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This paper explores GPU acceleration techniques for the PC algorithm, a key method in causal inference, to enable its application to high-dimensional datasets. By leveraging the parallel processing capabilities of GPUs and employing algorithm redesign, memory optimization, and precision management, the authors demonstrate significant performance improvements over CPU implementations. The study highlights the impact of newer NVIDIA GPU architectures (A10, A100, H100) in reducing computation times and facilitating real-time causal inference.
GPU-acceleration transforms the PC algorithm from a computationally prohibitive method into a practical tool for real-time causal inference on high-dimensional datasets.
GPU acceleration is revolutionizing causal inference through the PC algorithm, transforming a previously computationally prohibitive task into a practical analytical approach for complex, high-dimensional datasets. The architecture of modern GPUs, with their massively parallel processing capabilities, aligns perfectly with the inherent parallelism of conditional independence tests central to causal discovery. From algorithm redesign to memory optimization and precision considerations, careful implementation strategies can yield performance improvements of several orders of magnitude compared to traditional CPU implementations. The evolution from NVIDIA A10 to A100 and H100 GPUs has progressively reduced computation times and expanded practical dataset sizes, enabling real-time causal inference applications in fields ranging from finance and healthcare to industrial control systems. This technological advancement bridges the gap between theoretical causal modeling and practical deployment, moving AI systems beyond correlation to understand true causal relationships.
