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Signed-off-by: Julien Jerphanion <[email protected]>

Author: jjerphan

_posts/sklearn-perf-context.md

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+Title: Performance and scikit-learn (1/4)
+Date: 2021-12-16
+Category: scikit-learn
+Slug: sklearn-perf-context
+Lang: en
+Authors: Julien Jerphanion
+Summary: Context: the current state of scikit-learn performance
+Status: Published
+
+## High-level overview of the scikit-learn dependences
+
+scikit-learn is mainly written in Python and is built on top of
+some core libraries of the scientific Python ecosystem.
+
+This ecosystem allows _high expressiveness_ and
+_interactivity_: one can perform complex operations in a few
+lines of code and get the results straight away.
+
+It also allowed setting up simple conventions which makes the
+code-base algorithms easy to understand and improve
+for new contributors.
+
+It also allows delegating most of the complex operations
+to well-tested third-party libraries. For instance, calls
+to functions implemented in
+[`numpy.linalg`](https://numpy.org/doc/stable/reference/routines.linalg.html),
+[`scipy.linalg`](https://docs.scipy.org/doc/scipy/reference/linalg.html ), and
+[`scipy.sparse.linalg`](https://docs.scipy.org/doc/scipy/reference/sparse.linalg.html)
+are delegated to
+[BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms),
+[LAPACK](https://www.netlib.org/lapack/),
+and [ARPACK](https://www.caam.rice.edu/software/ARPACK/) interfaces.
+
+## Main reasons for limited performance
+
+The PyData stack is simple but is not tailored for optimal performance
+for several reasons.
+
+### CPython internals
+
+CPython -- the main implementation of Python -- is slow.
+
+First, CPython has _an interpreter_: there's a cost in converting Python
+instructions into another intermediate representation --
+the _byte-code_ -- and executing the instructions by interpreting their
+byte-code.
+
+Secondly, nearly every value in CPython is _boxed_ into a `PyObject`
+-- implemented as a C struct. As such, simple operations
+(like adding two floats) come with a non-negligible dispatch
+overhead as the interpreter has to check the type which is unknown
+in advance.
+
+Thirdly, CPython for memory management relies on a global
+mutex on its interpreter called the _Global Interpreter Lock_[ref]For more information about the GIL, see
+[this reference from the Python Wiki](https://wiki.python.org/moin/GlobalInterpreterLock).[/ref].
+This mechanism comes in handy but computations are restricted in
+most cases to sequential execution in a single thread, removing the benefit
+of using threads.
+
+### Memory-hierarchy suboptimal implementations
+
+`numpy` supports high-level operations but this comes with intermediate
+and dynamically-allocated arrays.
+
+Moreover, this pattern is inefficient from a memory perspective:
+during the execution, blocks of data are moved back and forth
+between the RAM and the different CPU caches several times, not
+making optimal use of the caches.
+
+For instance, based on this minimalistic toy example:
+```python
+import numpy as np
+
+A = np.random.rand(100_000_000)
+B = np.random.rand(100_000_000)
+
+X = np.exp(A + B)
+```
+
+The following is performed:
+
+ - a first temporary array is allocated for `A + B`
+ - a second array is allocated to store `np.exp(A + B)` and
+ the first temporary array is discarded
+
+This temporary allocation makes the implementation suboptimal
+as memory allocation on the heap is slow.
+
+Furthermore, high-level operations on `X` come with more data
+moves between the RAM and the CPU than needed to compute the
+elements of `X` and hardly make use of the memory hierarchy
+and the size of the caches.
+
+### No "bare-metal" data-structures
+
+The Python ecosystem comes with a few high-level containers
+such as numpy arrays, and pandas DataFrames.
+
+Contrarily to other languages' standard libraries (like the one of
+C++), no "bare-metal" data structures, including heaps, or
+memory-contiguous resizable buffers (as implemented in C++ by
+[`std::priority_queue`](https://en.cppreference.com/w/cpp/container/priority_queue)
+and [`std::vector`](https://en.cppreference.com/w/cpp/container/vector))
+are available to implement some algorithms efficiently
+from both a computational complexity and a technical perspective.
+
+## Cython: combining the conciseness of Python and the speed of C
+
+In brief, Cython allows transpiling a superset of Python to C code and allows using code that was written in C or C++, which makes bypassing some of CPython's internals possible. Moreover, Cython allows using [OpenMP](https://www.openmp.org/specifications/), an API that allows using lower-level parallelism primitives for implementations written in C or Fortran[ref] For more information on Cython, see [its documentation](https://cython.readthedocs.io/en/latest/).[/ref].
+
+In most cases, features provided by Cython are sufficient enough to reach optimal implementations for many scientific algorithms for which static tasks scheduling -- at the level of C via OpenMP -- is the most natural and optimal one.
+Plus, its syntax makes this language expressive enough to get nearly optimal performance while keeping the instructions short and concise -- which is a real advantage for developers coming from Python who are looking for performance and relief for C or C++ developers[ref]Compared to C or C++, Cython is also less verbose and can be integrated Python build system more easily.[/ref].
+
+As such, many algorithms in `scikit-learn` are implemented in Cython performance, some of which use OpenMP when possible. This is for instance the case of `KMeans` which was initially written in Python using numpy and which was rewritten in Cython by Jérémie du Boisberranger, improving the execution time by a factor of 5 for this algorithm[ref]For more information about `KMeans`, see the original contribution,
+[`scikit-learn#11950`](https://github.com/scikit-learn/scikit-learn/pull/11950), and [this blog
+post](https://scikit-learn.fondation-inria.fr/implementing-a-faster-kmeans-in-scikit-learn-0-23-2/).[/ref].
+
+In the following posts, the case of $k$-nearest neighbors search -- the base routine
+for `KNearestNeighborsClassifier`, `KNearestNeighborsRegressor` and other scikit-learn interfaces -- is covered
+and a new Cython implementation is proposed.
+
+---
+
+## Notes
+

