents a single column of data of a specific data type. A ColumnAccessor is a mapping of column labels to Columns. A Frame owns a ColumnAccessor.

ColumnAccessor#

The primary purpose of the ColumnAccessor is to encapsulate pandas column selection semantics. Columns may be selected or inserted by index or by label, and label-based selections are as flexible as pandas is. For instance, Columns may be selected hierarchically (using tuples) or via wildcards. ColumnAccessors also support the MultiIndex columns that can result from operations like groupbys.

Columns#

Under the hood, cuDF is built around the Apache Arrow Format. This data format is both conducive to high-performance algorithms and suitable for data interchange between libraries. The Column class encapsulates our implementation of this data format. A Column is composed of the following:

  • A data type, specifying the type of each element.

  • A data buffer that may store the data for the column elements. Some column types do not have a data buffer, instead storing data in the children columns.

  • A mask buffer whose bits represent the validity (null or not null) of each element. Nullability is a core concept in the Arrow data model. Columns whose elements are all valid may not have a mask buffer. Mask buffers are padded to 64 bytes.

  • Its children, a tuple of columns used to represent complex types such as structs or lists.

  • A size indicating the number of elements in the column.

  • An integer offset use to represent the first element of column that is the “slice” of another column. The size of the column then gives the extent of the slice rather than the size of the underlying buffer. A column that is not a slice has an offset of 0.

More information about these fields can be found in the documentation of the Apache Arrow Columnar Format, which is what the cuDF Column is based on.

ColumnBase provides some standard methods, while other methods only make sense for data of a specific type. As a result, we have various subclasses of ColumnBase like NumericalColumn, StringColumn, and DatetimeColumn. Most dtype-specific decisions should be handled at the level of a specific Column subclass. Each type of Column only implements methods supported by that data type.

Different types of ColumnBase are also stored differently in memory according to the Arrow format. As one example, a NumericalColumn with 1000 int32 elements and containing nulls is composed of:

  1. A data buffer of size 4000 bytes (sizeof(int32) * 1000)

  2. A mask buffer of size 128 bytes (1000/8 padded to a multiple of 64 bytes)

  3. No children columns

As another example, a StringColumn backing the Series ['do', 'you', 'have', 'any', 'cheese?'] is composed of:

  1. A data buffer of UTF-8 characters ['d', 'o', 'y', 'o', 'u', 'h', ..., '?']

  2. No mask buffer as there are no nulls

  3. A child column of “offsets” indicating the start of each string to the characters column e.g. [0, 2, 5, 9, 12, 19]

Operations that call libcudf routines first convert the ColumnBase to a pylibcudf.Column, invoke the corresponding pylibcudf function, and convert the result back to a ColumnBase object.

Data types#

cuDF uses dtypes to represent different types of data. Since efficient GPU algorithms require preexisting knowledge of data layouts, cuDF does not support the arbitrary object dtype, but instead defines a few custom types for common use-cases:

  • ListDtype: Lists where each element in every list in a Column is of the same type

  • StructDtype: Dicts where a given key always maps to values of the same type

  • CategoricalDtype: Analogous to the pandas categorical dtype except that the categories are stored in device memory

  • DecimalDtype: Fixed-point numbers

  • IntervalDtype: Intervals

Note that there is a many-to-one mapping between data types and Column classes. For instance, all numerical types (floats and ints of different widths) are all managed using NumericalColumn.

Buffer#

Columns are in turn composed of one or more Buffers. A Buffer represents a single, contiguous, device memory allocation owned by another object. A Buffer constructed from a preexisting device memory allocation (such as a CuPy array) will view that memory. Conversely, when constructed from a host object, Buffer uses rmm.DeviceBuffer to allocate new memory. The data is then copied from the host object into the newly allocated device memory. You can read more about device memory allocation with RMM here.

Spilling to host memory#

Setting the environment variable CUDF_SPILL=on enables automatic spilling (and “unspilling”) of buffers from device to host to enable out-of-memory computation, i.e., computing on objects that occupy more memory than is available on the GPU.

Spilling can be enabled in two ways (it is disabled by default):

  • setting the environment variable CUDF_SPILL=on, or

  • setting the spill option in cudf by doing cudf.set_option("spill", True).

Additionally, parameters are:

  • CUDF_SPILL_ON_DEMAND=ON / cudf.set_option("spill_on_demand", True), which registers an RMM out-of-memory error handler that spills buffers in order to free up memory. If spilling is enabled, spill on demand is enabled by default.

