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  2. Accelerated Linear Algebra - Wikipedia

    en.wikipedia.org/wiki/Accelerated_Linear_Algebra

    XLA (Accelerated Linear Algebra) is an open-source compiler for machine learning developed by the OpenXLA project. [1] XLA is designed to improve the performance of machine learning models by optimizing the computation graphs at a lower level, making it particularly useful for large-scale computations and high-performance machine learning models.

  3. BERT (language model) - Wikipedia

    en.wikipedia.org/wiki/BERT_(language_model)

    The high performance of the BERT model could also be attributed [citation needed] to the fact that it is bidirectionally trained. This means that BERT, based on the Transformer model architecture, applies its self-attention mechanism to learn information from a text from the left and right side during training, and consequently gains a deep ...

  4. Torch (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Torch_(machine_learning)

    It provides LuaJIT interfaces to deep learning algorithms implemented in C. It was created by the Idiap Research Institute at EPFL. Torch development moved in 2017 to PyTorch, a port of the library to Python. [4] [5] [6]

  5. C11 (C standard revision) - Wikipedia

    en.wikipedia.org/wiki/C11_(C_standard_revision)

    C11 mainly standardizes features already supported by common contemporary compilers, and includes a detailed memory model to better support multiple threads of execution. Due to delayed availability of conforming C99 implementations, C11 makes certain features optional, to make it easier to comply with the core language standard.

  6. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_(deep_learning...

    For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...

  7. Nuitka - Wikipedia

    en.wikipedia.org/wiki/Nuitka

    Nuitka (pronounced as / n juː t k ʌ / [2]) is a source-to-source compiler which compiles Python code to C source code, applying some compile-time optimizations in the process such as constant folding and propagation, built-in call prediction, type inference, and conditional statement execution.

  8. Memory model (programming) - Wikipedia

    en.wikipedia.org/wiki/Memory_model_(programming)

    Changes in the ordering of reads and writes can cause race conditions. Without a memory model, a compiler may not apply such optimizations to multi-threaded programs at all, or it may apply optimizations that are incompatible with multi-threading, leading to bugs. Modern programming languages like Java therefore

  9. Memory barrier - Wikipedia

    en.wikipedia.org/wiki/Memory_barrier

    In computing, a memory barrier, also known as a membar, memory fence or fence instruction, is a type of barrier instruction that causes a central processing unit (CPU) or compiler to enforce an ordering constraint on memory operations issued before and after the barrier instruction. This typically means that operations issued prior to the ...