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  2. Simulation Optimization Library: Throughput Maximization

    en.wikipedia.org/wiki/Simulation_Optimization...

    The problem of Throughput Maximization is a family of iterative stochastic optimization algorithms that attempt to find the maximum expected throughput in an n-stage Flow line. According to Pichitlamken et al. (2006), there are two solutions to the discrete service-rate moderate-sized problem.

  3. DeepSpeed - Wikipedia

    en.wikipedia.org/wiki/DeepSpeed

    The library is designed to reduce computing power and memory use and to train large distributed models with better parallelism on existing computer hardware. [2] [3] DeepSpeed is optimized for low latency, high throughput training.

  4. Speedup - Wikipedia

    en.wikipedia.org/wiki/Speedup

    In computer architecture, speedup is a number that measures the relative performance of two systems processing the same problem. More technically, it is the improvement in speed of execution of a task executed on two similar architectures with different resources.

  5. Python (programming language) - Wikipedia

    en.wikipedia.org/wiki/Python_(programming_language)

    Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. [33] Python is dynamically type-checked and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional ...

  6. HTCondor - Wikipedia

    en.wikipedia.org/wiki/HTCondor

    HTCondor is an open-source high-throughput computing software framework for coarse-grained distributed parallelization of computationally intensive tasks. [1] It can be used to manage workload on a dedicated cluster of computers, or to farm out work to idle desktop computers – so-called cycle scavenging.

  7. XGBoost - Wikipedia

    en.wikipedia.org/wiki/XGBoost

    Soon after, the Python and R packages were built, and XGBoost now has package implementations for Java, Scala, Julia, Perl, and other languages. This brought the library to more developers and contributed to its popularity among the Kaggle community, where it has been used for a large number of competitions.

  8. CatBoost - Wikipedia

    en.wikipedia.org/wiki/Catboost

    CatBoost [6] is an open-source software library developed by Yandex.It provides a gradient boosting framework which, among other features, attempts to solve for categorical features using a permutation-driven alternative to the classical algorithm. [7]

  9. Anaconda (Python distribution) - Wikipedia

    en.wikipedia.org/wiki/Anaconda_(Python_distribution)

    Anaconda is an open source [9] [10] data science and artificial intelligence distribution platform for Python and R programming languages.Developed by Anaconda, Inc., [11] an American company [1] founded in 2012, [11] the platform is used to develop and manage data science and AI projects. [9]