  • CUDF_SPILL_DEVICE_LIMIT=<X> / cudf.set_option("spill_device_limit", <X>), which sets a device memory limit of <X> in bytes. This introduces a modest overhead and is disabled by default. Furthermore, this is a soft limit. The memory usage might exceed the limit if too many buffers are unspillable.

Design#

Spilling consists of two components:

  • A new buffer sub-class, SpillableBuffer, that implements moving of its data from host to device memory in-place.

  • A spill manager that tracks all instances of SpillableBuffer and spills them on demand. A global spill manager is used throughout cudf when spilling is enabled, which makes as_buffer() return SpillableBuffer instead of the default Buffer instances.

Accessing Buffer.get_ptr(...), we get the device memory pointer of the buffer. This is unproblematic in the case of Buffer but what happens when accessing SpillableBuffer.get_ptr(...), which might have spilled its device memory. In this case, SpillableBuffer needs to unspill the memory before returning its device memory pointer. Furthermore, while this device memory pointer is being used (or could be used), SpillableBuffer cannot spill its memory back to host memory because doing so would invalidate the device pointer.

To address this, we mark the SpillableBuffer as unspillable, we say that the buffer has been exposed. This can either be permanent if the device pointer is exposed to external projects or temporary while libcudf accesses the device memory.

The SpillableBuffer.get_ptr(...) returns the device pointer of the buffer memory but if called within an acquire_spill_lock decorator/context, the buffer is only marked unspillable while running within the decorator/context.

Statistics#

cuDF supports spilling statistics, which can be very useful for performance profiling and to identify code that renders buffers unspillable.

Three levels of information gathering exist:

  1. disabled (no overhead).

  2. gather statistics of duration and number of bytes spilled (very low overhead).

  3. gather statistics of each time a spillable buffer is exposed permanently (potential high overhead).

Statistics can be enabled in two ways (it is disabled by default):

  • setting the environment variable CUDF_SPILL_STATS=<statistics-level>, or

  • setting the spill_stats option in cudf by doing cudf.set_option("spill_stats", <statistics-level>).

It is possible to access the statistics through the spill manager like:

>>> import cudf
>>> from cudf.core.buffer.spill_manager import get_global_manager
>>> stats = get_global_manager().statistics
>>> print(stats)
    Spill Statistics (level=1):
     Spilling (level >= 1):
      gpu => cpu: 24B in 0.0033

To have each worker in dask print spill statistics, do something like:

    def spill_info():
        from cudf.core.buffer.spill_manager import get_global_manager
        print(get_global_manager().statistics)
    client.submit(spill_info)

Putting It All Together#

To this point, our discussion has assumed that all cuDF functions follow a strictly linear descent through these layers. However, it should be clear that in many cases this approach is not appropriate. Many common Frame operations do not operate on individual columns but on the Frame as a whole. Therefore, we in fact have two distinct common patterns for implementations in cuDF.

  1. The first pattern is for operations that act on columns of a Frame individually. This group includes tasks like reductions and scans (sum/cumsum). These operations are typically implemented by looping over the columns stored in a Frame’s ColumnAccessor.

  2. The second pattern is for operations that involve acting on multiple columns at once. This group includes many core operations like grouping or merging. These operations bypass the Column layer altogether, instead going straight from Frame to pylibcudf.

The pandas API also includes a number of helper objects, such as GroupBy, Rolling, and Resampler. cuDF implements corresponding objects with the same APIs. Internally, these objects typically interact with cuDF objects at the Frame layer via composition. However, for performance reasons they frequently access internal attributes and methods of Frame and its subclasses.

Copy-on-write#

This section describes the internal implementation details of the copy-on-write feature. It is recommended that developers familiarize themselves with the user-facing documentation of this functionality before reading through the internals below.

The core copy-on-write implementation relies on ExposureTrackedBuffer and the tracking features of BufferOwner.

BufferOwner tracks internal and external references to its underlying memory. Internal references are tracked by maintaining weak references to every ExposureTrackedBuffer of the underlying memory. External references are tracked through “exposure” status of the underlying memory. A buffer is considered exposed if the device pointer (integer or void*) has been handed out to a library outside of cudf. In this case, we have no way of knowing if the data are being modified by a third party.

ExposureTrackedBuffer is a subclass of Buffer that represents a slice of the memory underlying an exposure tracked buffer.

When the cudf option "copy_on_write" is True, as_buffer returns a ExposureTrackedBuffer. It is this class that determines whether or not to make a copy when a write operation is performed on a Column (see below). If multiple slices point to the same underlying memory, then a copy must be made whenever a modification is attempted.

