FED: Fast and Efficient Dataset Deduplication Framework with GPU Acceleration

• URL: http://arxiv.org/abs/2501.01046v3
• Date: Wed, 12 Mar 2025 13:36:32 GMT
• Title: FED: Fast and Efficient Dataset Deduplication Framework with GPU Acceleration
• Authors: Youngjun Son, Chaewon Kim, Jaejin Lee,
• Abstract summary: Recently, NVIDIA introduced a GPU-based MinHash LSH deduplication method, but it remains suboptimal.<n>This paper proposes a GPU-accelerated deduplication framework, FED, that optimize MinHash LSH for GPU clusters.<n>In large-scale experiments, the deduplication of 1.2 trillion tokens is completed in just 6 hours in a four-node, 16- GPU environment.
• Score: 4.499466939042501
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dataset deduplication plays a crucial role in enhancing data quality, ultimately improving the training performance and efficiency of large language models. A commonly used method for data deduplication is the MinHash LSH algorithm. Recently, NVIDIA introduced a GPU-based MinHash LSH deduplication method, but it remains suboptimal, leaving room for further improvement in processing efficiency. This paper proposes a GPU-accelerated deduplication framework, FED, that optimizes MinHash LSH for GPU clusters and leverages computationally efficient, partially reusable non-cryptographic hash functions. FED significantly outperforms the CPU-based deduplication tool in SlimPajama (using 64 logical CPU cores) by up to 107.2 times and the GPU-based tool in NVIDIA NeMo Curator by up to 6.3 times when processing 30 million documents on a node with four GPUs. Notably, our method dramatically accelerates the previously time-consuming MinHash signature generation phase, achieving speed-ups of up to 260 compared to the CPU baseline. Despite these gains in efficiency, FED maintains high deduplication quality, with the duplicate document sets reaching a Jaccard similarity of over 0.96 compared to those identified by the standard MinHash algorithm. In large-scale experiments, the deduplication of 1.2 trillion tokens is completed in just 6 hours in a four-node, 16-GPU environment. The related code is publicly available on GitHub (\href{https://github.com/mcrl/FED}{https://github.com/mcrl/FED}).

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