标题： 基于向量化序列的块划分用于数据去重 显示英文标题

标题： Vectorized Sequence-Based Chunking for Data Deduplication

摘要： 数据去重作为一种提高存储效率和节约网络带宽的机制广受赞誉，而其最关键的阶段——数据分块，则负责通过去重过程实现的整体空间节省。然而，现代的数据分块算法速度慢且计算密集，因为它们需要扫描大量数据，同时做出基于数据的边界决策。我们提出了SeqCDC，这是一种新颖的分块算法，利用轻量级边界检测、内容定义跳过以及SSE/AVX加速来提升大块大小的分块吞吐量。我们的评估显示，SeqCDC的吞吐量比未加速的算法高出15倍，并且比向量加速的数据分块算法高出1.2到1.35倍，同时对去重的空间节省影响极小。 显示英文摘要

摘要： Data deduplication has gained wide acclaim as a mechanism to improve storage efficiency and conserve network bandwidth. Its most critical phase, data chunking, is responsible for the overall space savings achieved via the deduplication process. However, modern data chunking algorithms are slow and compute-intensive because they scan large amounts of data while simultaneously making data-driven boundary decisions. We present SeqCDC, a novel chunking algorithm that leverages lightweight boundary detection, content-defined skipping, and SSE/AVX acceleration to improve chunking throughput for large chunk sizes. Our evaluation shows that SeqCDC achieves 15x higher throughput than unaccelerated and 1.2x-1.35x higher throughput than vector-accelerated data chunking algorithms while minimally affecting deduplication space savings.