:orphan:

|rss_image|  **2021-03 - 1/1** :ref:`Blog <ap-main-0>` :ref:`article (8) <ap-cat-article-0>` :ref:`articles (3) <ap-cat-articles-0>` :ref:`cours (6) <ap-cat-cours-0>` :ref:`module (7) <ap-cat-module-0>` :ref:`paper (3) <ap-cat-paper-0>`

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    :target: ../_downloads/rss.xml
    :alt: RSS

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.. index:: 2021-03


.. _ap-month-2021-03-0:

2021-03 - 1/1
+++++++++++++

.. blogpostagg::
    :title: Articles, liens, code efficaces
    :date: 2021-03-24
    :keywords: implémentation
    :categories: papers
    :rawfile: 2021/2021-03-24_fast.rst

    * `How to optimize GEMM on CPU
      <https://tvm.apache.org/docs/tutorials/optimize/opt_gemm.html#sphx-glr-tutorials-optimize-opt-gemm-py>`_
    * `HIGH PERFORMANCE CODE GENERATION IN MLIR: AN EARLY CASE STUDY WITH GEMM
      <https://arxiv.org/pdf/2003.00532v1.pdf>`_
    * `Fireiron: A Data-Movement-Aware Scheduling Language for GPUs
      <https://dl.acm.org/doi/pdf/10.1145/3410463.3414632>`_
    * `Linalg Dialect Rationale: The Case For Compiler-Friendly Custom Operations
      <https://mlir.llvm.org/docs/Rationale/RationaleLinalgDialect/>`_
    * `Robocode
      <https://robocode.sourceforge.io/>`_
    * `Introducing PyTorch Profiler - the new and improved performance tool
      <https://pytorch.org/blog/introducing-pytorch-profiler-the-new-and-improved-performance-tool/>`_




.. blogpostagg::
    :title: pandas en plus rapide
    :date: 2021-03-21
    :keywords: py-polars,pandas
    :categories: modules
    :rawfile: 2021/2021-03-21_pypolars.rst

    `py-polars <https://github.com/ritchie46/polars>`_ implémente
    des dataframes, comme :epkg:`pandas`, à ceci près qu'elle
    propose également une interface *lazy* ou streaming.
    Elle est implémentée en :epkg:`Rust`.





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|rss_image|  **2021-03 - 1/1** :ref:`2022-10 (1) <ap-month-2022-10-0>` :ref:`2022-12 (2) <ap-month-2022-12-0>` :ref:`2023-01 (1) <ap-month-2023-01-0>` :ref:`2023-02 (1) <ap-month-2023-02-0>` :ref:`2023-04 (1) <ap-month-2023-04-0>`