Cellular automata (CA) have become essential for exploring complex phenomena like emergence and self-organization across fields such as neuroscience, artificial life, and theoretical physics. Yet, the lack of a hardware-accelerated cellular automata library has created a barrier to innovation, collaboration, and reproducibility within these areas.
To address this gap, in a new paper CAX: Cellular Automata Accelerated in JAX, an Imperial College London research team introduces CAX (Cellular Automata Accelerated in JAX), a powerful and adaptable open-source library designed to enhance CA research. Built on JAX (Bradbury et al., 2018), a high-performance numerical computing library, CAX enables rapid CA simulations through extensive parallelization on various hardware accelerators, including CPUs, GPUs, and TPUs.
CAX is an open-source library with cutting-edge performance, designed to provide a flexible and efficient framework for cellular automata research. CAX is built on JAX (Bradbury et al., 2018), a high-performance numerical computing library, enabling to speed up cellular automata simulations through massive parallelization across various hardware accelerators such as CPUs, GPUs, and TPUs.
CAX leverages both JAX and Flax (Heek et al., 2024) to exploit the natural alignment between CA systems and recurrent convolutional neural networks. This integration allows the library to benefit from recent advancements in machine learning, streamlining CA research and providing substantial computational efficiencies. CAX offers a modular design and user-friendly interface that supports both discrete and continuous CA models across multiple dimensions, giving researchers the flexibility to navigate various CA types and complexities within a single framework.
With CAX, experiments involving millions of cell updates can be conducted in mere minutes, reducing computation times by up to 2,000 times compared to traditional CA implementations. This exceptional performance enables large-scale CA experiments that were previously too computationally intensive to pursue.
The library includes comprehensive documentation, example notebooks, and seamless compatibility with machine learning workflows, which collectively lower the entry barrier and encourage reproducibility and collaboration. By making CA research more accessible, the developers aim to accelerate progress in the field and attract a broader community of researchers.
The code is available on project’s GitHub. The paper CAX: Cellular Automata Accelerated in JAX is on arXiv.
Author: Hecate He | Editor: Chain Zhang
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