Скачать или смотреть [FPGA 2022] Accelerating Constraint-Based Causal Discovery by Shifting Speed Bottleneck

Ce Guo, Imperial College London
Wayne Luk, Imperial College London

Causal discovery is a technique to find the causal relationship between variables using data. This technique has many applications in data mining and knowledge discovery. However, the high data dimensionality results in a significant computational efficiency problem. A common speed bottleneck in conventional causal discovery methods is the execution of conditional independence (CI) tests. This paper proposes, analyzes, and evaluates a novel acceleration strategy for causal discovery, which has low communication costs and can effectively exploit FPGA on-chip memory and parallelism. First, we propose an algorithmic method to shift the speed bottleneck from CI test execution to CI test generation. Second, we design a hardware accelerator for CI test generation on FPGAs. Third, we evaluate the proposed approach by comparing the accuracy-speed trade-off against four state-of-the-art accelerated causal discovery tools on CPUs and GPUs. Our accelerated implementation running on an Intel Arria 10 GX FPGA shows a superior accuracy-speed trade-off in 12 causal discovery problems. The implementation achieves up to 8.8 times speedup over the cuPC software running on an NVIDIA GeForce RTX 2080 Ti GPU. It also achieves up to 155.7 times speedup over the stable.fast software running on an Intel Xeon Silver 4110 octa-core CPU. To the best of our knowledge, the proposed approach is the first FPGA-based acceleration approach for constraint-based causal discovery.

ACM Digital Library: https://dl.acm.org/doi/10.1145/349042...



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