Eager copies when exposing to third-party libraries#

If a Column/ExposureTrackedBuffer is exposed to a third-party library via __cuda_array_interface__, we are no longer able to track whether or not modification of the buffer has occurred. Hence whenever someone accesses data through the __cuda_array_interface__, we eagerly trigger the copy by calling .make_single_owner_inplace which ensures a true copy of underlying data is made and that the slice is the sole owner. Any future copy requests must also trigger a true physical copy (since we cannot track the lifetime of the third-party object). To handle this we also mark the Column/ExposureTrackedBuffer as exposed thus indicating that any future shallow-copy requests will trigger a true physical copy rather than a copy-on-write shallow copy.

Obtaining a read-only object#

A read-only object can be quite useful for operations that will not mutate the data. This can be achieved by calling .get_ptr(mode="read"), and using cuda_array_interface_wrapper to wrap a __cuda_array_interface__ object around it. This will not trigger a deep copy even if multiple ExposureTrackedBuffers point to the same ExposureTrackedBufferOwner. This API should only be used when the lifetime of the proxy object is restricted to cudf’s internal code execution. Handing this out to external libraries or user-facing APIs will lead to untracked references and undefined copy-on-write behavior. We currently use this API for device to host copies like in ColumnBase.data_array_view(mode="read") which is used for Column.values_host.

Internal access to raw data pointers#

Since it is unsafe to access the raw pointer associated with a buffer when copy-on-write is enabled, in addition to the readonly proxy object described above, access to the pointer is gated through Buffer.get_ptr. This method accepts a mode argument through which the caller indicates how they will access the data associated with the buffer. If only read-only access is required (mode="read"), this indicates that the caller has no intention of modifying the buffer through this pointer. In this case, any shallow copies are not unlinked. In contrast, if modification is required one may pass mode="write", provoking unlinking of any shallow copies.

Variable width data types#

Weak references are implemented only for fixed-width data types as these are only column types that can be mutated in place. Requests for deep copies of variable width data types always return shallow copies of the Columns, because these types don’t support real in-place mutation of the data. Internally, we mimic in-place mutations using _mimic_inplace, but the resulting data is always a deep copy of the underlying data.

Examples#

When copy-on-write is enabled, taking a shallow copy of a Series or a DataFrame does not eagerly create a copy of the data. Instead, it produces a view that will be lazily copied when a write operation is performed on any of its copies.

Let’s create a series:

>>> import cudf
>>> cudf.set_option("copy_on_write", True)
>>> s1 = cudf.Series([1, 2, 3, 4])

Make a copy of s1:

>>> s2 = s1.copy(deep=False)

Make another copy, but of s2:

>>> s3 = s2.copy(deep=False)

Viewing the data and memory addresses show that they all point to the same device memory:

>>> s1
0    1
1    2
2    3
3    4
dtype: int64
>>> s2
0    1
1    2
2    3
3    4
dtype: int64
>>> s3
0    1
1    2
2    3
3    4
dtype: int64

>>> s1.data._ptr
139796315897856
>>> s2.data._ptr
139796315897856
>>> s3.data._ptr
139796315897856

Now, when we perform a write operation on one of them, say on s2, a new copy is created for s2 on device and then modified:

>>> s2[0:2] = 10
>>> s2
0    10
1    10
2     3
3     4
dtype: int64
>>> s1
0    1
1    2
2    3
3    4
dtype: int64
>>> s3
0    1
1    2
2    3
3    4
dtype: int64

If we inspect the memory address of the data, s1 and s3 still share the same address but s2 has a new one:

>>> s1.data._ptr
139796315897856
>>> s3.data._ptr
139796315897856
>>> s2.data._ptr
139796315899392

Now, performing write operation on s1 will trigger a new copy on device memory as there is a weak reference being shared in s3:

>>> s1[0:2] = 11
>>> s1
0    11
1    11
2     3
3     4
dtype: int64
>>> s2
0    10
1    10
2     3
3     4
dtype: int64
>>> s3
0    1
1    2
2    3
3    4
dtype: int64

If we inspect the memory address of the data, the addresses of s2 and s3 remain unchanged, but s1’s memory address has changed because of a copy operation performed during the writing:

>>> s2.data._ptr
139796315899392
>>> s3.data._ptr
139796315897856
>>> s1.data._ptr
139796315879723

cuDF’s copy-on-write implementation is motivated by the pandas proposals documented here:

  1. Google doc

  2. Github issue