_posts/sklearn-perf-knn.md

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+Title: Performance and scikit-learn (2/4)
+Date: 2021-12-17
+Category: scikit-learn
+Slug: sklearn-perf-knn
+Lang: en
+Authors: Julien Jerphanion
+Summary: Hardware scalability issue: the k-neighbors search example
+Status: Published
+
+## $k$-nearest neighbors search in scikit-learn
+
+$k$-nearest neighbors search is at the base of many implementations used within scikit-learn.
+
+For instance, it is used in Affinity Propagation, BIRCH, Mean Shift, OPTICS,
+Spectral Clustering, TSNE, KNeighbors Regressor, and KNeighbors Classifier.
+
+Whilst many libraries implement approximated versions of $k$-nearest neighbors search to speed-up
+the execution time[ref]Approximate nearest neighbors search algorithms come in many different
+flavors, and there's even [a benchmark suite comparing them!](https://ann-benchmarks.com/).[/ref], scikit-learn's implementation aims at returning the exact $k$-nearest neighbors.
+
+
+## Computing chunks of the distance matrix computations
+
+The general steps for $k$-nearest neighbors search are:
+
+ - Compute $\mathbf{D}_d(\mathbf{X}, \mathbf{Y})$, the distance matrix between the vectors of two
+ arrays $\mathbf{X}$ and $\mathbf{Y}$.
+ - Reduce rows of $\mathbf{D}_d(\mathbf{X}, \mathbf{Y})$ appropriately for the given algorithm:
+ for instance, the adapted reduction for $k$-nn search is to return the $k$ smallest indices of values in an ordered set.
+In what follows, we call this reduction $\texttt{argkmin}$.
+ - Perform extra operations on results of the reductions (here sort values).
+
+Generally, one does not compute $\mathbf{D}_d(\mathbf{X}, \mathbf{Y})$ entirely because its
+space complexity is $\Theta(n_x \times n_y)$. Practically,
+$\mathbf{D}_d(\mathbf{X}, \mathbf{Y})$ does not fit in RAM for sound datasets.
+
+Thus, in practice, one computes chunks of this dataset and reduced them directly.
+This is what was performed as of scikit-learn 1.0[ref][`KNearestNeighbors.kneighbors`](https://github.com/scikit-learn/scikit-learn/blob/c762c407873b8d6417b1c2ff78d19d82550e48d3/sklearn/neighbors/_base.py#L650) is the interface to look for.[/ref].
+
+
+## Current issues
+
+The current implementation relies on a general parallelization scheme using higher-level functions with
+[`joblib.Parallel`](https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html).
+
+Technically, this is not the most efficient: working at this level with views on numpy arrays moves
+large chunks of data back and forth several times between the RAM and the CPUs' caches, hardly
+make use of caches, and allocate temporary results.
+
+Moreover, the cost of manipulating those chunks of data in the CPython interpreter causes a non
+negligible overhead because they are associated with Python objects which are bound to the
+Global Lock Interpreter for counting their references.
+
+Hence, this does not allow getting proper _hardware scalability_: an ideal parallel
+implementation of an algorithm would run close to $n$ times faster when running on
+$n$ threads on $n$ CPU cores compared to sequentially on $1$ thread.
+
+For instance, the current implementation of $k$-nearest neighbors search of scikit-learn
+cannot efficiently leverage all the available CPUs on a machine -- as shown by the figure below.
+
+![Scalability of `kneighbors` as of scikit-learn 1.0](https://user-images.githubusercontent.com/13029839/155144242-6c041729-154b-47aa-9069-3a7d26deef5a.svg)
+
+When using $8$ threads, it only run $\times 2$ faster than the sequential implementation
+and adding more threads and CPUs beyond $8$ does not help to get better performance.
+
+In the next blog, we go over the design of a new implementation to improve the scalability of $k$-nn search.
+
+---
+
+## Notes